The micro structure of concrete around embedded

The micro structure of concrete around embedded steel influence on the chloride threshold for chloride induced corrosion

Research Thesis In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

Amit Kenny

Submitted to the Senate of the Technion - Israel Institute of Technology

Tevet 5771

Haifa

January 2012

The Research Thesis Was Done Under The Supervision of Associate Professor Amnon Katz in the Faculty of Civil Engineering. The Generous Financial Help Of GIF Grant I786-94.10, The Technion Israel Institute of Technology, Miss Warshavsky, The Rachel Shalon Found, and The Rachel and Selim Benin Found Are Gratefully Acknowledged.

Contents Contents

v

List of Figures

xi

List of Tables

xv

Abstract

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Nomenclature

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1 Introduction

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1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Corrosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Electrochemistry . . . . . . . . . . . . . . . . . . . . . . 1.2.2 The passive lm . . . . . . . . . . . . . . . . . . . . . . 1.2.2.1 The passive lm in high pH . . . . . . . . . . . 1.2.2.2 The passive lm on steel embedded in concrete 1.2.3 Corrosion mechanisms . . . . . . . . . . . . . . . . . . . 1.2.4 Corrosion of steel in reinforced concrete . . . . . . . . . 1.2.4.1 Methods for corrosion research in concrete . . 1.2.4.2 Eect of concrete composition . . . . . . . . . 1.2.4.3 Eect of steel-concrete interface . . . . . . . . 1.2.4.4 The chloride threshold in reinforced concrete . 1.2.5 Electrochemical methods in corrosion research . . . . . . 1.2.5.1 Half cell potential . . . . . . . . . . . . . . . . 1.2.5.2 Linear polarization . . . . . . . . . . . . . . . . 1.2.5.3 Electrochemical impedance spectroscopy . . . . 1.3 The ITZ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 ITZ by image analysis . . . . . . . . . . . . . . . . . . . 1.3.1.1 Automated image analysis . . . . . . . . . . .

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1.3.2 Mechanical properties of the ITZ . . . . . . . . . . . . . . . . . . . 30

2 Research objective

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3 Research Signicance

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4 Methodology 4.1 Concrete mix design . . . . . . . . 4.2 ITZ characterization . . . . . . . . 4.3 Determination of chloride threshold 4.4 Data analysis . . . . . . . . . . . .

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5 Methods 5.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Mix preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Preparation of Concrete Specimen . . . . . . . . . . . . . . . . . . 5.3.1 Rebar preparation . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Specimen's dimensions . . . . . . . . . . . . . . . . . . . . . 5.4 Fresh concrete properties . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Bleeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Slump . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Hardened concrete properties . . . . . . . . . . . . . . . . . . . . . 5.5.1 Method of measurement . . . . . . . . . . . . . . . . . . . . 5.5.1.1 Macro properties . . . . . . . . . . . . . . . . . . . 5.5.1.2 Micro properties . . . . . . . . . . . . . . . . . . . 5.5.2 Strength of hardened concrete . . . . . . . . . . . . . . . . . 5.5.3 Pull-out Test . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.3.1 Procedure . . . . . . . . . . . . . . . . . . . . . . . 5.5.3.2 Pull out data analysis . . . . . . . . . . . . . . . . 5.5.4 ITZ characterization . . . . . . . . . . . . . . . . . . . . . . 5.5.4.1 ITZ Porosity . . . . . . . . . . . . . . . . . . . . . 5.5.4.2 ITZ thickness . . . . . . . . . . . . . . . . . . . . . 5.5.4.3 Steel-concrete distance . . . . . . . . . . . . . . . . 5.6 Corrosion test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Chloride measurement . . . . . . . . . . . . . . . . . . . . . . . . . 5.7.1 Presentation of the chloride concentration in dierent units vi

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5.8 Simulation of the ITZ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.9 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.10 Image acquisition and analysis . . . . . . . . . . . . . . . . . . . . . . . . 5.10.1 BSE images acquisition . . . . . . . . . . . . . . . . . . . . . . . . 5.10.1.1 Preparation of BSE Specimens . . . . . . . . . . . . . . . 5.10.1.2 BSE Image Acquisition . . . . . . . . . . . . . . . . . . . 5.10.2 Image analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.10.2.1 Low pass lter . . . . . . . . . . . . . . . . . . . . . . . . 5.10.2.2 High pass lter . . . . . . . . . . . . . . . . . . . . . . . . 5.10.2.3 Entropy lter . . . . . . . . . . . . . . . . . . . . . . . . . 5.10.2.4 Selection of lter size . . . . . . . . . . . . . . . . . . . . 5.10.2.5 Application of lters . . . . . . . . . . . . . . . . . . . . . 5.10.2.6 Pixel clustering and classication by MS . . . . . . . . . . 5.10.2.7 Estimation of classication error . . . . . . . . . . . . . . 5.10.2.8 Comparison of the MS method with Gray level thresholding 5.10.2.9 Characterization of the ITZ by image analysis . . . . . . 6 Results 6.1 Relationships between mix composition and fresh mix properties . . . . . 6.2 Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Image analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Error of the MS classication method . . . . . . . . . . . . . . . . 6.3.2 Error of the GLT classication method . . . . . . . . . . . . . . . . 6.3.3 Comparison between the mean-shift method (MS) and the gray level thresholding method (GLT) . . . . . . . . . . . . . . . . . . . 6.3.4 ITZ properties from image analysis . . . . . . . . . . . . . . . . . . 6.3.4.1 ITZ in mature and early age specimens . . . . . . . . . . 6.3.4.2 ITZ properties . . . . . . . . . . . . . . . . . . . . . . . . 6.3.4.3 Relationships between ITZ properties as revealed in image analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 ITZ characterization by pullout test . . . . . . . . . . . . . . . . . . . . . 6.4.1 Pullout correlation with mix composition and properties . . . . . . 6.4.2 Pullout correlation with ITZ microstructure . . . . . . . . . . . . . 6.5 Chloride threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Simulation of the ITZ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.1 Electrochemical analysis . . . . . . . . . . . . . . . . . . . . . . . .

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57 59 60 60 60 61 61 62 62 62 63 63 63 67 67 68 71 71 72 74 74 77

77 83 83 83 87 89 89 92 94 97 97

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6.6.2 Surface analysis . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Corrosion validation . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7.1 EIS measurement of specimens vs. electrochemical potential 6.7.2 Visual examination of specimens . . . . . . . . . . . . . . .

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98 109 109 113

7 Discussion 115 7.1 ITZ vs. mix properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 7.1.1 ITZ around horizontal rebars . . . . . . . . . . . . . . . . . . . . . 115 7.1.1.1 ITZ thickness . . . . . . . . . . . . . . . . . . . . . . . . . 115 7.1.1.2 Steel-concrete distance . . . . . . . . . . . . . . . . . . . . 116 7.1.2 ITZ around vertical rebars . . . . . . . . . . . . . . . . . . . . . . . 117 7.2 Pullout vs. mix & ITZ properties . . . . . . . . . . . . . . . . . . . . . . . 119 7.3 Chloride threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 7.3.1 The presentation of the chloride threshold . . . . . . . . . . . . . . 125 7.3.2 Inuence of steel-concrete distance on the chloride threshold . . . 126 7.3.3 Inuence of ITZ thickness on the chloride threshold . . . . . . . . . 127 7.3.3.1 Curve tting of the chloride threshold-ITZ relationship . 129 7.3.4 Inuence of ITZ on chloride threshold: integration of model and experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 130 7.3.4.1 Micro-structure of ITZ and chloride threshold . . . . . . . 134 7.3.4.2 pH of hydrated cement paste . . . . . . . . . . . . . . . . 136 7.3.4.3 Diculties with the model . . . . . . . . . . . . . . . . . 138 7.3.4.4 Quality of the relationship . . . . . . . . . . . . . . . . . 139 7.3.5 Practical aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 7.4 Open questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 7.4.1 ITZ structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 7.4.2 Cement and concrete chemistry . . . . . . . . . . . . . . . . . . . . 142 8 Conclusions 8.1 ITZ . . . . . . . . . . . 8.1.1 Characterization 8.1.2 Formation . . . . 8.1.3 Simulation . . . . 8.2 Pullout . . . . . . . . . 8.3 Corrosion validation . . 8.4 Chloride Threshold . . .

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145 . 145 . 145 . 145 . 146 . 146 . 146 . 146

Contents

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Appendix, MATLAB scripts for image analysis

A.1 A.2 A.3 A.4

Image classication . . Steel-concrete distance Porosity prole . . . . ITZ thickness . . . . .

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151 154 155 156 159

ix

List of Figures 1.1 1.2 1.3 1.4

Galvele model . . . . . . . . . . . . . . . . . . . . . . . . . . Concentration vs. the product of depth and current density The corrosion process of embedded steel in concrete . . . . Chloride threshold vs. entrapped air at interface . . . . . .

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12 13 14 18

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 5.14 5.15 5.16 5.17 5.18 5.19 5.20 5.21

Aggregate grading. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rebar tip protection . . . . . . . . . . . . . . . . . . . . . . . . . . . Specimens for corrosion . . . . . . . . . . . . . . . . . . . . . . . . . Specimen for pull-out (dimensions in mm) . . . . . . . . . . . . . . . Pullout test setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Typical stress-displacement curve obtained in pullout measurement. . ITZ around horizontal and vertical rebars. . . . . . . . . . . . . . . Porosity versus the distance from the steel surface . . . . . . . . . . ITZ around horizontal and vertical rebars . . . . . . . . . . . . . . . Determination of ITZ thickness using its porosity prole . . . . . . . Visualization of the steel-concrete distance calculation . . . . . . . . Corrosion test layout . . . . . . . . . . . . . . . . . . . . . . . . . . . The physical model . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equivalent circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sets of points, with the correlation coecient. . . . . . . . . . . . . . Specimens for BSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . Filtered BSE image . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensitivity analysis for representing sample size for image analysis . . Clustering and classication owchart . . . . . . . . . . . . . . . . . Example of Clustering and Classication . . . . . . . . . . . . . . . . Inherent errors of using GLT method . . . . . . . . . . . . . . . . . .

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40 43 44 45 47 48 50 51 52 53 54 55 57 58 59 60 64 65 66 66 69

6.1 ITZ around rebar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 6.2 Common phenomena in the ITZ (steel - white zone) . . . . . . . . . . . . 75

xi

List of Figures 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 6.16 6.17 6.18 6.19 6.20 6.21

List of Figures

Estimated error in clustering and classication versus number of clusters . Analysis of the gray level classication histogram . . . . . . . . . . . . . . Zoom into GLT classication . . . . . . . . . . . . . . . . . . . . . . . . . Analysis of the gray level histogram . . . . . . . . . . . . . . . . . . . . . . Original image and its classication by the GLT and MS methods . . . . . Distribution of chloride threshold . . . . . . . . . . . . . . . . . . . . . . . Electrochemical behavior of WE . . . . . . . . . . . . . . . . . . . . . . . Deposits on the steel surface located in front of the cement plug. . . . . . Single crystals of deposits on the steel surface located in front of the cement plug. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Deposits on the carbon surface located in front of the cement plug. . . . . Steel electrode in front of epoxy plug at the end of the experiment . . . . Carbon electrode located in front of the epoxy plug, at end of experiment The cement plug in front of the steel electrode . . . . . . . . . . . . . . . . The cement plug in front of the steel electrode, a zoom in . . . . . . . . . The cement plug in front of the epoxy and carbon electrode . . . . . . . . The cement plug at the frontier the epoxy and steel. . . . . . . . . . . . . Rp versus potential in concrete specimens. (a) Two groups of results. (b) Zoom into group A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equivalent circuits that t the EIS spectra. . . . . . . . . . . . . . . . . . Rebars from the corrosion experiment. (a) Overall corrosion. (b) Localized corrosion. (c) No visable corrosion. (d) Unaerobic corrosion. (e) Unaerobic corrosion. (f) Worm like deposites . . . . . . . . . . . . . . . .

7.1 Changes of ITZ thickness below horizontal rebar . . . . . . . . . . . . . . 7.2 Schematic presentation of the chloride threshold correlations with ITZ and mix variables for both rebar orientations . . . . . . . . . . . . . . . . . 7.3 Schematic presentation of the chloride threshold correlations with ITZ and mix variables for horizontal rebar . . . . . . . . . . . . . . . . . . . . . . 7.4 Schematic presentation of the chloride threshold correlations with ITZ and mix variables for vertical rebar . . . . . . . . . . . . . . . . . . . . . . . 7.5 Chloride threshold versus steel-concrete distance . . . . . . . . . . . . . . 7.6 Relationship between chloride threshold and ITZ thickness . . . . . . . . 7.7 Void at the steel-concrete interface as analogue to pit . . . . . . . . . . . . 7.8 pH vs. x · i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.9 Critical x · i as function of external pH . . . . . . . . . . . . . . . . . . . .

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76 79 80 81 82 95 99 100 101 102 103 104 106 107 108 109 110 112

114 117 121 122 124 124 127 132 132 133

List of Figures

List of Figures

7.10 Solids on steel surface . . . . . . . . . . . . . . . . 7.11 The eect of two buers on critical current density 7.12 Chloride concentration (% of concrete weight) vs. time-to-corrosion . . . . . . . . . . . . . . . . . . .

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8.1 Visual presentation of correlations between mix composition, fresh mix properties, ITZ, and chloride threshold, for horizontal rebars. . . . . . . 148 8.2 Visual presentation of correlations between mix composition, fresh mix properties, ITZ, and chloride threshold, for vertical rebars. . . . . . . . 149

xiii

List of Tables 1.1 1.1 1.1 5.1 5.2 5.3 5.4 5.5 6.1 6.2 6.3 6.4 6.5 6.5 6.6 6.7 6.8 6.9 6.10 6.11 6.12 6.13

chloride threshold reported data . . . . . . . . . . . . . . . . . . . . . . . . chloride threshold reported data . . . . . . . . . . . . . . . . . . . . . . . . chloride threshold reported data . . . . . . . . . . . . . . . . . . . . . . . . Cement composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chemical composition of steel bar (%) . . . . . . . . . . . . . . . . . . . . Mixes composition per 1 m3 of fresh concrete . . . . . . . . . . . . . . . . Compressive strength and properties of the fresh mix . . . . . . . . . . . Composition of simulated pore solution . . . . . . . . . . . . . . . . . . . . Correlation among mix properties . . . . . . . . . . . . . . . . . . . . . . . Correlation matrix of ITZ characteristics for all images . . . . . . . . . . . Correlation matrix of ITZ characteristics for early age . . . . . . . . . . . Correlation matrix of ITZ characteristics for mature specimens . . . . . . ITZ properties by concrete mix . . . . . . . . . . . . . . . . . . . . . . . . ITZ properties by concrete mix . . . . . . . . . . . . . . . . . . . . . . . . Correlation matrix of ITZ characteristics for horizontal . . . . . . . . . . . Correlation matrix of ITZ characteristics for vertical . . . . . . . . . . . . Averaged pullout results per cast orientation . . . . . . . . . . . . . . . . . Correlation matrix between pullout results and some mix composition and properties, for horizontal rebars of age range of 47 to 100 days . . . . . . . Correlation matrix between pullout results and some mix composition and properties, for horizontal rebars of age range of 174 to 246 days . . . . . . Correlation matrix between pullout results and some mix composition and properties, for vertical rebars of age range of 47 to 100 days. . . . . . . . . Correlation matrix between pullout results and some mix composition and properties, for vertical rebars of age range of 174 to 246 days. . . . . . . . Correlation matrix between pullout results and ITZ thickness, for horizontal rebars of age range of 47 to 100 days. . . . . . . . . . . . . . . . . . . .

21 22 23 39 40 41 42 58 73 84 84 85 86 87 88 89 90 91 91 92 92 93 xv

List of Tables

List of Tables

6.14 Correlation matrix between pullout results and ITZ thickness, for horizontal rebars of age range of 174 to 246 days. . . . . . . . . . . . . . . . . . . 6.15 Correlation matrix between pullout results and ITZ thickness, for horizontal rebars of age range of 47 to 100 days. . . . . . . . . . . . . . . . . . . . 6.16 Correlation matrix between pullout results and ITZ thickness, for horizontal rebars of age range of 174 to 246 days. . . . . . . . . . . . . . . . . . . 6.17 Chloride threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.18 Equivalent circuit: Rs Rp /Cdl parameters - epoxy plug . . . . . . . . . . . 6.19 Equivalent circuit: Rs Rp /Cdl parameters - cement pug . . . . . . . . . . . 6.20 Equivalent circuit: Rs Rp /CP Edl parameters - epoxy pug . . . . . . . . . 6.21 Equivalent circuit: Rs Rp /CP Edl parameters - cement pug . . . . . . . . 6.22 Composition of crystal on cement in front of the steel . . . . . . . . . . . . 6.23 Electrochemical properties of concrete specimens . . . . . . . . . . . . . . 7.1 7.2 7.3 7.4 7.5 7.6 7.7

Correlation between the ITZ and the mix properties, horizontal casts . . . Correlation between the ITZ and the mix properties, vertical casts . . . . − Clth vs. mix and ITZ properties, both orientation . . . . . . . . . . . . . . − Clth vs. mix and ITZ properties, horizontal specimens . . . . . . . . . . . − Clth vs. mix and ITZ properties, vertical specimens . . . . . . . . . . . . . Correlation coecient of the chloride threshold EE with some variables . . Parameters and SEE for calculating the chloride threshold using the ITZ characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . − inuencing factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8 Clth 7.9 Critical x · i . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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93 93 94 96 97 97 98 98 105 110 118 120 121 122 123 128 130 131 134

Abstract Corrosion damages is the biggest item on the maintenance bill of reinforced concrete (RC) structures. Chloride induced corrosion is the main cause for corrosion initiation. Elevating the chloride concentration for steel depassivation (chloride threshold) may scientically reduce life cycle cost of RC. A work to nd any relationship between the concrete microstructure, at the interfacial transition zone (ITZ) with embedded steel rebar, and the chloride threshold was performed. Sixteen concrete mixes were produced and cast to molds where rebars were aligned horizontally or vertically. The specimens were used for corrosion test and to characterize the steel-concrete interface microstructure. Corrosion test was performed by uni-dimensional chloride penetration. Chloride penetration was accelerated by wetting and drying cycles. Corrosion initiation was detected by potential shift relative to internal reference electrode. Corrosion was validated by visual inspection of split specimens several months after corrosion initiation detection. The use of potential shift was validated for several specimens by electrochemical impedance. A model of an interface was analyzed as well to validate results. Microstructure of the ITZ was analyzed using backscatter secondary electron microscopy images by automated image analysis program, which was developed for this task. The properties of the ITZ which were analyzed are: ITZ thickness, porosity, and steel-concrete distance. The ITZ thickness ranges from 19 mm to 529 mm, and steel-concrete distance ranges from 2 mm to 83 mm. In addition to the image analysis, mechanical test of the steel-concrete bond was done. The relationship between the steel-concrete distance and the mix properties is unclear. In vertical rebar casts, the steel-concrete distance tends is be lower as the mix become more uid. In horizontal rebar casts, the steel-concrete distance may be inuenced by processes that occur during settlement of the fresh concrete and early hydration. The ITZ thickness around horizontal rebars and its variation depends on mix properties related to bleeding. No clear relationship between the ITZ thickness around vertical rebars and other properties was found. It is not clear from this work what the micro-mechanism of the pullout test is, and how the pullout test results relate to the ITZ's micro-structure,

1

Abstract

Abstract

mix properties, and concrete strength. Chloride thresholds which were found range from 0.56 to 7.98 gr Cl/kg concrete (equivalent to 0.28 % to 5.12 % cement weight). This range overlaps the published range. The expression of chloride threshold as percent of cement weight result in dierent threshold when chloride concentration is constant and cement content is changed. Expression of the chloride threshold as the chloride concentration in pore solution demand measurement of concrete saturation, which introduce additional error to the chloride threshold measurement. Ergo, the most appropriate form for expressing the chloride threshold is by weight of concrete. For both rebar orientations, the chloride threshold was correlated with the steel-concrete distance. The chloride threshold of the horizontal rebars was correlated with the ITZ thickness as well. The chloride threshold of the vertical rebar was correlated with some mix properties which are associated with the steel-concrete distance. Smaller steel-concrete distances are associated with elevated chloride thresholds. A mathematical model of concentration polarization of hydronium, which results from separation between the anodic and cathodic reaction and environment pH, was examined with respect to the results. Considering the pH of concrete components and preferential precipitation at the ITZ, the pH-distance relationships found in the model t the experimental results.

2

Nomenclature A

amper

a

Activity

AC

Alternating current

ANOVA Analysis of variance

aox

Activity of the oxydizing specy

ared

Activity of the reducing specy

BFSC Granulated blast furnace slag cement BSE

back scattered electron imaging

C

a. Capacitor b. Covariance c. Cement content

Cdl

double-layer capacitance

CE

Counter electrode

− Clth

Chloride treshold

CPE

Constant phase element impedance

CP Edl

the double-layer CPE

CSH

calcium silicate hydrate gel

d

diameter

E0

Cell potential at standard conditions

EDS

energy dispersive X-ray spectroscopy

3

Nomenclature

Nomenclature

E

A potential of an electrode

EE

Error of estimation

Eeq

Standard potential at equilibrium

EIS

Electrochemical impedance spectroscopy

FA

Fly ash

Fdebonding F

Force at bond failure

a. Faraday constant b. Force

GLT

Gray level threshold

GNP

Gross national product

h

A parameter for the improved MS algorithm

HPC

High performance concrete

hr

hour

I(i,j)

The number corresponding to pixel i,j in the matrix I

I

a matrix which represent an image

i

electrochemical current density

Iscaled

a scaled matrix which represent an image

ITZ

Interfacial transition zone

K


L


l

a. length b. liter

LOI

Lost of ignition

LP

Linear polarization

min

minute

4

Nomenclature

Nomenclature

m

meter

mm

millimeter

MS

Mean shift clustering algorithm

n

a. number of electron in redox reaction b. an exponent parameter of CPE

Ω

Ohm

ω

water to cement ratio

OPC Ordinary portland cement pKb

The negative of the ten base logaritem of the disassociassion constant of a base

P

Powder content

p

Volume of pores to unit weight of concrete

R(i,j) correlation coecient of property i with property j

RC

Reinforced concrete

ρw

the water specic gravity

RH

Relative humidity

Rp

polarization resistance

R

a. Resistor b. Correlation coecient c. The ideal gas constant

Rs

solution resistance

SCC self consolidating concrete SCE Saturated calomel electrode sec

second

SEE

Standard error of estimation. The standard error of the dierences between measured values and the estimated values based on linear model 5

List of Tables

List of Tables

SEM

Scaning electron microscop

SHE

Standard hydrogen electrode

σ

stress

σb

bond strength (stress at de-bonding)

SRPC Sulphate resistant portland cement T

Absolute temperature

wd

dry concrete weight

WE

Working electrode

WRA Water reducing agent W

a. Warburg element impedance b. Water content

ww

saturated concrete weight

Zconc

concrete impedance

Zpe

polarizable electrode impedance

Z

Impedance element

6

1 Introduction 1.1 Overview

Corrosion damages is the biggest item on the maintenance bill of reinforced concrete (RC) structures. In the USA, direct costs due to corrosion of RC infrastructure is estimated at 0.25 % of the GNP, which corresponds to $ 16.6 billion a year [1]. Chloride-induced corrosion is the main cause for corrosion damages in bridges in the USA [2]. In extreme cases, such as in India, costs of chloride-induced corrosion damage can reach 10 % to 15 % of the original building costs each year [3]. The main sources of chloride are sea water, saline water, and de-icing salt. Hence, most RC corrosion problems are encountered along littoral zones, bridges, and RC highways and roads which tend to freeze in winter. The reinforcing steel in RC is protected from corrosion by a passive lm, as long as a high pH is maintained and the chloride concentration is low. When the chloride content exceeds a certain limit (with respect to total concrete mass, cement content, or other), referred to as − the chloride threshold, Clth , the probability of the steel reinforcement undergoing active corrosion increases signicantly. Although several mechanisms have been suggested for depassivation by chloride, there is no one accepted theory [4]. A review of the chloride threshold in concretes and mortars reveals values that range from 0.12 % to 3.04 % of the cement mass [5, 6]. The chloride threshold value depends on many parameters, which might explain the diculty of nding one specic value for the chloride threshold, as well as the fact that there is no standard method of representing and determining the chloride threshold [7]. As a result, various design standards for RC elements dictate conservative thresholds (0.2 % of cement in ordinary RC). Knowing the inuence of specic factors on the chloride threshold may enable to increase the threshold value without increasing the probability of corrosion. Concrete technology, and especially high performance concretes (HPC), have advanced greatly in recent years, mainly due to better control of rheology and better understanding and ability to control the interfacial transition zone (ITZ). Since a great deal of infrastructure RC has been built using HPC in recent years, and since there is no long-term experience with this material, a need has arisen to study the inuence of ITZ on the chloride threshold. Understanding this inuence will not only

7

CHAPTER 1. INTRODUCTION

1.2. CORROSION

enable a better prediction of RC durability, but will also enable to develop concrete mixes designed for prolonged durability. 1.2 Corrosion

Corrosion is dened as the degradation in material properties by chemical action [8]. For metals, corrosion usually refers to metal oxidation. The process of RC corrosion usually has two stages: the rst stage is the corrosion of the ferric reinforcement, which is followed by a second stage in which concrete members crack and disintegrate. During the corrosion of reinforcement, the reinforcing steel's cross-section is compromised, consequently compromising its ability to carry loads. The corrosion of the reinforcing steel is a continuous and unstoppable process. For durability and maintenance scheduling, the most important parameter is corrosion rate over time; and more specically, the time at which the corrosion rate transitions from the state of negligible, passive corrosion to active corrosion. In order to understand corrosion behavior, it is necessary to be familiar with the electrochemistry, the thermodynamics, and the kinetics of corrosion. These are aected both by the chemo-physical properties of the system, as well as by its microscopic and macroscopic structure. 1.2.1 Electrochemistry

Electrochemical corrosion of metals consists of the transformation, through an oxidation process, of metal atoms into its ions. In the case of carbon steel, the iron is oxidized to bi-valent or three-valent iron. The oxidizing agent in RC is usually oxygen, and the corrosion products are iron oxides and hydroxides. In the 10-12 pH range, which is close to the pH in concrete, the following oxidation reactions are believed to take place on the anode, depending on the anode potential [9]: F e + 2H2 O ⇋ F e(OH)2 + 2H + + 2e− , Eeq = −1.06V

(1.1)

3F e + 4H2 O ⇋ F e3 O4 + 8H + + 8e− , Eeq = −1.04V

(1.2)

3F e(OH)2 ⇋ F e3 O4 + 2H2 O + 2H + + 2e− , Eeq = −0.98V

(1.3)

F e(OH)2 ⇋ γ − F eOOH + 2H2 O + H + + e− , Eeq = −0.71V

(1.4)

8

CHAPTER 1.

INTRODUCTION

1.2.

CORROSION

F e3 O4 + 2H2 O ⇋ 3γ − F eOOH + H + + e− , Eeq = −0.16V

(1.5)

The reactions in equations 1.1 and 1.2 represent the corrosion of solid iron, while the reactions in Equations 1.3 to 1.5 describe further oxidation of the corrosion products. The cathodic reaction always consists of oxygen reduction in a basic environment:

O2 + 2H2 O + 4e− ⇋ 4OH − , Eeq = 0.401V

(1.6)

The potential of an electrochemical cell is calculated using the Nernst Equation:

RT log E = E0 + 2.3 nF ,where:

E0

aox ared

cell potential under standard conditions;

number of electrons passing in the reaction;

F

R

(1.7)

universal gas constant;

Faraday's constant; and

a

n

activity

of the reduced (red) and oxidized (ox) species. In low concentrations, the activities are equal to the concentration whereas in concentrated solutions, the activities depend on the valence of the other species in the solution. The activity of solids and of the solvent (water in most cases) is dened as 1.

The potential in Equation 1.6 is given under

standard conditions (oxygen partial pressure is 1 atm), while in Equations 1.1 to 1.5 the potentials are given in equilibrium at room temperature. Except for the proton, all of the reactants are solids and solvent; hence their activities are 1. Cell potential depends, therefore, only on the pH, temperature, and oxygen concentration. The potential itself does not correspond to the oxidation rate.

Moreover, practical

potential measurements dier from the calculated potential, because of a thin layer often encountered on the metal surface.

This layer is crucial for the actual potential and

corrosion rate, and is often referred as the passivation layer or passive lm .

1.2.2 The passive lm Passivity is the phenomenon of a low or negligible corrosion rate due to a low transformation rate between the metal surface and the environment, which is caused by a thin lm that results from a reaction between the metal and the environment. Passivity can be measured by the potential shift of the metal to noble potential or to a very low corrosion rate (practical passivity) [10]. The ASTM denes passivity as follows:

passivethe state of metal surface characterized by low corrosion rates in a potential region that is strongly oxidizing for the metal

9


1.2. CORROSION

[11]. The formation of a passive lm requires the creation of an insoluble, dense corrosion product lm. A Pourbaix diagram predicts that iron will become passive at pH > 10, due to the insolubility of iron hydroxide [12]. In fact, the passive lm on steel depends strongly on the chemical composition of the environment. This may explain the existence of multiple theories about the composition and structure of the passive lm on iron, which are summarized in [10]. A review of published papers that address the oxide lm on carbon steel structures reveals a connection between passivity and lm thickness. Passivity was found for lm thicknesses of 10 nm or less, whereas activity was presented with lm thicknesses of 30 nm or more [13, 14, 15, 16].

1.2.2.1 The passive lm in high pH Amaral [9], who experimented in environments similar to concrete, assumed that the two-dimensional F e(OH)2 mono-layer constitutes the passive lm while the rest of the thickness has a negligible inuence on passivity. The passivation layer on iron was found to be an n-type semiconductor, which means that current passes through it by means of negative-charge carriers. This layer is several nanometers thick and it is composed of corrosion products in gel and semi-crystallized forms [9]. Chloride penetration into the passivation lm does not cause a measurable change in the lm structure, but seems to reduces the lm's resistance to charge transfer, by acting at the lm/solution interface [17]. Thus, activation energy is reduced and the rate of oxygen reduction increases, leading to a higher corrosion rate. Many researchers in the eld of RC durability assumed that the passive lm formed on reinforcing steel depends on pH only; hence, a saturated Ca(OH)2 solution, whose pH is adjusted using N aOH c, can simulate concrete pore solution in corrosion experiments [18, 19, 20].

1.2.2.2 The passive lm on steel embedded in concrete Practically, although dicult to measure, concrete pore solution contains additional ion species, such as sulphate, aluminate, and silicate [21, 22, 23]. Field evidence implies that silicate concentration plays an important role in the embedded steel's passivity [24]. Laboratory studies, most of which were not conducted with embedded steel corrosion as a research goal, revealed that silicate ions drastically change the nature of the passive lm formed on steel in alkaline solutions. The lm was more resistant and its resistance tended to increase over time [25, 26, 27, 9]. It was also thinner than the lm created in Ca(OH)2 solution [28]. A review of the double layer theory [29] suggests that the

10


1.2. CORROSION

tendency of ions to be directly absorbed by the solid phase can partially explain the importance of solution composition to the formation and destruction of the passive layer. Since ion size and charge are the main parameters that dene the ion's anity for surface adsorption, silicate ions are expected to have a higher anity for surface adsorption than chloride ions (whose anity is somewhat higher than that of the hydroxide ion). Hence, if silicate is present, it will be adsorbed on the solid, at its interface with the solution. When iron is oxidized, it precipitates as ferrous silicate, which is less soluble than ferrous hydroxide, especially at lower pH values. This creates a more stable passive layer on the iron surface. In the presence of chloride, the oxidized iron creates soluble ferrous chloride, which can then diuse out of the internal Helmholtz plane, where it precipitates along with other anions, releasing chloride that becomes available for re-absorption at the solid-liquid interface. Chloride surface concentration is, therefore, a crucial corrosion rate factor of the passive state, and is dependent on the concentration of the other large anion (hydroxide, silicate, carbonate, etc.). 1.2.3 Corrosion mechanisms

The literature mentions at least eight types of corrosion [30]. These corrosion types are related to specic environments and alloys, some of which overlap. Of this variety of corrosion phenomena, only two localized corrosion types are relevant for embedded steel corrosion: crevice corrosion and pitting. ASTM denes crevice and pitting corrosion as follows [31]: Crevice corrosion localized corrosion of a metal surface at, or immediately adjacent to, an area that is shielded from full exposure to the environment because of close proximity between the metal and the surface of another material. Pitting corrosion of metal surface, conned to a point or a small area, that takes the form of cavities. Pitting itself can be regarded as a specic case of crevice corrosion, in which the surface of the pit is in proximity to itself. Localized corrosion, such as pitting and crevice corrosion, is the rst manifestation of corrosion at the transition of a metal from passive (non-corroded) to uniform corroding. Local corrosion is often more dangerous than uniform corrosion, since its rate is very fast and it can cause failure before it is even detected. Localized corrosion forms within a concentration cell, which is a local concentration gradient that creates an electromotive force (EMF) for the corrosion process [31], thus creating a local galvanic cell. Galvele [32]. demonstrated, using a simplied model, how the combination of corrosion and geometry causes concentration polarization, and how

11


1.2. CORROSION

concentration polarization can lead to localized corrosion such as pitting. Galvele's model is a unidirectional diusion of species in a pit, in which corrosion takes place only at the bottom of the pit, and the conditions at the surface (i.e. at the pit interface with the environment) are constant (Figure 1.1). The model shows the dependence between the pH within the pit on the surface pH and the product of multiplying the pit depth with the corrosion rate (at the pit bottom). The pH drops as much as four units (proton concentration rises by four orders of magnitude) for a specic product of depth and current density (Figure 1.2). Other results of the model are concentration polarization of aggressive anions, such as sulphate and chloride, whose concentrations are higher at the pit Figure 1.1: Galvele model [32] bottom. This concentration polarization is reduced in the presence of bivalent ions. The inferred initiation mechanism of localized corrosion is as follows: the passive metal begins corroding at a very slow rate. As the chloride concentration increases, the corrosion rate increases as well, but is still maintained at a low level until the pH within some existing defect drops low enough so as to reduce the local potential and form a concentration cell, in which local corrosion creates concentration polarization which supports active corrosion (stable pitting). It can be deduced that the chloride threshold for pitting corrosion initiation is geometrydependent, assuming Galvele's model and Ahn's passivity breakdown mechanism [17] are valid. Assuming the concrete is a buer and the void between the concrete and the embedded steel is equivalent to the pit depth, then the voids in the steel concrete interface should inuence the chloride threshold. The cement paste denes the pH at the boundary, which ranges from 12.4 to 13.6 depending on the paste's chemistry (before carbonation). The existence of dierent solids and complex ions with dierent pKa impart upon the concrete solids and the pore solution a complex buering eect [33]. This buering capacity depends on the concrete chemistry and its inuence on the chloride threshold is yet to be determined.

12

CHAPTER 1.

INTRODUCTION

1.2.

CORROSION

1.2.4 Corrosion of steel in reinforced concrete The corrosion of RC is divided, for engineering purposes, into two stages - passive and active - whereby de-passivation is the process that separate the two.

The con-

ceptual structure of this system is as follows:

The steel is covered by a thin ox-

ide layer (called the passivation layer, as long as it maintains its passivity). In the aqueous phase, an electrical double layer appears on the oxide layer in which ions are arranged such that they balance the surface electrical charge. The oxide layer is surrounded by an interfacial transition zone (ITZ), which is composed of concrete that diers in structure and composition from the bulk concrete. The ITZ is more porous than the bulk concrete, and it is these pores that contain the aqueous phase (in which the double layer is located). The distance from the steel surface to the bulk concrete can range from several microns

Figure 1.2: Concentration vs. the product of depth and current density [32]

to several hundreds of microns, and bulk concrete surrounds the entire system (Figure 1.3). The overall chloride-induced corrosion process in RC includes the ingress of chloride by diusion and its transport by capillary water movement, de-passivation, and than diusion of oxygen to the oxide layer surface (Figure 1.3). Chloride-induced corrosion can, consequently, be inhibited by reducing the diusion and transport processes in the covercrete or by raising the chloride threshold.

1.2.4.1 Methods for corrosion research in concrete

Many of the works that considered the corrosion of RC determined time to corrosion but ignored the mechanisms that underlie it [34, 35, 36, 37].

The ASTM standard

method for testing admixtures' eect on corrosion deals with time to corrosion and, likewise, disregards its mechanism [38]. Other works separate the process into chloride

13


1.2. CORROSION

Figure 1.3: The corrosion process of embedded steel in concrete threshold and chloride ingress, whereby the latter includes the processes of diusion, transportation, and chloride binding by the cement paste components. Breit and Schieÿl [39], investigated the chloride threshold of steel in mortar by introducing mortar embedded steel into a sodium chloride solution. They used a potentiostate to maintain the steel at a constant potential of 500 mV vs. a standard hydrogen electrode (SHE). Electrical current as well as the change in the open-circuit potential, were monitored, which in case of active corrosion could not be maintained constant using the potentiostate. A rise the current and a drop in potential indicated the de-passivation state. Upon de-passivation, the samples were split open, the bar was removed, and a drill was used to pulverize the 2 mm of mortar adjacent to the bar for chloride content analysis. This method of determining chloride concentration is much more complicated than drilling at steel depth. The use of a potentiostate can be problematic when a large number of specimens are monitored in parallel, due to its cost. Poupard et al. [40] determined the chloride eective diusive coecient and used it to calculate chloride concentration at the embedded steel upon breakdown of passivity. They used the resistance polarization, as measured by electrochemical impedance spectroscopy, to determine steel passivity at time of breakdown. Some studies used simulated pore solution to investigate the chloride threshold [41, 19, 18]. This is a very simple approach to corrosion measurement and to determination of chloride concentration at de-passivation, but it does not represent the complexity of 14


1.2. CORROSION

the RC system. The simulated pore solutions in these works were based on a saturated calcium hydroxide solution to which sodium and/or potassium hydroxide was added to obtain pH levels higher than 12.4. Such simulated pore solutions do not, however, simulate real pore solution since they do not contain minor ingredients such as silicate, which can have a tremendous electrochemical eect on corrosion behavior. These studies also lack the concrete steel interface. This can result in dierent corrosion mechanisms, hence, in inapplicable results. Some important results were, however, derived from these studies, such as the eect of specimen size [19] and surface condition prior to corrosion initiation [41]. Some research on inhibitor eciency was conducted with simulated pore solution [42], extracted pore solution [43], and in concrete [44, 45]. Not all inhibitors that were eective in the pore solutions were found to be eective in real concrete [44]. In other cases, the inhibitor changed the chloride diusivity only and had minor or no eect on the chloride threshold [45]. Whether or not changes in inhibitor eectiveness are due to poor simulation of pore solutions, to the absorption of the inhibitor in the cement paste or to a change in mechanism, these results emphasize the need to study the corrosion phenomenon of RC using concrete specimens. Markeset [46] assessed chloride threshold using a corrosion sensor that was inserted into an existing concrete structure. The chloride content was analyzed at the depth at which corrosion was indicated. The study revealed a wide range of chloride thresholds. The lack of exact information regarding the concrete used in the structure, however, rendered it impossible to draw any conclusions regarding the factors that are related to the threshold.

1.2.4.2 Eect of concrete composition on steel corrosion Numerous investigations have been conducted to determine the eect of pozzoulana on RC corrosion. Pozzoulana is a pulverized siliceous and aluminous material that reacts with the calcium hydroxide released during the Portland cement hydration, to create a C-S-H gel. This gel reduces pore size and pore connectivity, thus reducing the eective diusion coecient in the concrete [47]. The chemical composition of the pore solution may change as well. Most of the investigations claim that the reduced eective diusion rate of chloride and oxygen in concretes containing pozzoulanas is the cause for the high durability of RC elements [48, 49, 36]. These writers ignored other process in chloride-induced corrosion, such as the destruction of the passive layer, measuring only the time-to-corrosion variable. All of the pozzoulanic materials reduce bulk concrete porosity, as well as the porosity 15


1.2. CORROSION

in the steel-concrete interfacial transition zone. Dierent pozzoulanic additives yield dierent results and the use of dierent pozzoulanic additives in the same concrete mix yields a synergistic eect. In other words, a blend of dierent pozzoulanic materials performs better than the sum of each of them added separately [36]. It is not clear whether or not the synergistic eect is a result of grain grading, which creates denser ITZ, or of some other chemical reactions. Kawamura et al. [24] demonstrated that the chemistry of cement pastes containing reactive silica glass is important. Remarkably important is the relationship between the alkalinity of the paste and the amount of reactive silica it contains. In their experiments, Kawamura et al. found that as the quantity of reactive silica increased, higher resistance to corrosion was observed with cement of higher alkalinity, and vice verse. Results also showed that sodium chloride solution can be more corrosive than real sea water. The researchers observed a white deposit on the rebar surface in specimens that contained reactive silica. This study was initiated after a survey of coastal RC structures in which RC structures with cracks, caused by the reaction of reactive silica aggregate with alkalis, demonstrated no corrosion, despite chloride concentration at rebar depth, which was higher than the accepted chloride threshold. It can be inferred that the soluble silica from the reactive aggregates changed the chemistry of the steel surface and reduced its susceptibility to chloride-induced corrosion. This conclusion is supported by electrochemical experiments performed in solution by Amaral and others [27, 25, 9]. They found that resistance to charge transfer during iron oxidation increased over time and with the silicate concentration. When silicate concentration was higher, the iron became more resistant to corrosion. Amaral et al. explained this result as the manifestation of the protection imparted by the covering of the iron and its oxide layer by silicate. They ignored the possibility of a change in the nature of the oxide layer, whereby silicate may reduce chloride absorption on the steel-water interface due to its larger ionic radius, thus increasing resistance to chlorideinduced corrosion. Alkalinity itself raises the chloride threshold [37, 50, 15, 51]. This can be a result of the boundary conditions for concentration polarization, as demonstrated by Galvele [32], of the lower solubility of the iron oxides, or of changes in the passivation layer properties. The buering capacity of the concrete, which can be quantied, relates not only to its alkali and calcium hydroxide contents, but to other hydration products as well. Specically, the greater the buering capacity, the higher is the assumed corrosion resistance of the reinforced concrete [33]. Tricalcium aluminate (C3 A) reacts with chloride to produce an insoluble salt. This 16


1.2. CORROSION

reduces the penetration rate of chloride into concrete, prolonging the time to corrosion initiation. Alonso et al. [5] investigated the eect of tricalcium aluminate content on the chloride threshold in mortars. They found no correlation between tricalcium aluminate content and the chloride threshold, thus supporting the assumption that C3 A aects only the eective chloride diusion, but not the corrosion chemistry itself. Although the water-to-cement ratio is a critical parameter for every concrete property, from physical behavior to durability, there are no publications that relate the chloride threshold to the water-to-cement ratio to be found. The water-cement ratio is, however, known to aect pore solution chemistry by reducing its pH [7]. High water-to-cement ratios are expected to yield low pore solution pH values. In addition, the water-to-cement ratio may eect the formation of the ITZ around the rebar, thus changing the chloride threshold by altering the ITZ's structure. Cement hydration products determine the buering capacity of the concrete. The concrete's cement content is believed to represent the overall ability of the concrete to withstand chloride-induced corrosion. For this reason, the chloride threshold is commonly represented as weight percentage of the cement [7].

1.2.4.3 Eect of voids on steel surface at the steel-concrete interface Several authors emphasized voids on the steel surface as the main durability-related problem of reinforced concrete. Observations of corroding RC structures show that corrosion initiates at these voids [52]. Glass and Reddy [53] found that the chloride threshold rises sharply when the percent of voids drops below 2 % of the interface surface (Figure 1.4). In their work, they intentionally created voids by applying insucient vibration to dry concrete. This voids were, however, macroscopic and do not simulate the true situation in well-consolidated concrete. The importance of voids to the corrosion behavior of embedded steel can be understood through the localized corrosion mechanism, as explained earlier (Section 1.2.3, page 11). Galvele model [32] shows concentration polarization of pH between a metal surface and a buer in aqueous solution. For a specic corrosion rate, as the distance between the metal and the buer increases, the pH at the metal-solution interface decreases, and the metal is at risk for a higher corrosion rate. Since the concrete is a strong buer at high pH values, it can be inferred that the further the steel is from the concrete, it becomes more susceptible to corrosion. Thus, for every single point on the steel surface, the distance to the closest concrete component determines its susceptibility to localized corrosion. The point at which this distance is maximal will be the most susceptible to corrosion. Parameters of minimal distance between the steel and the concrete should, 17

CHAPTER 1.

INTRODUCTION

1.2.

CORROSION

Figure 1.4: Chloride threshold level determined as a function of the percentage of entrapped air voids at the steel interface [53]

18


1.2. CORROSION

therefore, be considered to be an important characteristic of the ITZ, with respect to RC durability. 1.2.4.4 The chloride threshold in reinforced concrete

The chloride threshold is dened as the chloride quantity above which the corrosion hazard to the embedded reinforcement raises drastically. A change in the chloride threshold can have a much larger eect on the lifetime of RC than have the transport properties of the concrete or the covercrete thickness. There are several ways to represent the chloride threshold. Chloride concentration in the pore solution and the chloride-to-hydroxide (Cl− /OH − ) concentration ratio in the pore solution represent the chemists' view point. Chloride content per cubic meter of concrete or as a percentage of cement content are, however, more commonly used as threshold representations. These representation methods are convenient for engineering proposes, but their relation to corrosion mechanism is doubtful. Chloride itself is represented either as free chloride or as total chloride, which also includes chloride that is immobilized in the concrete solids. The conversion from one representation to other is not simple. The porosity tends to decrease and the cement content tends to increase as the concrete's water-to-cement ratio drops. That means that for a constant chloride concentration, the chloride threshold, represented as a percentage of concrete or cement mass is expected to decrease with a decrease in water-to-cement ratio, due to reduced porosity and increased cement content. The same considerations are valid for the use of pozzoulanas, which not only reduce porosity but also reduce the pore pH values as well. In this case, a rise in the Cl− /OH − ratio is expected for the same pore solution chloride concentration. The rationale for using the Cl− /OH − ratio to represent the threshold is the inhibitive property of the hydroxide ion. The underlying idea is that such a threshold will compensate for the drop in pH over the structure's lifetime [54, 55]. The range of dierent Cl− /OH − ratio threshold level results makes the ratio's use, as a representation of the threshold, doubtful. Opponents of this approach claim that the buering capacity of the solid phase is more important than the pore solution pH itself [7, 6]. The fact that the properties of the passive layer do not depend solely on pH, as mentioned earlier, and the dependence of the threshold on other factors (such as the micro structure) may be the reason this representation method is inappropriate as a chloride threshold representation method. Chloride absorption in the cement paste is temperature and pH dependent. This is why, although free chloride can represent the chlorides that participate in the corrosion 19


1.2. CORROSION

process during laboratory experiments in which temperature is maintained constant, it does not represent the real corrosion hazard for a structure exposed to the elements of nature. A discussion on chloride threshold representation can be found in [7] and [6]. The range of chloride threshold values found in the literature is wide, ranging from 0.03 to 4 percent of free chloride from cement mass, to 0.04 to 2.42 percent of total chloride from cement mass, or 0.12 to 3 Cl− /OH − ratio (Table 1.1). The conversion from one representation method to other is not always possible due to missing data in the reported works. The considerable spread of chloride threshold values encountered in the literature may be the result of the high number of variables that may inuence the chloride threshold, as mentioned above. Inconsistent investigation of the variables and published data make it dicult to extract information about the inuencing variables. A separate investigation should, thus, be conducted for every variable.

20

Cl− /OH −

21

1.2. CORROSION

3 1.8-2.9


Table 1.1: chloride threshold reported data Reference Environment source Additional information Threshold total (% of free (% of cement) cement) a [56] concrete both internal Medium Strength 1.15 and external [56] concrete both internal High Strengtha 0.85 and external [56] concrete both internal High Strength + Supplement 0.8 and external [56] concrete both internal High Strength + Supplement 0.45 and external + FA [57] concrete both internal OPC + FA 1-1.5 and external [57] concrete both internal OPC + BFSC 1-1.5 and external [57] concrete both internal OPC 0.5-1.7 and external a [58] concrete external 0.5-1.5 a [59] concrete external [60, 61, 62] concrete external seawatera [48] concrete external OPC 0.7 [48] concrete external OPC + FA 50% 0.2 [48] concrete external OPC + FA 15% 0.65 Cl−

Environment

Threshold

total (% of

free (% of

cement)

cement)

Cl− /OH −

22

[48]

concrete

external

OPC + FA 30%

0.5

[63]

concrete

internal

OPC

0.2-0.4

[64]

concrete

internal

OPC

0.079-0.19

[65]

concrete

internal

OPC, C3 A 7.59%

0.62

0.17

[65]

concrete

internal

OPC, C3 A 14%

1

0.22

[65]

concrete

internal

OPC, C3 A 2.43% + SO3

0.35 0.54

[65]

concrete

internal

OPC, C3 A 7.59% + SO3

0.62 0.8

[65]

concrete

internal

OPC, C3 A 2.43%

0.35

[65]

concrete

internal

OPC, 70°C

0.04-0.19

[65]

concrete

internal

OPC, C3 A 14% + SO3

0.78-1.00

[15]

concrete

internal

OPC + FA 30%

0.68

0.07

0.21

[15]

concrete

internal

OPC + FA 15%

0.9

0.11

0.19

[15]

concrete

internal

BFSC

0.97

0.03

0.23

[15]

concrete

internal

OPC

0.78-0.93

0.11-0.12

0.16-0.26

[15]

concrete

internal

SRPC

0.45

0.1

0.27

[66]

concrete

internal

OPC

0.5-2.0

[67]

concrete

internal

OPC

0.32-1.9

[68]

concrete

internal

OPC bar as received

0.6

[68]

concrete

internal

OPC

3.04

[68]

concrete

internal

BFSC

1.01


Reference

Table 1.1: chloride threshold reported data Additional information

Cl− source

0.14

1.2. CORROSION

Environment

[18]

mortar

[68]

mortar suspension mortar suspension pore solution pore solution

[68] [71] [54] a

external external

Cl− /OH −

OPC, xed potential above -200 mV vs. SCE OPC, various C3 A contents and alkalinities OPC

1-8.34

1-4

0.12-0.44 0.15-0.69 1.7-2.6 1.7-2.6 2.5-6.0 1.7-20

0.7

0.5

1.7±0.3

BFSC

1.21

No additional information regarding cement composition was reported

2.42

0.25-0.8 0.35

1.2. CORROSION

mortar mortar mortar mortar mortar mortar mortar

23

[69] [70] [70] [60, 61, 62] [60, 61, 62] [60, 61, 62] [18]

Threshold free (% of cement)


Reference

Table 1.1: chloride threshold reported data Additional information total (% of cement) external OPC, 100% & 50% RH 0.6-1.8 internal BFSC internal OPC OPC, 80% RH 0.6-1.8 OPC, 100% RH 0.5-1.7

Cl− source


1.2. CORROSION

1.2.5 Electrochemical methods in corrosion research

Several methods are used to investigate corrosion. The more common methods used for RC corrosion investigation are reviewed hereafter. 1.2.5.1 Half cell potential

The potential dierence across a metal-solution boundary is an indicator of the metal surface properties. Since it is impossible to measure this potential dierence, but only the dierence between this potential dierence to the potential dierence of another metalsolution boundary (referred to as the cell potential), half-cell potential, which is the cell potential when one electrode is standard and dened as zero, is used. Half-cell potential, and especially its shift, is widely applied in corrosion research. The half-cell potential of embedded steel decreases when active corrosion occurs. Although reduction is associated with active corrosion, it does not necessarily indicate active corrosion. A potential shift is caused by a change in the steel-solution interface, which can be induced by corrosion or by other processes that change the oxide layer on the steel. The use of half-cell potential was validated as an indicator of corrosion initiation of embedded reinforcing steel in concrete for several steel types and cement types [72]. 1.2.5.2 Linear polarization

Linear polarization (LP) is a method for measuring corrosion current density and resistance polarization. It is based on a potential change (in the order of 20 mV), to above and below the specimen's potential. Due to the substantial potential shift, the LP method is a destructive method that changes the surface properties of the investigated specimen. The current used to maintain the potential shift is measured and recorded and the corrosion current is calculated from the slope of the produced graph according to the Tafel law. 1.2.5.3 Electrochemical impedance spectroscopy

Electrochemical impedance spectroscopy (EIS) is an electrochemical method that generates more information about an investigated electrochemical system than do all other electrical methods. The EIS is believed to be a non-disturbing measurement and the additional information it provides can be used to gain insight on the corrosion mechanism. To achieve a spectrum of a system, impedance data on the AC current is collected at dierent frequencies. The AC perturbing signal is a sinusoidal signal generated around a predetermined electrochemical potential. The signal's amplitude should be small enough 24

CHAPTER 1.

INTRODUCTION

1.2.

CORROSION

to prevent changes in the system, which may result from potential change. The signal is controlled using a potentiostate and a reference electrode. The potentiostate controls the potential shift of the working electrode relative to its rest potential or a predetermined potential. Assuming the system is stable and linear during the measurement, the collected spectrum can be analyzed by parametrization of equivalent circuits. An equivalent circuit is an electrochemical circuit that is similar to an electrical circuit. In addition to the electrical components (resistance, capacitance, and inductance), the electrochemical circuit can contain two electrochemical components: a Warburg element and a constant phase element (CPE), and their derivatives (bounded Warburg element or bounded CPE). The Warburg element corresponds to the impedance caused by diusion of species participating in the electrochemical process. The CPE is an empirical element. An equivalent circuit may represent the processes in the real system and thus can be used to measure its parameters with greater accuracy than is possible by other methods (such as LP, which disregards capacity).

Equivalent circuits for embedded steel in concrete

Several authors suggested that

equivalent circuits represent the concrete and the embedded steel [73]. The equivalent circuits of concrete can be divided into two groups: 1. Nested circuits (Ladder structure), as used in [74, 75], are found by algorithm to best t the data [73].

These equivalent circuits do not necessarily represent

the physics behind the investigated system. Even though the parameters of these circuits can be correlated to the investigated phenomenon, they do not necessarily allude to the underlying mechanism [73]. 2. Most publications refer to physical representative circuits, usually in Voigt structure [76, 77, 78, 44, 40, 79, 80]. These circuits are usually of the form

R1 R2 /Z1 R3 /Z2 ,

where / denotes parallel arrangement, R is resistance, and Z may be a capacitor (C), a Warburg element (W) or a constant phase element (CPE). The most simple representation of the steel (polarizable electrode) is

Rs

- solution resistance,

Rp

- polarization resistance,

Usually, however, the representation

Rs Rp /CP Edl

Cdl

(where

layer impedance) in order to better adjust the data [73, The expected equivalent circuit for RC is trode impedance, and

Zconc

Zpe Zconc ,

Rs Rp /Cdl

(where

- double-layer capacitance).

CP Edl

represent the double-

?].

where

Zpe

is the polarizable elec-

is the concrete impedance. In cases in which the impedance

of one of these components is very low, it will not be recognizable in the EIS spectrum.

25

CHAPTER 1.

INTRODUCTION

1.3.

THE ITZ

1.3 The interfacial transition zone The interfacial transition zone (ITZ) in concrete is created due to deviations in the cement paste properties close to solid boundaries, such as aggregate or rebar, relative to the corresponding properties in the bulk concrete. The ITZ between steel rebar and the concrete around the embedded steel rebar, in reinforced concrete (RC), has an inuence on the mechanical behavior [81] and durability [52, 53, 82] of a RC structure. Most ITZ studies investigated the cement paste-aggregate interface.

These studies found higher

porosity in proximity to the aggregate surface than in the bulk paste.

The extent of

the ITZ around aggregates was quantied and found to be of a magnitude of 10 mm to 20 mm. The main mechanism for porosity formation around aggregates was assumed to be poor compaction of cement grain along the interface due to the wall eect . During the hydration of concrete at a late age, calcium hydroxide and secondary cementitious compounds precipitate in the pores of the ITZ and reduce its porosity. These components are, therefore, expected to be found in higher concentrations in the ITZ compared with the bulk paste [83, 84]. This phenomenon is known as preferential precipitation. The ITZ around steel diers in its properties from the ITZ around aggregates [85, 86, 87]. Some investigators emphasized the dierences between the ITZ formed around vertical and horizontal bars.

Internal bleeding (movement of water upward over solid

particles that move downward in the fresh concrete) was indicated as the mechanism for this phenomenon. While the ITZ formed around vertical rebars tends to be uniform and dense, that formed around horizontal rebars has two distinct zones: an upper zone above the rebar, which is relatively dense, and lower zone, beneath the rebar, which is relatively porous, and often consists of one large pore caused by the accumulation of bleeding water beneath the rebar [52, 85]. These dierences in the ITZ also lead to dierences in the rebar-concrete bond properties [81].

1.3.1 Characterization of the ITZ by image analysis The spatial nature of the ITZ was investigated mainly by analyzing images taken along [53] or across [85, 88] the interface.

Diamond [89] emphasized the need for a large

the variation within a given tier around the periphery of a grain are often as great as, or greater than, the mean ITZ eects as customarily measured . Such observations, by Diamond [89] and others [90, 91] led some researchers to number of measurements, because

emphasize the importance of automated image analysis in quantitative research [90, 91]. Automated image analysis oers the ability to analyze a large number of images in a form that is unbiased by human perception, allowing to obtain reliable information that

26

CHAPTER 1.

INTRODUCTION

1.3.

THE ITZ

can be analyzed statistically. Characterization of the ITZ is a complicated task. It involves data reduction, which means reducing the information contained in the image into several scalars or a short vector, in order to make it useful for statistical analysis and to reveal relationships between the ITZ characteristics and other related phenomenon or properties. The reduction should be into physical or geometrical properties relevant for a specic study, for example, the porosity or the thickness of the ITZ. The most common property investigated is the phase content prole of the cement paste or concrete as a function of its distance from an aggregate or steel surface [91, 92, 89]. The analysis is performed by calculating the distance from the body (aggregate or steel) and dividing the image into strips at specic distance ranges from the said body.

Then, the composition of every strip is analyzed

[89]. The prole itself contains too much data to be useful as is and must be further reduced. Another ITZ property is the pore structure itself. Pore structure can be characterized, for example, by pore size distribution, by the most common pore size in a specic specimen, or by the size of pores adjacent to another solid phase such as steel or aggregate. Pore size distribution is related to the transport properties of the concrete [87]. Pores on the steel surface aect RC durability [53], as discussed in subsection 1.2.4.3 on page 17. In order to quantify the ITZ characteristics, the image of the ITZ is divided rst into phases of interest.

In our case, the phases are steel, concrete solids (cement grains,

hydration products, ller, and aggregates), and pores. In other cases, the concrete solids can be separated to their composite phases: aggregate, cement paste, calcium hydroxide, and an-hydrated cement grains. Some researchers used only gray level image analysis. Glass et al. [86] examined the ITZ of the steel-concrete interface in order to investigate preferential precipitation of calcium hydroxide in the ITZ. The ITZ was investigated using back-scattered electron imaging (BSE); the analysis was done by visual judgment.

No computerized image

analysis was applied, and no additional method was used for validation.

Hence, this

analysis was neither quantitative nor statistically condent.

1.3.1.1 Automated image analysis of cement composites

The gray-scale of a pixel in a BSE image corresponds to the density of the volume interacting with the electron [93]. Every phase in the concrete has its own density and so the interactions of any dierent phase with the electron beam yields a dierent gray-level intensity.

Since the phases in concrete are not pure and have some heterogeneity (for

example, varying chemistry and density of hydrated cement paste) [94], each phase has

27


1.3. THE ITZ

a unique density distribution. As a result, each phase in a BSE image has a unique grayscale distribution. Since there is no information on the in-phase density (or gray-level) distribution, a normal distribution is assumed [95]. The histogram of the gray-level from a BSE image of concrete is, therefore, a combination of Gaussians. These facts create diculties in phase identication based on the gray-level threshold alone [92, 91], even though an optimal gray-level threshold can be found [95]. Koleva et al. [87] investigated the pore structure in concrete and its relationship with the transport properties of the concrete. They did not however investigate the pore structure with respect to the distance from the rebar surface, and therefore ignored the ITZ around the rebar. An automated image-analysis technique was used, and results from 25 images were averaged for every specimen. Pores were separated from other concrete components by applying a threshold to the gray-level. Thus, quantitative and statistical results were obtained, but not of the ITZ. A simple means of adding information to the BSE image is by using energy dispersive X-ray spectroscopy (EDS). For example, Brough and Atkinson [92] used EDS to add more information for image analysis. Specically, they used it to automatically dierentiate/separate aggregates from cement paste using the silicon-to-calcium ratio. Horne et al. [85] investigated the ITZ around 9mm reinforcing steel embedded in concrete, in two concrete mixes and two rebar orientations. They applied a combination of BSE and EDS in order to dene the boundary between aggregate and cement paste phases, but only the histogram of the BSE was used to classify the cement paste constituents. EDS, as a means of adding information to the automated BSE image analysis has, however, several drawbacks: it requires additional equipment and a longer image-acquisition duration, up to 1000 times longer than necessary for BSE (EDS acquisition time of 0.1 sec. per pixel, or more, compared with less than a millisecond for BSE) [93]. This makes EDS inadequate when a large number of images is needed. Its spatial resolution is much lower than that of the BSE. While the BSE interaction volume is located at a depth of up to 0.2 mm, most of the x-ray signal in materials with a specic gravity of 2.7 (typical to aggregate) is generated at a depth of 1.5 mm, and the signal is rendered from a depth of up to 3.7 mm. For cement paste with a density of 2.2 gr/cm3, the analyzed depth is up to 4.5 mm, and for porous parts of the specimen the depth can be much larger [93]. This makes EDS inadequate when spatial resolution of one micron or less is needed. These limitations make the use of EDS inadequate for the acquisition of the large number of images needed for a proper quantitative study of the ITZ. The gray-level of an individual pixel is not explicit enough to classify a BSE concrete image, but no other information other than the gray-levels of the pixels in the image is 28


1.3. THE ITZ

available for the analysis of a large number of images. This problem can be solved by using information that can be obtained using the data embedded in the pixel's neighborhood. Parameters of the pixel's neighborhood can be used for the pixel clustering. These parameters can be the mean gray-level, the standard deviation of the gray-level and so on. Every parameter derived from neighboring pixels adds a new dimension to the problem of pixel clustering. Using this information transforms the problem into a highdimension clustering problem. Several methods for clustering in the multi-dimensional space exist, which can be classied into two types: supervised or unsupervised. In supervised clustering (dened in [8]), a human operator denes which of a group pixels referred to as a "training set" belongs to each class. Then, an algorithm denes the classifying rules by applying some statistical operations on the data. In unsupervised clustering, statistical tools are used to identify the number of clusters in the data set, and how each pixel is assigned to these clusters. These statistical tools are based on statistical assumptions and pre-dened parameters [96]. Supervised clustering requires a-priory knowledge as to the belonging of some pixels, knowledge that is achieved by human visual judgment of exemplary pixels. This method, therefore, is not suitable when a large number of images is involved, or when human judgment is doubtful, as in the present case. A-priory knowledge of the number of clusters may facilitate the clustering process; however, the number of clusters is not always known. The mean shift is an unsupervised clustering technique that clusters the data into its "natural" clusters and does not require prior knowledge of the number of cluster (as opposed to other clustering methods such as k-means) [97]. The mean shift algorithm nds the densest regions in the multidimensional space and uses them to obtain the number of clusters and to estimate the modes of the multivariate distributions. The disadvantage of the mean shift algorithm is its computational complexity, which increases with the number of dimensions, due to the need to calculate the distance of every point from its neighbors. This problem was resolved by Georgescu et al. [98, 99]. Their algorithm tessellates the space into partitions using two parameters: K and L. This approach reduces the number of calculations and consequently the computation time. The modied mean shift algorithm is useful for automatic clustering of reinforced concrete BSE images, where the concrete phase is composed of several unknown sub-phases (dierent aggregate types, paste, and pores, which can have more than one distinct nature, etc.), and when fast computation is needed. 29

CHAPTER 1.

INTRODUCTION

1.3.

THE ITZ

1.3.2 Mechanical properties of the ITZ The bond of smooth reinforcing steel to the concrete is ITZ-dependent, since the bond itself is a phenomena related to steel-concrete interface properties. Even though it seems important to investigate this relationship, publications that investigate both properties are rare.

Most investigation deal with the relationship between the pullout test and

concrete and rebar properties, but not to the ITZ itself (for example [100, 101, 102]). When both pullout of steel rebar and ITZ properties were examined, no relationship among the two was established [81]. The micro-strength and modulus of elasticity of the ITZ can be measured using a microstrength apparatus [81], yielding a direct measurement of the ITZ's physical properties. The limitations of the method are low spatial resolution and changes in properties due to epoxy impregnation of the specimens, as part of the specimens' preparation for the test. The micro-strength and modulus of elasticity of the ITZ were found to be lower than those of the bulk concrete.

Furthermore, values of these parameters were found to be

lower for areas beneath horizontal rebars [81].

30

2 Research objective The research objectives are: 1. Test the hypothesis that the micro-structure of the interfacial transition zone (ITZ) in the concrete around embedded rebar inuences the chloride threshold for corrosion initiation. To do so, we: a) Create tools to quantify the ITZ around embedded rebar. b) Produce concrete mixes with ITZ of varying characteristics around embedded rebar. c) Examine the inuence of dierent concrete mix properties on the ITZ around embedded rebar. d) Measure the chloride threshold for corrosion initiation for the dierent concrete mixes and for rebar with dierent orientation (vertical or horizontal). e) Accept or reject the hypothesis based on statistical tools. 2. Quantify the relationship between the micro-structure of the concrete around embedded rebar and the chloride threshold for corrosion initiation. To do so, we: a) Formulate the ITZ-chloride threshold relationship. b) Evaluate the error in the chloride threshold estimation by the function.

31

3 Research Signicance The hypothesis of relationship between the concrete microstructure around embedded steel rebar and chloride threshold, which was suggested in the literature (Subsection 1.2.4.3), was conrmed, quantied, and compared to results from a model of localized corrosion. The main research contributions can be summarized as following: 1. Experimental conrmation of the hypothesis of relationship between microstructure around embedded steel rebar and chloride threshold. 2. Quantication of the said relationship. 3. Negating relationship of mix properties (cement content and water to cement ratio) with chloride threshold in the context of concretes which were used in this work. 4. Validation of both results and theory by comparison with each other. In the process of producing the main research contributions, some other contribution to the eld of concrete research where produced: 1. A method for concrete BSE image analysis. 2. A method for ITZ characterization.

33

4 Methodology The research program is divided into eight stages: 1. Investigate the parameters aecting the ITZ around embedded rebars. 2. Production of concrete specimens with dierent ITZ characteristics around embedded rebar. 3. Characterization of the ITZ around the embedded rebar. 4. Exposure of the specimens to chloride by diusion until corrosion initiation. 5. Determination of chloride threshold, by measuring the chloride content near the rebar at the onset of corrosion. 6. Investigation of the relationship between the chloride threshold and the ITZ characteristics. 7. Evaluation of the results against models and existing knowledge. 8. Formulation of the ITZ - chloride threshold relationship. 4.1 Concrete mix design

The ITZ around embedded rebar was assumed to have characteristics similar to those of ITZ around aggregates, and to be inuenced by the same mix properties. Based on this assumption, the mixes were designed to exhibit various ITZ properties. In order to make the ITZ the only variable that is considered to inuence the chloride threshold, only the powder, pulverized limestone and cement contents were changed from mix to mix. Pozzoulanas, which may change the pore solution chemistry, were not used. Most of the mixes were designed to be moderately permeable so as to reduce the diusion time and enhance corrosion initiation. Mixes with low permeability (low water/cement ratio) were cast rst, in order to achieve corrosion before the end of the project. One 35

CHAPTER 4. METHODOLOGY

4.2. ITZ CHARACTERIZATION

mix, W52C54, was a quasi-SCC (self-consolidating concrete) and was designed in order to take rheology into consideration. In parallel to the ongoing production of specimens, the ITZ was characterized and the relationship among the mix properties and the ITZ characteristics was investigated. Subsequent mixes were designed based on the ndings. The above procedure was designed to evolve with time since no ITZ characterization method was known at the beginning of the project and just to the end of the project sucient results were gathered to determine which mix variables inuence the ITZ. 4.2 ITZ characterization

Two methods were used to characterize the ITZ: 1. Physical - pullout test. 2. Geometrical - backscattered electron image analysis of the microstructure near the steel. While the pullout test is a straightforward method used to characterize the macrophysical properties of the concrete (see 5.5.3.2 on page 47), the geometrical method is more complicated and had to be developed especially for this study. The methodology of geometrical analysis includes (a) the development of an automatic method that classies each image into one of the following phases: concrete, pore, and steel; and (b) characterization of the geometry of the voids in the steel-concrete interface. The automated image analysis is essential for two reasons: (a) it oers a consistent analysis and eliminates human operator bias; and (b) it enables to process a large number of images necessary to yield a reliable statistical presentation of the ITZ around every rebar. The methodology of image analysis is further explained in Section 5.10.2 on page 61. 4.3 Determination of chloride threshold

The chloride threshold was found by: 1. Exposing concrete specimens to unidirectional diusion of chloride from solution. 2. Identifying the onset of corrosion by monitoring the change in potential relative to a reference rebar embedded in the same specimen, but located away from the chloride front. 36

CHAPTER 4. METHODOLOGY

4.4. DATA ANALYSIS

3. Measuring the chloride concentration at rebar depth at the onset of corrosion (for an in-depth explanation, see Section 5.7 on page 55). 4.4 Investigation of the relationships among variables

Since a large number of variables are involved in the investigation, an in-depth analysis of the relationship between any pair of variables is not feasible. Thus, a `correlation coecient matrix' was used to indicate probable relationships among the variables. Then, the relationship between selected variables was further examined using graphical and/or statistical means.

37

5 Methods 5.1 Materials

The aggregates used in the study were 9.5 mm dolomite (referred to as coarse), graded dolomite sand (Shfar'am) or limeTable 5.1: Cement comstone sand (Modi'in) (referred to as medium), and quartzite position sand (referred to as ne) (Figure 5.1 on the following page). w% The cement was CEM I 52.5 N (EN-197, produced by Nesher). CaO 63.01 Table 5.1presents the chemical analysis of the cement. The SiO2 18.05 ller used was a calcium carbonate powder, AVGIL 510 (proAl2 O3 5.10 duced by Kfar Gil'adi Quarries), with a median particle size F e2 O 3 3.09 of 2.3 mm. Rheobuild 2000 (produced by MBT) was used as a M gO 1.23 Water reducing agent. Rebar for the specimens were 12 mm T iO2 0.33 (nominal) round and smooth steel bars. All specimens were K2 O 0.59 produced using sections of two rebar types with compositions N a2 O 0.23 as presented in Table 5.2. P2 O5 0.44 M n2 O 3 0.05 5.2 Mix preparation SO3 3.07 Cl 0.025 Coarse aggregates were premixed with 70 % of total water IR 0.31 for 1 min. and allowed to absorb water for an additional FL 1.12 5 min in rest. Fine aggregates, cement, powder, the rest of LOI 250 °C 0.44 the water, and admixture were then added and mixed for an LOI 600 °C 0.59 additional 3 min. The mixes composed of tree sries: varied LOI 950 °C 3.75 by the water to cement ratio, and varied water to powders LOI total 4.78 ratio with two dierent water to cement ratio. These pa+ soluble Cr6 ppm 11.6 rameters were assume to inuence the micro-structure, based on experience with aggregates (Section 1.3). Table 5.3 on page 41presents the composition of the dierent mixes and Table 5.4 on page 42 gives the fresh and hardened properties 39

CHAPTER 5. METHODS

5.2. MIX PREPARATION

Figure 5.1: Aggregate grading.

C Si Mn P S Cr Ni Mo Cu V

Bar 1 0.13 0.12 0.74 0.016 0.027 0.063 0.10 0.012 0.35 0.003

Table 5.2: Chemical composition of steel bar (%) Bar 2 0.046 0.19 0.46 0.032 0.030 0.12 0.10 0.017 0.38 0.002

40

CHAPTER 5. METHODS

5.2. MIX PREPARATION

of the mixes.

Mix

Table 5.3: Mixes composition per 1 m3 of fresh concrete Water Cement Aira Coarse Medium Fine Filler WRA

W/C

(kg)

(kg)

(l)

(kg)

(kg)

(kg)

(kg)

(kg)

W40

211

527

20

805

554

249

0

5.3

0.40

W40B2

211

525

23

803

553

249

0

5.3

0.40

W45

207

475

22

816

568

287

0

4.1

0.44

W45C04

211

468

13

805

554

299

19

4.7

0.45

W45C08

221

491

22

798

550

223

39

4.4

0.45

W45C12

224

496

18

814

560

187

60

5.0

0.45

W45C16

213

473

19

813

560

204

76

4.7

0.45

W45C20

212

470

19

821

563

184

94

4.9

0.45

W50

199

428

18

832

564

339

0

2.1

0.47

W52C08

218

419

5

816

562

300

34

4.2

0.52

W52C12

214

411

15

816

562

279

50

4.1

0.52

W52C17

205

393

0

821

565

325

68

5.9

0.52

W52C54

179

345

9

840

324

496

208

6.2

0.52

W55

210

381

13

800

551

400

0

1.9

0.55

W60

221

367

9

825

568

355

0

0.0

0.60

W65

235

362

6

823

567

335

0

0.0

0.65

a

Calculated 41

CHAPTER 5. METHODS

Table 5.4: Compressive strength and properties of the fresh mix Compressive strength (MPa) Slump Bleeding 3 days 28 days 90 days (mm) Duration Total Rate 3 (hr:min) (L/m ) (L/m3 /hr)

Mix

W40 W40B2 W45 W45C04 W45C08 W45C12 W45C16 W45C20 W50 W52C08 W52C12 W52C17 W52C54 W55 W60 W65 aA

5.3. PREPARATION OF CONCRETE SPECIMEN

42.6 40.7 40.8 40.5 39.2 37.8 38.9 40.1 36.0 38.5 35.2 35.0 39.9 29.4 25.5 21.4

55.7 52.7 58.7 54.0 52.8 50.0 50.9 57.6 47.6 50.5 48.3 52.1 59.4 47.9 40.9 40.1

64.8 58.8 66.3 57.3 60.7 54.7 60.3 62.9 50.8 55.2 53.9 52.3 66.0 51.2 45.5 41.0

100 126 142 95 96 110 75 110 80 132 185 80 300a 122 144 170

5:10 4:30 5:50 4:30 3:50 4:40 4:55 5:20 4:00 5:20 5:00 4:10 6:55 4:55 5:45 5:10

3.97 0.16 5.24 3.30 3.37 4.98 4.03 4.48 5.52 8.10 9.72 4.66 1.15 9.32 16.32 26.69

0.43 0.04 0.46 0.35 0.45 0.55 0.48 0.42 0.84 2.16 2.70 1.43 0.30 0.89 1.35 6.41

quasi SCC mix. U box time 5 sec, L box time 3 sec, slump ow 840 mm. For statistical calculation was considered as slump of 300 mm

5.3 Preparation of Concrete Specimen 5.3.1 Rebar preparation

Rebars for pullout specimens were 700 mm long, and those for corrosion and BSE samples were 190 mm long. The preparation procedure ensured minimum variability of rebar surface, while preserving the nature of the rebars as being used in reinforced concrete. Rebars were prepared for casting as follows: Immersion in H3 P O4 10 % by volume, for two hours. Washing with hot potable water and brushing while being rinsed.

42

CHAPTER 5. METHODS


Drying with hot air jet. Immersion in saturated Ca(OH)2 for no less than one day. No earlier than one day before casting, the rebars were removed from the Ca(OH)2

solution, washed with hot water, rinsed, and dried. After casting and curing, the rebar was wired and protected as follows: The tips of the rebars were exposed up to a depth of two centimeters below concrete

surface by cup drilling, and were cleaned to remove concrete residue and dust. One side of each rebar was wired to isolated copper wire by thin soldering, to enable

electrical measurements. The tin (Sn) surface and any other exposed wire surface were covered by an epoxy

coating to prevent measuring their potential and galvanic corrosion. After the epoxy cured, the rebar tip was covered by a PVC tube. The void between

the rebar and the tube was grouted using Sica Monotop anti-corrosive grout. The spaces around the tips were lled using the same compound. The grout was cured at 100 % RH for one week. The exposed surface of the grout was coated using silicon RTV to prevent penetra-

tion of chlorides. Figure 5.2 presents a schematic description of rebar tip protection.

Figure 5.2: Rebar tip protection

43

CHAPTER 5. METHODS


5.3.2 Specimen's dimensions

Dimensions of the specimens for corrosion measurement were: 150 mm ×150 mm ×230 mm; net distance between the rebars was 65 mm (Figure 5.3).

(a)

(b)

(c)

Figure 5.3: Dimensions of specimens for corrosion measurements. a. Vertical, b. horizontal, c. a specimen after one edge was sectioned to allow 10 mm between the face and the working electrode (WE). One 15 cm×15 cm face of every specimen designated for corrosion measurements was sectioned to obtain 10±2 mm concrete cover over the reinforcing bar used as the working electrode (WE), the side surfaces of the concrete close to the sectioned face was covered with silicon RTV (Figure 5.3c). The specimen was then placed over a salt solution (6 %) with the sectioned face immersed in the solution to a depth of 2mm. This procedure 44

CHAPTER 5. METHODS

5.4. FRESH CONCRETE PROPERTIES

ensures unidirectional ow and diusion from the solution into the specimen. Specimens for pullout tests were 150 mm×150 mm×150 mm cubes with perpendicular rebar that was centered with respect to two faces. The rebar was unbonded from the concrete along the rst 60mm of penetration, followed a bonded zone of 60 mm long, and an additional 30 mm of unbonded zone (Figure 5.4). The unbonded zones were achieved by covering the desired parts of the rebar with exible PVC tubes during casting. After curing, the tubes were removed. Figure 5.4: Specimen for pull-out (dimensions in mm) This setup follows the procedure described in [103] 5.4 Fresh concrete properties 5.4.1 Bleeding

During specimens preparation, a sample of concrete was cast and consolidated in a 3 liter container. the bleeding waters were collected and measured every 10 minutes, until the collected quantity decrease to less than half the initial quantity. The procedure for bleeding measurement follows the IS 26, part 2.08. 5.4.2 Slump

After mixing, before casting, the concrete mix slump was measured. The procedure for Slump measurement follows the IS 26, part 2.01. 5.5 Hardened concrete properties 5.5.1 Method of measurement 5.5.1.1 Macro properties

Macro properties are properties which are measured for a specimen as a whole. An example for a macro properties is the results of pull-out test, compressive strength, and the mix properties. 45

CHAPTER 5. METHODS

5.5. HARDENED CONCRETE PROPERTIES

5.5.1.2 Micro properties

Micro properties are properties measured across a cross-section of the steel-concrete interface. Such measurement is performed for many discrete spots and treat every distinct spot of as a single measurement, thus render detailed information of the ITZ variation. When such method is bi-dimensional, its results are represented as an image. Most common technique for micro properties measurement is Back Scatter Electron microscopy Imaging, which also referred as BSE or BEI (see Subsection 1.3.1 on page 26). The acquired image pixels are classied into their corresponding phases in order to retrieve the micro characters of the ITZ, as described hereafter. 5.5.2 Strength of hardened concrete

Compressive strength of hardened concrete was measured on 15 cm×15 cm×15 cm cubes. Cubes were cured for 28 days in 100 % RH and 20 ºC conditions. After curing the cubes were held in 60 % RH and 20 ºC conditions. The compressive strength was measured in 3, 28, and 90 days after cast. 5.5.3 Pull-out Test

The mechanical properties of the bond between the concrete and rebar is an outcome of the ITZ shear strength capacity which in turn, depends on the ITZ's micro-structure and on the strength of the cement paste (which is not necessarily equal to the concrete strength). Due to technical and scheduling problems, the measurements were taken at dierent specimens' age. The results was analyzed separately for measurements of specimens at age range of 47 to 100 days, and for specimens at age range of 174 to 246 days. 5.5.3.1 Procedure

Figure5.5 describes the pullout test setup. Two gauges measured rebar elongation, two gauges measured pullout slip at the loaded end relative to the concrete surface, and one gauge measured the slip of the rebar at the unloaded end. The load was recorded using a load cell. Displacement of the crosshead was held constant at a rate of 0.005 mm/sec.

46

CHAPTER 5. METHODS


Figure 5.5: Pullout test setup 5.5.3.2 Pull out data analysis

Rebar slip at the loaded end was calculated based on the direct measurement of the slip of the rebar relative to concrete with compensation for elongation along the rebar between the mounting point of the gauges on the rebar and the entry point of the rebar into the concrete. The stress-displacement data was analyzed to extract the following parameters: - The maximum pullout load divided by the surface area in contact with the concrete.

Bond strength

Slope

- The slope of the stress-displacement at the linear region of the pre-peak loading.

- The load at debonding divided by the surface area in contact with the concrete. Debonding is indicated by the initial movement of the upper part of the rebar (unloaded edge), as measured by the upper gauge.

Adhesion strength

- The area under the stress-displacement curve extending from zero displacement to debonding.

Adhesion energy

Figure 5.6 presents a visualization of these parameters. Since pullout results are scattered, ve repetitions were performed for every mix and each rebar orientation and average results of these repetitions are reported in this paper.

47

CHAPTER 5.

METHODS

5.5.

HARDENED CONCRETE PROPERTIES

Figure 5.6: Typical stress-displacement curve obtained in pullout measurement.

48

CHAPTER 5. METHODS


5.5.4 ITZ characterization

The interfacial transition zone is characterized as a zone, close to an inclusion (e.g. aggregate, reinforcing bar), in which its properties deviates from average properties of the concrete at the bulk, away from that inclusion. The maximum or minimum of a certain concrete property, which deviates in the ITZ from its bulk average, can be used to characterize the ITZ. Thus, ITZ characteristics are the parameters of this deviation. The methods described hereafter were performed using classied images. The method for obtaining the classied images is detailed in Section 5.10.

5.5.4.1 ITZ Porosity

Porosity is the most commonly known property that exhibits such behavior as described above, and is therefore best suited and most commonly used for ITZ characterization [89, 84, 91, 104, 105]. The script for obtaining a porosity prole from classied image is presented in Appendix A.3. The porosity proles observed in this work were usually identied as one of the following three types: (a) Porosity that decreases steadily from the steel surface to the average porosity of the bulk. This prole is common around vertical rebars and above horizontal rebars (Figure 5.7 c and d); (b) Porosity that increases to a value lower than 90 % (possible even as low as 1 %) at a distance of several microns from the steel surface, followed by a sharp drop in porosity to the average value. This prole is the characteristic of the ITZ around vertical rebars, which contains a layer of discrete voids at a specic distance from the steel surface (Figure 5.8a shows such porosity which increases up to 0.017 at a distance of about 10 mm from the rebar surface and drop to almost 0 at a distance about 40 mm from the rebar surface. A large distant pore appears at a distance of about 150 mm from the rebar surface, in the same prole); and (c) Porosity that may be lower than 100 % in the rst several microns from the steel surface, then is stable at a value of about 100 % for the next several dozen or hundreds of microns before dropping to the average bulk porosity (Figure 5.7 a and b). Porosity of 100 % indicate a gap between the rebar and the bulk concrete, which is as wide as the image taken (0.7 to 1.2 mm). This prole characterizes the area below horizontal rebars and results from the presence of a single large void formed below horizontal rebars due to concrete segregation. Hence, porosity higher than 90 % exhibited in images of ITZ around rebar is an indicative of horizontal rebar, and specically indicates that the image is of the lower part of the rebar.

49

CHAPTER 5.

METHODS

5.5.


(a) Porosity prole below horizontal rebar.

(b) BSE image of ITZ below horizontal rebar.

(c) Porosity prole around vertical rebar

(d) BSE image of ITZ around vertical rebar.

Figure 5.7: ITZ around horizontal and vertical rebars.

50

CHAPTER 5.

METHODS

5.5.


5.5.4.2 ITZ thickness

ITZ thickness is dened as the distance from the steel surface to the point where the concrete properties are at their average values.

There are two technical diculties in

nding this point: (i) variability of the concrete properties in the ITZ and in the bulk concrete; and (ii) limitations in the eld of view of the imaging method (the area covered by one image using the selected magnication).

(a) An example of the high variability of a concrete property.

(b) An example of insucient eld of view.

Figure 5.8: Porosity versus the distance from the steel surface. Examples of challenges in the determination of ITZ thickness.

Concrete properties often vary, and when the magnitude of their variation is similar to the variation of the ITZ average compared with the bulk average, determination of ITZ thickness may become problematic (Figure 5.8a). Such problems often occur due to large voids in the measured region. In some cases, the measurement technique may not cover sucient bulk concrete in order to yield an average bulk concrete measurement. This is the result of a compromise between the need for sucient resolution and the size of the ITZ in several images. Figure 5.8b presents an example of an ITZ that extends over the entire eld of view and therefore does not enable to measure the average bulk porosity. The transition from a porous zone (closest to threshold) to a less porous one (at the bulk concrete) is not always clear, and may cause diculties when automated work is needed. Fig 5.9 describes typical cases identied in this work. The following algorithm was developed to identify reliably the ITZ thickness.

51

CHAPTER 5. METHODS


(b) Porosity distribution below horizontal rebar

(a) BSE image of ITZ below horizontal rebar

(c) BSE image of ITZ around vertical rebar

(d) Porosity distribution around vertical rebar

Figure 5.9: ITZ around horizontal and vertical rebars 1. The point at which the porosity drops to the average value along the entire distribution line (the solid circle in Figures 5.9b and 5.9d) is calculated rst to avoid erroneous calculation by the eect of solid particles attached to the steel (see steelconcrete distance in the following) . 2. Then, the rst point on the porosity distribution line, away from the average found rst, at which the gradient of the property changes its sign from negative to positive is dened as the ITZ thickness (triangle in Figures 5.9b and 5.9d). This method was found to be robust for determining the ITZ thickness, especially in cases in which the porous zones were located at a certain distance from the steel surface, or in cases in which a large air void was observed in the image, both of which inuence the calculation of the average porosity and regression lines. Thus, the algorithm above 52

CHAPTER 5. METHODS


was found to be the most robust, and was used to determine the ITZ thickness. The script for this algorithm is presented in Appendix A.4. Figure5.10a demonstrates comparison of the ITZ thickness measurement with other method. The circle indicates the rst point at which the porosity declines and is equal to the average value (average porosity). The average is calculated for the whole prole, because the bulk region is unknown. The triangle indicates the last point after the red point at which the porosity declines (ITZ thickness). The possibility of determining ITZ thickness using the intersection between two regression lines (in green), one for the decrease in porosity from the steel surface to the average porosity point and the second for the decrease in porosity from the average point to the end of the prole, was assessed. The intersection of those lines was found to be very sensitive to uctuations in the porosity prole caused by large voids. So although the intersection indeed approximated the average porosity point in this prole, it was deemed inappropriate for ITZ thickness determination/estimation. Figure 5.10b demonstrates the shortcomings of using regression lines to estimate ITZ thickness.

(a)

(b)

Figure 5.10: Determination of ITZ thickness using its porosity prole

ITZ thickness should be interpreted separately for horizontal and vertical rebar. Thickness of the ITZ below horizontal rebars represents the thickness of voids formed due to bleeding (Figure 5.7 a and b) whereas the ITZ above horizontal rebars is more similar to that found around vertical rebars which is more dense (Figure 5.7 c and d. see also 5.5.4.1).

53

CHAPTER 5. METHODS


5.5.4.3 Steel-concrete distance

It should be noted that in most cases some concrete solids were found close to the steel surface and the porous area was found a small distance away from the steel. In such cases, the steel-concrete distance is smaller than the ITZ thickness.[106]. The distance between any point on the steel surface and the closest concrete solid particle is dened as the steel-concrete distance. Since there are as many steel-concrete distance data as the number of examined points on the steel perimeter, statistical functions such as the average, maximum, and standard deviation of the steel-concrete distance are used to represent the ITZ. The steel-concrete distance is obtained from the classied image. A distance matrix is created that represents the minimal Euclidean distance from concrete pixels (the rainbow-like colors in Figure 5.11b represent the distance from the concrete: blue being closer to the concrete and red being farther away). In the next stage, the corresponding distance for every pixel on the steel perimeter (the black frame in Figure 5.11b) is registered. The registered distances then are used to calculate the steel-concrete statistics that characterize the ITZ. The script which calculate the steel-concrete distance is presented in Appendix A.2.

(a) The model

(b) Distance from the concrete

Figure 5.11: Visualization of the steel-concrete distance calculation. 5.11a. A schematic model (red - steel, green - concrete, blue void). 5.11b. Distance from the concrete and the steel perimeter (blue - close, red/brown distant). The theory of localized corrosion (Subsection 1.2.3) emphasizes the importance of the distance from the buered environment for corrosion initiation [107, 32]. Concrete solids are known to have high buering capacity [33], hence, the distance of the steel from the concrete is expected to play an important role in the pitting of embedded steel.

54

CHAPTER 5. METHODS

5.6. CORROSION TEST

5.6 Corrosion test

The faces of the sectioned specimens for the corrosion test were subjected to cycles consisting of two weeks immersion in N aCl 6 % w/w solution (Figure 5.12), followed by two weeks in air at 30 % RH air and 30 °C. Corrosion initiation was monitored by the potential dierence between the upper and lower rebars in each concrete specimen. The lower rebar is the Figure 5.12: Corrosion test layout one closer to the chloride source and so is where corrosion is expected to initiate. The upper rebar is considered to be non-corroding and is therefore used as an internal reference for the electrochemical potential of non-corroding steel in concrete. Validation was done by measuring the potential against an Ag |AgCl half cell. A potential shift of more than 100 mV over the course of one day was taken to indicate passivity breakdown. 5.6.1 Validation

To validate the use of potential shift for corrosion initiation detection, some specimens were analyzed by electrochemical impedance spectroscopy (EIS). A SCE was used as reference electrod for EIS, and platinum electrode was used as caunter electrode. The spectra obtained were analyzed using the zt function, which was operated in MATLAB® [108]. All specimens were split open several months after corrosion initiation for visual inspection and corrosion validation. Result from specimens that did not exhibit visible indications of corrosion during the visual inspection were omitted from the data analysis. 5.7 Chloride measurement

The free chloride concentration at depassivation was determined as follows: Upon detection of active corrosion, 16 bores were drilled in the specimen parallel

to the corroding rebar at the rebar bottom depth, using a 4 mm drill. The powder from the drilling was collected. 55

CHAPTER 5. METHODS

5.7. CHLORIDE MEASUREMENT

40 ml distilled water were added to 2 gr samples of concrete drilling powder. Water-powder samples were shaken for two hours, and left to settle for 24 hours. 20 ml of supernatant uid were taken from each sample and acidied by adding 1 ml HN O3 1 M.

Chloride concentration was measured using an ion-selective electrode and calculated to yield the chloride concentration in terms of chloride weight per concrete weight ( Concrete). 5.7.1 Presentation of the chloride concentration in dierent units Chloride threshold is given in the literature in three dierent representation forms: i) percentage of cement weight; ii) concentration in water; and iii) percentage of concrete weight. i) Chloride concentration as chloride weight per cement weight (% Cement) was calculated by dividing the chloride concentration expressed as chloride weight per concrete weight by the cement content, and multiplying by the concrete unit weight of the specic mix (Equation 5.1). %Cement =

hConcrete · U W CEM

(5.1)

where %Cement is the chloride concentration as percentage of cement weight; hConcrete is the chloride concentration as permil of concrete weight; U W is the concrete unit weight (kg/m3 ); and CEM is the cement content (kg/m3 ). ii) To calculate the chloride concentration in the pore solution, pore volume per concrete mass was rst determined by measuring water absorption of the concrete. The water absorption of each concrete mix was measured by saturating the concrete specimen from the corrosion experiment in water for two weeks; weighing; drying at 105 C for one week; and weighing again. Pore volume was calculated using Equation 5.2.

°

p=

(ww − wd )/ρw wd

(5.2)

where p is volume of pores per unit weight of concrete (pore content), ww is the saturated concrete weight, wd is the dry concrete weight, and ρw is the specic gravity of water. The degree of saturation of the specimens at corrosion initiation is unknown. Two saturation degrees were assumed, and the chloride concentration in pore solution (gr/l) 56

CHAPTER 5. METHODS

5.8. SIMULATION OF THE ITZ

was calculated twice, once for each assumed saturation level: 1. The concrete specimen for corrosion was assumed to be saturated, including pores with entrapped air. The calculation was done by dividing the chloride concentration expressed as chloride weight per concrete weight by the pore content. 2. The concrete specimen for corrosion was assumed to be unsaturated, i.e. the entrapped air voids were assumed to be lled with air, while the capillary voids were assumed to be lled with water (i.e. saturated). The capillary void content was calculated by subtracting the air content from the pore content. The calculation was done by dividing the chloride concentration expressed as chloride weight per concrete weight by the capillary pore content. iii) chlorides content as percentage of concrete mass is directly determined from the experiment. 5.8 Simulation of the ITZ

(a) Electrode for ITZ model. yellow - Epoxy, Black -

(b) An electrode and an epoxy plug in-front.

Carbon, Grey - Steel

Figure 5.13: The physical model A physical model was built simulating a steel-concrete interface with a pre-specied distance between them. The objective of the model was to control the distance between steel and hydrated cement paste surfaces, in-order to study the relationship between

57

CHAPTER 5. METHODS


this distance and corrosion initiation. The model had two units: (i) a polished rebar, whereby the rest of its surface is embedded in epoxy, which serves as working electrode (WE), and a carbon, which serves as counter electrode CE; (ii) a counter surface of polished cement paste (Figure5.13a) or a counter surface of polished epoxy, used as an inert control surface to indicate whether or not the observations result from the presence of the hydrated cement paste. Polymer spacers, 105 mm thick, were used to control the distance between the WE and the counter surface (Figure 5.13b). The model, with one WE against cement paste and a second WE against epoxy, was submerged in simulated pore solution with composition as listed in Table 5.5. Impedance spectra were measured in increasing intervals for two weeks. The spectra were used to extract parameters for two equivalent circuits: Rs Rp /Cdl (where Rs - solution resistance, Rp - polarization resistance, Cdl - double-layer capacitance), and Rs Rp /CP Edl (where CP Edl represent the double-layer impedance) (Figure 5.14). Parameterization was done by application of the zt function, which was operated in MATLAB [108].

®

(a) With capacitor

(b) With CPE

Figure 5.14: Equivalent circuits The electrodes and polished surfaces placed in front of the electrodes were later examined using SEM and EDS to identify the precipitants accumulated on them.

Component SiO2 N aOH as Na KOH as K Ca(OH)2

pH

Table 5.5: Composition of simulated pore solution Concentration 6 mg/l 1 gr/l 8 gr/l 1.6 gr/l 12.9

58

CHAPTER 5. METHODS

5.9. DATA ANALYSIS

5.9 Data analysis

Data analysis was performed using Microsoft Excel and MATLAB 2007b. The data were analyzed by analysis of variance (ANOVA), where the main analysis tool, which was used, is the correlation coecient, which reects the noisiness and direction of a linear relationship (Figure 5.15, top row). The correlation coecient was used to indicate the existence or absence of relationships between the investigated variables1 . If the relationship is non-linear, the correlation may yield a clear indication of tendency, as long as the relationship is monotonous. The correlation coecient is related to the covariance by Equation 5.3: C(i, j) R(i,j) = p C(i, i)C(j, j)

(5.3)

where R(i,j) is the correlation coecient of the vectors i and j , C(i, j) is the covariance of the vectors i and j , C(i, i) and C(j, j) are the covariance of each vector with itself [96]. The p-value for the correlations was calculated as well. The p-value represents the probability of obtaining a correlation coecient from random data which is at least as valid as the one which is calculated for experimental data. The p-value was used to validate the signicance of the correlations coecients. p-value lower than 0.05 represent more than 95 % condence that the correlation is statistically signicant.

Figure 5.15: Several sets of (x, y) points, with the correlation coecient of x and y for each set. Adopted from [109] 1

The correlation coecient does not indicate the slope. When the slope is zero or innity, the coecient is zero because the variance of y or x is zero (see the middle column in Figure 5.15).

59

CHAPTER 5. METHODS

5.10. IMAGE ACQUISITION AND ANALYSIS

5.10 Image acquisition and analysis 5.10.1 BSE images acquisition 5.10.1.1 Preparation of BSE Specimens

Specimens for BSE analysis were cast with the same dimensions as specimens for corrosion monitoring, but included only one rebar along the center of the specimen (Figure 5.16). Four horizontal and four vertical specimens were prepared from every concrete mix. Two samples of every rebar orientation were oven dried after curing for one day; these samples are referred to as "early age" samples. The remaining samples were cured in water at 20 °C for 28 days and are referred to as "mature" samples. The samples were sectioned using a diamond saw to dimensions of about 30 mm×30 mm×70 mm, with the rebar axis parallel to the long dimension and approximately centered relative to the other dimensions. The prisms obtained were than sliced using a precision diamond saw to obtain one 5 mm-thick slice from the center of every prism. A thickness of 5mm was selected to reduce water movement through large pores at the steel-concrete interface during the sectioning process. The cooling water was saturated with concrete constituents to reduce leaching.

Figure 5.16: Concrete specimens for BSE analysis. Left horizontal specimen, right vertical specimen. The slices were oven dried and vacuum impregnated with epoxy in a mold for scanning electron microscope (SEM) specimen preparation. The epoxy was cured for at least two

60

CHAPTER 5. METHODS


days at room temperature and the specimens were then polished, as follows. First, a 45 mm diamond abrasive disk is used to expose the concrete and rebar. Next, the specimens are washed and a 12 mm diamond polishing disk is used for 4 min. at 150 rpm and a pressure of 6 lbs per specimen. The specimens are then washed and 800 mesh SiC paper is used twice for 2 min each time at 150 rpm and a pressure of 6 lbs per specimen. The specimens are washed and polished with a 6 mm diamond paste and cloth for 4 min. at 150 rpm and a pressure of 6 lbs per specimen. The specimens are washed again and polished with a 3 mm diamond paste on cloth for 4 min. at 150 rpm and a pressure of 6 lbs per specimen. After polishing, the specimens are washed with water, followed by methanol, and dried at 60 C before being coating with gold.

°

5.10.1.2 BSE Image Acquisition

The scanning electron microscope (SEM) used was JEOL 5300 by Jeol, and was operated in back-scattered mode (BSE) at 30 KV and WD 30. Magnication was ×100, whereby every pixel in the digital image corresponds to approximately 0.65 mm×0.65 mm. Images were taken in constant intervals around the rebar in order to create an unbiased representation of the ITZ. 5.10.2 Image analysis

®

All image analyses were performed using MATLAB R2007b (specic details and algorithms of the functions used can be found in the MATLAB documentation [96]). Clustering of the BSE image using only the gray level threshold was found to be unsatisfactory. The gray levels of several concrete constituents are equal, so the gray level is not explicit enough to distinguish between the concrete constituents. The following, therefore, presents a short discussion of the solution eventually applied, followed by a detailed description of the methods used and the comparisons made between them. To overcome the aforementioned problems, a dierent approach was needed. One proposed solution was to transform the problem from a one-dimensional clustering process to a multi-dimensional clustering process. Additional dimensions can be created using lters, which do not only enhance image sharpness and reduce noise, but also reveal other properties of the image such as texture patterns. In this way, additional image properties can be revealed that are not contained in the gray level of a particular pixel alone. The result of each lter action adds another dimension that can be used for clustering. More information on the lters used and the underlying theory can be found in [96]. 61

®

CHAPTER 5.

METHODS

5.10.

IMAGE ACQUISITION AND ANALYSIS

5.10.2.1 Low pass lter Low-pass lters reduce random noise originating from the detector reading or from the nature of the specimen, by relating the pixel to its surrounding pixels so that the pixel becomes more similar to its neighborhood (the opposite of sharpening the image). Thus, by applying a low pass lter, the gray level value of a pixel in a certain phase, which is surrounded by its own phase, becomes closer to its phase average intensity, hence reducing the standard deviation of the phase's gray level, implying that a phase should have a minimal continuous size. Low-pass lters used to relate pixels to their environment can be moving average or Gaussian lters, for example. The use of a Gaussian lter is preferable, since it calculates a ranked average, attributing a higher rank to the most proximate pixels.

Figure 5.17b depicts the result of applying a low-pass lter to the

image in Figure 5.17a.

5.10.2.2 High pass lter High-pass lters, on the other hand, enhance the sharpness of the image, thus clarifying non-sharp boundaries. A gradual change in pixel intensity perpendicular to a physical boundary is one of the causes for misclassication. Two main contributors to this phenomenon are:

(a) a gradual change in pixel intensity perpendicular to the steel-pore

interface, which maybe caused by cementitious deposition or rust, and (b) the nature of the cement paste, which has local high-density centers where cement grains are located together with hydration products, surrounded by areas with a gradual decline in density. This problem is similar in nature to poor focus. Both of these problems can be solved by sharpening the image: the high-pass lter enhances the contrast around boundaries, helping to overcome these boundary problems. Figure 5.17c depicts the result of applying a high-pass lter to the image in Figure 5.17a.

5.10.2.3 Entropy lter Some phases can not be segmented by their pixels' gray level but rather by the spatial variation of the pixels' gray level, known as texture. The main reason that the gray levels of the concrete and pore phases overlap is the presence of aggregates. Aggregates have distinct, characteristic textures that can be used to dierentiate them from the cement paste and its pores. Since entropy lters reveal such texture dierences (Figure 5.17d), they can be used to separate the aggregates from the other phases.

62

CHAPTER 5. METHODS


5.10.2.4 Selection of lter size Filter size should match the specic type of image and the image analysis purpose. When the size of the lter is equal to the size of the image or bigger, all extremity pixels, for instance, will receive approximately the same value. On the other hand, when the lter size is one pixel, the only change applied to the image will be a gain change. Filters can also be represented by frequency domains. According to this representation, the length in pixels represents the wave length, and the length divided by the corresponding image dimension is the frequency. Thus, a lter identical in size to the image size has a frequency of one and applying a zero-frequency lter means that the average of the image is applied to all pixels. In other words, the lter frequency should correspond to the size of the inspected phenomenon. In our case, no distinct frequency was found in randomly selected images so lter size was selected by trial and error, using visual judgment to select the entropy lter. The size of the selected texture lter was then adopted for all other lters. Specic references for the lters can be found in [96].

5.10.2.5 Application of lters The following procedure was used for all images: The original image was ltered using each of the following lters: 15×15 low-pass Gaussian lter, high-pass a = 0.1 non-sharp lter, 15×15 entropy lter, and because the texture after the high-pass lter was distinctively dierent from the original image, a 15×15 entropy lter was applied to the image obtained from the high-pass lter (Figure 5.17). Specic references for the lters can be found in [96]. The ltered images were re-scaled to have a minimum value of zero and a maximum value of 255, using Equation 5.4. Iscaled(i,j) =

n I(i,j) − minn (I)

maxn (I)

(5.4)

where I is the image matrix, and n equals 4 for the entropy lter, 2 for the high-pass, and 1 for all other images. The exponent n was used to convert the data to a more uniformly distributed form. This way, it is easier to distinguish between similar patterns.

5.10.2.6 Pixel clustering and classication by the Mean Shift (MS) method The MS algorithm modied by Georgescu [98] was used to identify the various clusters in each image. It will be denoted as the mean-shift (MS) algorithm in the followings. 63

CHAPTER 5. METHODS


(a) Original Image

(b) Low-Pass

(c) High-Pass (on original image)

(d) Entropy

Figure 5.17: An example of the results of applying dierent lters to the same BSE image that includes steel, pores and concrete elements.

64

CHAPTER 5. METHODS


This algorithm was applied on a representative sample of pixels from the image and by that the clusters' centers was found. Once the centers were found each pixel of the whole number of pixels in the image was assigned to the nearest center. Sensitivity analysis revealed that 1% of pixels in an image (about 30k pixels per image rather than 3M pixels) constitute a representative sample for the determination of clusters' centers (Figure 5.18) , thus the computation time has reduced signicantly. The use of the cluster centers found by the MS algorithm, for subsequent clustering or classifying by the cluster centers, will be denoted in the followings as the MS method (note the dierence between "method" and "algorithm"). The initial parameters of the MS algorithm, in this work, were: K = 10, L = 15, and h = 20 (where K is the number of cast in partition, L is the number of partitions that determine the expected volume of the cluster; and h is the radius around the investigated point [98]). These parameters usu- Figure 5.18: Sensitivity analysis for representing sample size for image ally yield 3 to 5 clusters. When the autoanalysis mated clustering process yielded no clusters that could be identied as pores, the clustering process was repeated with K = K + 10 and L = L×0.85, in order to identify more clusters. To classify the clusters, the sum of the dimensions of the original, the low-pass, and the high-pass images of each cluster was calculated. The cluster with the lowest sum or with a sum that is smaller than a pre-dened threshold, was classied as a pore. Steel, which creates only one cluster in each image, always has the brightest value, thus, the single cluster with the highest sum was classied as steel. Pixels that were classied as steel due to their high intensity, but do not belong to the single body that was previously identied as steel were subsequently classied as concrete, as were all other pixels that were not classied as steel or pores. Figure 5.20 presents an example of clustering and classication of the image in Figure 5.17a. Here, clustering yielded ve dierent clusters. Four of the ve clusters are well dened as aggregates, steel, cement paste, and pores. It is not clear whether the fth cluster is aggregate or cement paste. However, for the purpose of studying the pore structure in the vicinity of steel, the number of clusters was 65

CHAPTER 5. METHODS


Figure 5.19: Clustering and classication owchart

(a) Clustered

(b) Classied

Figure 5.20: Example of Clustering and Classication reduced, during the classication step, to these classes only: steel, pores, and concrete solids, as shown in 5.20b. The script for performing the clustering and classication is presented in Appendix A.1. To ensure algorithm reliability, all images were visually inspected to assure reasonable classication before proceeding with data reduction. Visual inspection takes about 2 seconds per image, allowing quality control without consuming too much human working time. In cases where visual inspection detected mis-classication (primarily of pores), classication of clusters as pores was repeated. This time the threshold for classication was set to a level that was determined manually on a typical image and was found to be representative to most of the images. Clusters with sums lower than this threshold were classied as pores. Such a threshold enables more than one cluster to be classied as a pores cluster, as opposed to identifying the cluster with the lowest value as a pores cluster. Pores identied by more than one cluster are typical to images of horizontal

66

CHAPTER 5. METHODS


specimens, in which a large pore is typically located below the rebar. This pore has different characteristics, especially in terms of its texture, compared with the pores in the paste. After the reclassication procedure was performed, the visual inspection identied misclassications in only about 4 % of images.

5.10.2.7 Estimation of classication error The real phase represented by each pixel is unknown, thus, the classication error can be only estimated. The error estimation is based on the underlying hypothesis of each method; i.e. clustering by the MS algorithm; and the Gaussians, used by the GLT method, do represent the real gray-level distribution of the phases in a given image. For both methods, the error is estimated, by dividing the number of erroneously classied pixels, by the number of pixels in the relevant phase as described in the following. It should be emphasized that the error estimate is determined dierently in each of the methods thus comparison of errors is valid within each method only. The subset of pixels, which used to nd the clusters centers, were clustered and classied twice: rst by the MS algorithm and then by the MS method. The fraction of pixels which were assigned into dierent clusters, or dierent phases by each process: directly by the MS algorithm, or by the MS method, was calculated. The clustering error of the MS method is dened as the fraction of pixels which were assigned into dierent clusters by the MS method and algorithm. The classication error of the MS method is dened as the fraction of pixels which were assigned into dierent classes by the MS method and algorithm. One should note that a clustering error does not necessarily cause a classication error. Because, several clusters may be classied into the same phase. These two errors were used to estimate the robustness of clustering and classication by the MS method.

5.10.2.8 Comparison of the MS method with Gray level thresholding Former works in the eld of ITZ research, which investigated the ITZ by applying BSE imaging only, used gray level thresholding (GLT) for pixel classication [95, 84, 91]. Thus, a comparison between the MS and GLT method was performed.

Classication by the GLT method Classication by the GLT method was performed by tting Gaussians to the image histogram, using the MATLAB "curve tting tool". The Gaussian with the lowest oset (the lowest mean) was assumed to represent the pore phase, whereas the Gaussian with the highest oset (the highest mean) was assumed 67

CHAPTER 5. METHODS


to represent the steel phase. All other Gaussians were assumed to represent all other concrete phases. The number of Gaussians was limited to the minimum number needed to t the curve with at least one Gaussian of low oset. The GLT method was considered to have failed if the curve may be tted with Gaussians with negative gain proles only or with gains that are greater than the number of pixels in the image. Those Gaussians that represented phases other than pores and steel were assumed to represent the concrete phases; thus, they were summed up to create a single histogram column. The intersecting points between this histogram and the Gaussians representing the pores and steel were set as the threshold points between the phases. The total number of pixels in a specic phase was estimated as the area under the Gaussian that represents the said phase. The error of classication was estimated by dividing the number of erroneously classied pixels (The gray area in Figure 5.21) by the estimated total number of pixels that belong to the relevant phase. Error estimation of the GLT method

5.10.2.9 Characterization of the ITZ by image analysis

The classied image is not a usable data yet. It is merely a stage in the process of extracting data from images, which is called image analysis. The underlying data incorporated in the image still have to extracted. In data reduction process started by reducing the data from 8 bit matrix (original image) to 3 bit matrix (classied image). The next stage is to calculate several scalars or a short vector which will represent the data in the image which is meaningful. Later the data could be used for extracting information about the ITZ. In this work, data reduction goals are to extract the parameters of ITZ described in Subsection 5.5.4.

68

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5.10.

IMAGE ACQUISITION AND ANALYSIS

Figure 5.21: Inherent errors of using GLT method

69

6 Results 6.1 Relationships between mix composition and fresh mix properties

Mix compositions and properties of the fresh concrete are usually inter depended. Where such dependency exist, deriving clear conclusion about the parameters inuencing the ITZ properties based on statistical analysis becomes more complicated. When two properties are correlated with each other, it may appear as if both are inuencing a certain ITZ property whereas only one of them is the real inuencing parameter. Hence, prior analysis of the relationship between mix compositions and fresh concrete properties is essential. In addition, some of these correlations are straight forward. Thus, these correlations will be used to validate the use of the proposed statistical tool for analyzing test results. Table 6.1 shows the correlation coecients between the compositions and properties of the fresh concrete mixes. Correlations with statistical condence larger than 95% (p < 0.05) are emphasized. The concrete mix exhibits the expected correlations between its constituents. The powder content is correlated with the cement content (0.79) because the cement composes the main powder component in the concrete mixes; the powders and cement contents are inversely correlated with the W/C ratio (-0.76, -0.89 respectively) because more cement usually means lower W/C ratio. Increasing powder content required increase in WRA to keep the concrete workable, identied by a correlation coecient of 0.83. Coecient value of 0.90 was identied also between WRA content and W/P ratio. As expected, the slump is correlated with the water content. The slump is correlated with the cement and powder contents as well. These correlations may result from the use of WRA (water reducing agent), which is strongly correlated with the W/P ratio (-0.90). The negative correlation of the slump with the W/C ratio may be a result of the co-linearity of the W/C ratio and the powder content. These nding validate the use of the ANOVA as a mean to identify relationships between the variables studied here. The total bleeding and bleeding rate are co-linear (0.94), i.e. increase in one is accompanied by proportional increase of the other. Surprisingly, several parameters are

71

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6.2. MICROSCOPY

correlated with the total bleeding and bleeding rate in opposite manner. The powder content reduces the bleeding rate (-0.63) while increasing the the total bleeding (+0.74). Reduction in the bleeding rate is expected because the powders supply more surface area for friction with the water movement. But the total bleeding increases with the powder and cement contents. This held true for the slump, which its correlation with the powder and cement contents may result from the use of WRA (which its dosage is higher in mixes with ller. Table 5.3). The WRA reduces the friction between the powders particles and allow them to pack in denser form. The water displaced from the denser bulk may be the resource for the higher amount of bleed water. These mechanisms explain why the slump is correlated with the total bleeding only, both inuenced by WRA, while the powders content correlated with the bleeding rate and total bleeding.

6.2 Microscopy

The ITZ around horizontal rebars comprises three distinctive regions: the region above the rebar, the region below the rebar, and the transition region between these two regions (Figure 6.1a). The ITZ around vertical rebar is more uniform, as long as no air pockets are in contact with the rebar surface (Figure 6.1b). Particles near the rebar surface are often graded. Aggregates closer to the surface are smaller, usually of sub-millimeter in size, and are larger, up to 3 mm in size farther away from the rebar (Figure 6.2a). Regions with larger aggregates rarely enter into the picture frame. In several images, the ITZ around vertical rebars and above horizontal rebars is so thin that it is barely visible (Figure 6.2b). In images in which large voids are found close to the rebar surface, a thin layer of solids can usually be seen on the rebar surface itself. Three types of these solids can be identied by visual inspection: (i) adhered cement paste (Figure 6.2c the brighter particles are of higher density; hence, recognized as the remains of cement grains); (ii) thin low-density deposits, which does not contain granular mater (Figure 6.2d); and (iii) corrosion products originated from the steel (Figure 6.2e). The rebar surface, though seem smooth to the naked eye, is irregular. Irregularity is in the order of 10 mm. Many local pits were identied all over the surface (Figure 6.2). Shallow pits are usually lled with solids (as can be seen in Figure 6.2).

72

73

a The

-0.35

-0.42

-0.52

0.55

Rate

Duration

0.28

0.77

0.85

0.53

1.00

0.02

-0.61

-0.76

-0.45

-0.93

-0.07

0.74

0.90

0.56

1.00

-0.93

0.79

-0.01

-0.59

-0.85

-0.46

-0.90

0.83

-0.71

0.48

-0.50

0.60 -0.52

WRA

W/P

quasi SCC mix, W52C 54, was not included for correlation with slump

Bleeding

-0.40

-0.57

0.62

Total

0.50

a Slump

0.79

-0.76

0.64 -0.52

-0.29

0.64

1.00

-0.76

-0.89

1.00

-0.89

0.60

0.28

W/C

W/P

0.18

Cement

Powders -0.29

W/C 0.28

0.18

Cement

Powders

1.00

Water

Water

0.89 0.14

1.00

0.64

0.90

-0.76

0.85

-0.57

0.62

0.05

1.00

0.89

0.63

0.74

-0.61

0.77

-0.52

0.55

Rate

Bleeding Total

0.57

a

0.63

0.64

1.00

0.56

-0.45

0.53

-0.40

0.50

Slump

Table 6.1: Correlation among mix properties

CHAPTER 6. RESULTS 6.2. MICROSCOPY

CHAPTER 6. RESULTS

6.3. IMAGE ANALYSIS

(a) Horizontal

(b) Vertical

Figure 6.1: ITZ around rebar 6.3 ITZ characterization by image analysis

6.3.1 Error of the MS classication method The estimated error1 in clustering and classication by the MS method depends on the number of clusters. Figure 6.3 presents the relationship between the number of clusters and the relative error in clustering and classication. As the number of clusters increases from 3 to 5, the error decreases. The clustering error does not change signicantly for larger numbers of clusters whereas the error in classication decreases continuously to a value lower than that of the clustering error. This result indicates that each phase might be composed of several sub-phases, represented by dierent clusters. The centers of these clusters are very close to each another, which explains why the clustering error does not decrease with the increase in the number of clusters. The concrete is composed of a large number of phases; the aggregates are made of various minerals; the cement paste is composed of non-hydrated cement grains, hydration products of high and low density; pore phase which is composed of large voids (air bubbles, large pores below horizontal rebars), small voids due to inecient compaction, voids within aggregates, and micro-voids in the cement paste. These phases can be identied only if a dierent cluster is attributed to each of them. This study, however, 1

See Subsection 5.10.2.7 for denition of the estimated error

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CHAPTER 6. RESULTS

6.3. IMAGE ANALYSIS

(a) Aggregates arranged by size

(c) Adhered

cement

(d) Chemical deposition on steel

paste

(b) Dense ITZ

(e) Corrosion products

Figure 6.2: Common phenomena in the ITZ (steel - white zone)

75

CHAPTER 6. RESULTS

6.3. IMAGE ANALYSIS

Figure 6.3: Estimated error in clustering and classication versus number of clusters

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6.3. IMAGE ANALYSIS

concentrated on three phases only: concrete solids (aggregates, cement paste), pores, and steel. Thus, increasing the number of clusters renes the identication of these phases, and by that reduces the classication error. The average estimated classication error for the images in this work was 9 %. Due to the low phase-classication error, 94 % of the images were successfully classied by the MS method.

6.3.2 Error of the GLT classication method Gray level threshold (GLT) could not be used successfully in the images obtained in this study to dierentiate the pore phase from concrete. By applying the assumption that the various phases of interest may be represented by a series of normal distributions, the following results are obtained: About 34 % of the pixels that should represent pores were erroneously classied as

concrete solids.

About 14 % of pixels classied as pores should represent concrete solids. Only 75 % of images can be successfully classied. i.e. for any pixel in the other

25 % of images, the probability of being a pore is less than the probability of belonging to some other phase, so that no pixel can be classied as a pore.

6.3.3 Comparison between the mean-shift method (MS) and the gray level thresholding method (GLT) The imparity between the high rate of successful classication by the MS method and the inferior results obtained by the GLT method is explained by the following examples. Two dierent cases of GLT versus MS-based classication are demonstrated. In the rst case, an image is successfully classied using the MS-based method, but exhibits a gray level histogram that cannot be classied using the GLT method (Figure 6.4). The second case presents an image in which the gray level histogram behaves according to the theory of the superposition of phases that are each characterized by a normal distribution (Figure 6.6, which refers to the image in Figure 5.17a). Thus, this image can be successfully analyzed by the GLT method. In the following demonstration, the two images are analyzed by both methods. It should be noted that the rst case is more typical to the images of dense ITZ as found near vertical rebars and above horizontal rebars. Although 75 % of images can successfully be classied by the GLT method (as in the second case), these images include only cases of porous ITZ located under horizontal 77

CHAPTER 6. RESULTS

6.3. IMAGE ANALYSIS

rebars or mixes with high W/C ratios. Hence these images do not properly represent the ITZ.

Case I

Figure 6.4 presents a BSE image (Figure 6.4a), MS classication for this image (Figure 6.4b), curve tting using the GLT method (Figure 6.4c), and a comparison between the GLT and MS methods (Figure 6.4d). Although the image contains enough data points to achieve a good tting of the gray level distribution to some Gaussians, the tting of more than three Gaussians is complex and may yield unreasonable physical meanings, such as phases with a negative number of pixels. In this image, for example, tting three Gaussians to this gray level histogram yields a Gaussian representing the pore phase that has a negative mean (in such cases, all pixels with negative values assign a gray level of 0), and an area that is larger than the image itself (which physically cannot represent the pore phase). Trtik et al. (2009) [110] demonstrated how artifact peaks may appear in a histogram, representing measurement of a mixture of two or more phases. It was demonstrated, that such peaks appear when a single phase extends over a size which is similar to the interaction volume of the measuring device (in this case is the volume of the concrete specimens that interacts with the electron beam). Their analysis may explain the the appearance of the low-mean Gaussian used by the GLT to represent pores. The error obtained by applying GLT to this image is large (Figure 6.4c). Classication into phases using the MS method (Figure 6.4b) yields similar results as those obtained by applying visual judgment. Although the mode (i.e. the most probable value) of the pores' gray level is much higher than the mode expected from Gaussian ttings, which means that pores cannot be separated from concrete solids by the GLT method (Figure 6.4d), the classied image seems to represent the microstructure better than does the GLT. The error estimation for this image, according to the MS method, is about 11 %. Figure 6.5 demonstrates the risk of underestimating pore area using the GLT method for ITZ micro-structure characterization. Figure 6.5a presents a zoom into Figure 6.4a. Figure 6.5b presents the GLT classication of 6.5a, where red represents steel, green pore, and blue - concrete solids. Voids, which are in proximity to the rebar, remain undetected by the GLT method. Furthermore, since the tting of Gaussians tends to be more complicated as more phases are involved, and since the number of phases expected to be observed in the image is unknown before the onset of the tting procedure, automatic thresholding by GLT often yields unreasonable results. When phases are distributed normally but the Gaussians identied for the specic phases fully overlap each other (as seen in Figure 6.6b - continuous and dashed lines represent two phases of the concrete solids), classication by MS provides a powerful

78

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6.3. IMAGE ANALYSIS

(a)

(b)

(c)

(d)

Figure 6.4: Analysis of the gray level classication histogram. 6.4a Original image. 6.4b Image classied by MS (red steel, green pores, blue concrete solids). 6.4c Gaussians tting the histogram, including classication error using GLT. 6.4d Comparison between GLT classication and MS classication.

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6.3. IMAGE ANALYSIS

(b)

(a)

Figure 6.5: (a) Zoom into GLT classication of the image in Figure 6.4a. (b) GLT classication of 6.5a. tool. The phases can be divided successfully into aggregates and cement paste as shown in the clustered image in Figure 5.20a, even though such separation is impossible by the GLT method.

Case II

Figure 6.6a demonstrates curve tting of three Gaussians to the gray level histogram of the image shown in Figure 5.17. The gray circles represent the original histogram of the image. The continuous curve represents tting to only one Gaussian, the dashed curve represents tting to two Gaussians, and the dotted curve represents tting to three Gaussians (it is dicult to distinguish it from the original histogram because they almost completely overlap). In this case, tting the histogram to only two Gaussians yields a poor tting in the range of the lower values, where the pores are expected to be located. It is evident that, for this image, at least three Gaussians are needed to identify the pore phase, while maintaining a good t with the original histogram. Figure 6.6b shows each of the three Gaussians that together represent the original histogram. The concrete phase is composed of two overlapping Gaussians - the continuous and dashed curves - and the pores are represented by the dotted curve. By setting the threshold level that distinguishes between concrete and pores to 25, the pixels are classied by the GLT method using the optimal threshold. As a result of thresholding, some pore pixels are classied as concrete solids and some concrete solid pixels are classied as pores. The gray area in Figure 6.6b represents pores that are erroneously classied as concrete; and the black area represents concrete that is erroneously classied as pore. Applying the MS method yields a histogram that is similar to the Gaussians obtained by the GLT method, but pores can have higher gray-level values than some concrete solids, and vice verse (Figures 6.6c and 6.6d). It is, thus, possible to assign each pixel

80

CHAPTER 6. RESULTS

6.3. IMAGE ANALYSIS

(a)

(b)

(c)

(d)

Figure 6.6: Analysis of the gray level histogram for the image in Figure 5.17. 6.6a Fitting to one Gaussian or the sum of two or three Gaussians (GLT algorithm). 6.6b The three Gaussians composing the histogram. 6.6c Phase histogram as classied by the MS algorithm, with the Gaussians (GLT) for comparison. 6.6d Zoom into Figure 6.6c.

81

CHAPTER 6. RESULTS

(a) Original

6.3. IMAGE ANALYSIS

(b) GLT

(c) MS

Figure 6.7: Original image and its classication by the GLT and MS methods its true character, which is essential for calculating the geometrical parameters required for ITZ determination. The image histogram, which is the sum of the phase histograms, almost overlaps its Gaussian estimation. It is important to note that taking the GLT threshold to the intersection of the phase histogram, as obtained by the MS method, does not yield results similar to the classication by the MS method, because GLT does not permit pore pixels to have higher values than have solids pixels, and vice verse. It can be concluded from this comparison, that the GLT method underlying assumption, if not wrong, yet cannot be generalized, as been demonstrated by the rst case.

Figure 6.7 demonstrates the importance of the classication method for studying the pore structure. Voids are clearly seen on the steel perimeter (right part of Figure 6.7a) and ne voids are spread throughout the cement paste. If the GLT method is applied, voids are not identied on the steel perimeter (Figure 6.7b). The ne microstructure of the ITZ is missing in Figure 6.7b, but is clearly seen in the MS classied image (Figure 6.7c). Implications for the measurement of ITZ properties

It can be concluded, that while the use of GLT might be satisfactory for determining the porosity, a more complicated method, such as the MS method presented here, is needed to identify the ITZ microstructure.

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6.3. IMAGE ANALYSIS

6.3.4 ITZ properties from image analysis

The ITZ structure is age depended. The ITZ is usually more distinct from the bulk paste at early age. Hereafter there is a short discussion regarding the age of the specimen analyzed. Next the results of the ITZ properties of the mature specimens are presented. 6.3.4.1 ITZ in mature and early age specimens

The ITZ around aggregates is known to be more visible at early concrete age [89]. On the other hand, the ITZ properties of the ITZ which might aect the chloride threshold may change from the early age to the mature concrete. Thus, it was important to indicate whether the ITZ around the rebar could be well characterized for mature concrete specimen. Early age specimens were used for comparison. In-order to evaluate the quality of characterization, an hypothesis, that better characterization will reveals stronger correlations among characteristics within the same image and expose high variability among specimens, was laid. This means that each specimen is well characterized and diers from other specimens, enabling the identication of relationships between ITZ properties and other results. When investigating the correlation between ITZ properties, as were quantied by image analysis for each image separately, good correlation was found only among parameters within separate groups of properties: steel-concrete minimal distance and ITZ thickness (Table 6.2). No correlation was found between parameters from dierent groups (the groups were: porosity, ITZ thicknesses, and steel-concrete distances). Examining the correlations in early age and mature specimens separately (Table 6.3 and 6.4) reveals that ITZ properties of mature specimens are better correlated, although the variation of the data tends to increase in these specimens, relative to the early age. Thus for example, the correlation coecient between the maximum steel-concrete distance and the standard deviation of the steel-concrete is 0.94 at early age, and increased to 0.97 for mature specimens, while the range of measured steel-concrete distance increased as well. This indicates a better ability to dierentiate between ITZ properties in mature specimens, and so it was decided to use specimens of mature concrete for image analysis. 6.3.4.2 ITZ properties

The ITZ properties, per mix and rebar orientation are presented in Table 6.5. The maximal porosity which was found for all the horizontal rebar was 1.0 or very close to this value. Around vertical rebars, such high value of porosity (higher than 0.95) was 83

CHAPTER 6. RESULTS

6.3. IMAGE ANALYSIS

Table 6.2: Correlation matrix and statistics for ITZ characteristics of all images (1134 images). Correlations with p-value below 0.05 are emphasized. . Maximum ITZ Steel concrete distance porosity thickness Maximum Average Standard deviation Maximum porosity 1.00 0.50 0.31 0.27 0.27 ITZ thickness 0.50 1.00 0.16 0.14 0.14 Steel Maximum 0.31 0.16 1.00 0.88 0.97 concrete Average 0.27 0.14 0.88 1.00 0.93 distance Standard 0.27 0.14 0.97 0.93 1.00 deviation Statistics Units % mm mm mm mm Average 51 131 14 2 2 Standard deviation 30 88 20 5 5 Minimum 2 19 1 1 0 Maximum 100 529 387 83 118

Table 6.3: Correlation matrix and statistics for ITZ characteristics of early age specimens images (349 images). Correlations with p-value below 0.05 are emphasized. Maximum ITZ Steel concrete distance porosity thickness Maximum Average Standard deviation Maximum porosity 1.00 0.51 0.27 0.16 0.22 ITZ thickness 0.51 1.00 0.11 0.03 0.09 Steel Maximum 0.27 0.11 1.00 0.80 0.94 concrete Average 0.16 0.03 0.80 1.00 0.92 distance Standard 0.22 0.09 0.94 0.92 1.00 deviation Statistics Units % mm mm mm mm Average 58 139 10 2 2 Standard deviation 31 101 9 2 2 Minimum 4 19 1 1 0 Maximum 100 529 72 23 17

84

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6.3. IMAGE ANALYSIS

Table 6.4: Correlation matrix and statistics for ITZ characteristics of mature specimens images (785 images). Correlations with p-value below 0.05 are emphasized. Maximum ITZ Steel concrete distance porosity thickness Maximum Average Standard deviation Maximum porosity 1.00 0.49 0.37 0.34 0.32 ITZ thickness 0.49 1.00 0.21 0.19 0.18 Steel Maximum 0.37 0.21 1.00 0.89 0.97 concrete Average 0.34 0.19 0.89 1.00 0.93 distance Standard 0.32 0.18 0.97 0.93 1.00 deviation Statistics Units % mm mm mm mm Average 48 139 15 3 3 Standard deviation 29 101 23 5 6 Minimum 2 19 1 1 0 Maximum 100 470 387 83 118 found only for ve mixes, out of the 16 mixes tested. The average porosity showed similar trends of higher porosity for all the horizontal rebars compared with all the vertical bars. The average and maximum ITZ thickness are larger for horizontal rebars (144 mm and 470 mm respectively) relative to the vertical rebars (104 mm and 339 mm respectively). The same is valid for the steel-concrete distance. The distances are larger for horizontal rebars (average 3.38 mm and maximum 83.4 mm) relative to the vertical rebars (average 1.73 mm and maximum 51.4 mm). It should be noted that steel-concrete distance is the distance from the steel surface to the closest concrete solid particle, which is completely dierent from the ITZ thickness which is the distance from the steel surface to the point where the porosity reaches the value of the bulk paste. The steel-concrete distance is calculated for every pixel on the steel perimeter; and the ITZ thickness is found from the porosity prole which is calculated for each image. Thus, in general, the maximum concrete-steel distance is smaller than the ITZ thickness, but an instance can occur where the maximum steel-concrete distance is larger than the ITZ thickness as for the horizontal bar in the mix W52C17. A discussion and analysis of the factors inuencing the ITZ properties is presented in Section 7.1.

85

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6.3.

IMAGE ANALYSIS

Table 6.5: ITZ properties by concrete mix

Average

Standard deviation

Maximum

Average

distance [mm]

alignment

Maximum

[mm]

mix

Standard deviation

Steel-concrete

Average

ITZ thickness

Maximum

Porosity

W40

H

1

0.611

0.367

365

132

113

35.2

6.02

W40B2

H

0.999

0.735

0.247

392

142

97.1

43.0

10.0

W45

H

1

0.513

0.322

379

127

94.9

97.3

20.3

W45C04

H

1

0.606

0.327

300

120

78.0

75.6

15.3

W45C08

H

1

0.557

0.278

470

139

82.6

69.0

13.0

W45C12

H

1

0.782

0.322

529

235

109

49.0

17.7

W45C16

H

0.995

0.559

0.305

326

122

90.8

25.0

8.99

W45C20

H

1

0.756

0.297

398

150

98.0

386

27.3

W50

H

0.999

0.402

0.270

353

135

93.0

77.6

16.7

W52C08

H

1

0.573

0.323

392

155

100

48.3

17.8

W52C12

H

0.999

0.555

0.258

379

162

89.3

37.9

11.4 32.7

W52C17

H

1

0.555

0.326

268

126

59.7

a 281.9

W52C54

H

1

0.488

0.235

372

145

68.2

42.9

14.8

W55

H

1

0.546

0.349

340

172

88.9

59.2

12.4

W60

H

1

0.639

0.282

411

170

133

22.3

8.91

W65

H

0.994

0.487

0.356

444

146

130

26.0

9.10

W40

V

0.905

0.360

0.173

287

134

61.8

27.1

7.27

W40b2

V

0.988

0.297

0.217

274

83.6

58.4

19.0

7.66

W45

V

0.972

0.422

0.215

255

79.9

38.1

54.0

14.5

W45C04

V

0.899

0.397

0.181

228

88.6

44.3

21.3

12.2

W45C08

V

0.519

0.364

0.098

228

128

32.4

37.4

11.8

W45C12

V

0.470

0.285

0.108

281

118

54.4

26.3

9.81

W45C16

V

0.700

0.348

0.124

189

95.0

46.0

10.6

5.18

a

Steel concrete distance is measured for every pixel on the steel perimeter separately, while the ITZ

thickness is measured for an image of up to about 1 mm around the rebar. distance may be grater than the ITZ thickness.

86

Thus, local steel-concrete

CHAPTER 6. RESULTS

Standard deviation

Maximum

Average

Standard deviation

Maximum

Average

V V V V V V V V V

Steel-concrete distance [mm]

Average

W45C20 W50 W52C08 W52C12 W52C17 W52C54 W55 W60 W65

Table 6.5: ITZ properties by concrete mix Porosity ITZ thickness alignment [mm]

Maximum

mix

6.3. IMAGE ANALYSIS

0.588 1 0.998 0.901 0.662 0.604 0.514 0.996 0.699

0.323 0.613 0.457 0.351 0.357 0.347 0.303 0.405 0.297

0.153 0.295 0.220 0.164 0.133 0.133 0.113 0.216 0.137

202 281 320 340 202 274 222 372 248

86.9 124 135 118 84.9 141 117 103 110

41.8 66.3 80.5 64.7 42.8 49.0 45.5 76.1 43.6

24.9 60.2 13.6 28.5 18.6 235 9.43 72.3 23.8

8.86 11.9 7.61 7.35 7.98 18.2 4.72 12.1 7.82

6.3.4.3 Relationships between ITZ properties as revealed in image analysis

It is important to check if ITZ properties are interrelated. If such relationship can be found, it might cast on the analysis of mix-ITZ and ITZ-chloride threshold relationships. The statistical characteristics (average, maximum, and standard deviation) of the concrete-steel distances for the horizontal bars were found to be lower than the ITZ thickness (Table 6.6). Comparing these results with the images reveals three main causes for this observation, which are related to bar orientation. In images of ITZs of horizontal casts, a layer of deposits adhered to the steel surface, separating it from the larger voids. These deposits were cement paste particles that adhered to the steel surface before bleeding created large voids under the steel, rust and scale from the steel itself, and other minerals deposited on the steel surface from the bleed water (Figures 6.2c to 6.2e). All of these substances were classied as concrete components. Other possible causes for the appearance of a layer of concrete solid pixels, could be an artifact from non-sharp intensity change from steel to an adjacent void caused by the image acquisition system, and for random noise. Non-sharp images and random noise should, however, have been overcome by the high- and low-pass lters.

87

CHAPTER 6. RESULTS

6.3. IMAGE ANALYSIS

Table 6.6: Correlation matrix and statistics for ITZ characteristics of mature horizontal specimens (438 images). Correlations with p-value below 0.05 are emphasized. Maximum ITZ Steel concrete distance porosity thickness Maximum Average Standard deviation Maximum porosity 1.00 0.57 0.37 0.33 0.32 ITZ thickness 0.57 1.00 0.22 0.21 0.20 Steel Maximum 0.37 0.22 1.00 0.88 0.97 concrete Average 0.33 0.21 0.88 1.00 0.92 distance Standard 0.32 0.20 0.97 0.93 1.00 deviation Statistics Units % mm mm mm mm Average 58 145 19 3 4 Standard deviation 32 93 28 7 7 Minimum 4 19 1 1 0 Maximum 100 470 387 83 118

The standard deviations of the horizontal cast parameters were very high relative to the average values. This stems from the dierences in structure around the bar (the ITZ was denser above the bar, and a large void was located beneath it). Diamond [89] mentioned that the variation of ITZ around a single aggregate can be greater than the variation of the average between dierent aggregates. This phenomenon is evident in the porosity and thickness data for the ITZ around horizontal bars (Table 6.6). Furthermore, the standard deviation of the distance between concrete and steel, in any single image, was much larger than the standard deviation of the average distance for all of the images. Homogeneity itself is a characteristic of the ITZ, thus, the extreme value of an ITZ property is a probabilistic value, due to the high standard deviations of ITZ properties. As a result, the standard deviation may be as important as the average parameter, for instance in the case of steel-concrete distances. This situation is quite dierent for vertical bars; here the dierence between maximum distances and thickness was about 90 mm (Table 6.7). The standard deviation of the steelconcrete distance was high relative to the average values, implying that this parameter is not normally distributed. A visual examination veried that the maximum distance parameter gives a good estimate of the ITZ properties as perceived by the human eye for images of vertical cast specimens. 88

CHAPTER 6. RESULTS

6.4. ITZ CHARACTERIZATION BY PULLOUT TEST

Table 6.7: Correlation matrix and statistics for ITZ characteristics of mature vertical specimens (347 images). Correlations with p-value below 0.05 are emphasized.. Maximum ITZ Steel concrete distance porosity thickness Maximum Average Standard deviation Maximum porosity 1.00 -0.07 0.19 0.18 0.17 ITZ thickness -0.07 1.00 -0.03 -0.08 -0.04 Steel Maximum 0.19 -0.03 1.00 0.94 0.97 concrete Average 0.18 -0.08 0.94 1.00 0.98 distance Standard 0.17 -0.04 0.97 0.98 1.00 deviation Statistics Units % mm mm mm mm Average 36 105 11 2 2 Standard deviation 17 55 14 3 4 Minimum 2 19 1 1 0 Maximum 100 340 235 51 69 6.4 ITZ characterization by pullout test

The pullout test has been taken at dierent specimens age due to logistic limitations. For analysis, the results are divided into two groups of age at test range: 47 to 100 days, and 174 to 246 days. Table 6.8 shows the average pullout results and the specimens age at test for horizontal and vertical specimens according to concrete mix. Pullout properties of vertical rebars have higher value than do the horizontal specimens. Adhesion strength varies from 1.42 MPa to 10.19 MPa; bond strength is only slightly higher than the adhesion strength1 ; and the strength are higher for vertical rebars, relatively to horizontal rebars in the same mix. The slope and bond energy do not show any clear tendency. Correlations of the pullout results with other parameters are presented in the followings and shortly discussed. Only parameters, which has correlations with the pullout results are presented in this section. 6.4.1 Pullout correlation with mix composition and properties

No signicant correlation was found between pullout results of horizontal rebar, which were done with specimens at age range of 47 to 100 days, and mix composition or properties (Table 6.9). For horizontal specimens at age range of 174 to 246 days, there are 1

The adhesion strength is the maximum stress until the upper tip of the rebar starts to move. The

bond strength is the maximum stress during the pullout test (see Subsection 5.5.3).

89

CHAPTER 6. RESULTS


Mix

Orientation

Table 6.8: Averaged pullout results per cast orientation with its dtandard deviation.

W40

H

W40B2

H

W45

H

W45C04

H

W45C08

H

W45C12

H

W45C16

H

W45C20

H

W50

H

W52C08

H

W52C12

H

W52C17

H

W52C54

H

W55

H

W60

H

W40

V

W40B2

V

W45

V

W45C04

V

W45C08

V

W45C12

V

W45C16

V

W45C20

V

W50

V

W52C08

V

W52C12

V

W52C17

V

W52C54

V

W55

V

W60

V

W65

V

Bond

Adhesion

Slope

Energy

strength

strength

(kN/mm)

per area

test

(MPa)

(MPa)

(N/m)

(days)

63.4 40.1

97

± 2.60±1.40 1.51±1.44 3.79±0.31 3.58±0.97 2.27±0.55 1.92±1.14 2.89±1.39 1.75±1.48 1.95±0.62 3.21±0.87 1.31±0.83 8.52±0.50 4.59±1.35 0.33±0.18 5.17±0.49 4.16±0.42 4.47±1.04 4.71±0.44 5.83±0.56 4.89±0.65 4.53±0.19 6.55±0.79 4.07±1.03 5.08±0.56 4.97±0.43 5.19±1.65 10.2±1.05 7.64±0.44 4.29±0.69 7.18±0.53 3.36 0.47

± 2.54±1.42 1.42±1.47 3.62±0.19 3.54±1.00 2.17±0.64 1.89±1.18 2.85±1.47 1.02±1.15 1.95±0.62 2.78±1.17 0.97±1.02 8.52±0.50 4.58±1.34 0.24±0.16 5.17±0.49 3.34±1.08 3.74±0.80 4.22±0.75 5.82±0.56 4.89±0.66 4.51±0.17 6.44±0.80 4.04±1.04 5.07±0.56 4.57±0.52 3.01±0.83 10.2±1.05 7.31±0.91 4.24±0.62 7.14±0.52 3.36 0.47

± 162±112 29.1±25.3 117±31.7 98.9±45.7 73.3±25.8 72.6±49.6 129±101 37.9±49.2 48.0±16.7 95.2±52.5 31.6±22.1 205±54.2 181±143 6.77±8.13 131±31.2 118±52.0 74.4±33.2 74.0±69.5 213±136 113±15.9 107±54.0 196±31.2 95.3±33.6 151±69.0 123±46.5 125±39.1 212±95.6 231±102 207±154 115±26.3 96.1 14.3

90

± 44.4±56.9 41.7±42.2 75.2±52.1 133±84 36.1±14.3 39.5±20.6 66.2±58.8 35.5±62.8 46.9±14.9 57.6±46.0 14.5±20.8 240±30 95.9±64.4 70.2±153 132±81 64.4±54.8 127±101 151±102 129±41 144±37 165±96 137±49 131±48 127±27 154±58 50.7±16.9 384±124 155±92 354±367 230±139

age at

78 100 246 54 69 63 73 48 204 205 202 175 78 48 97 78 99 215 54 69 63 73 48 203 205 201 174 78 49 210

CHAPTER 6. RESULTS


Table 6.9: Correlation matrix between pullout results and some mix composition and properties, for horizontal rebars of age range of 47 to 100 days. No correlations with p-value below 0.05. Bleeding duration Powder content Bond strength -0.39 0.19 Slop -0.28 0.21 Adhesion energy -0.26 -0.15 Adhesion strength -0.29 0.23 Table 6.10: Correlation matrix between pullout results and some mix composition and properties, for horizontal rebars of age range of 174 to 246 days. Correlations with p-value below 0.05 are emphasized. Bleeding duration Powder content Bond strength 0.87 0.98 Slop 0.79 0.97 Adhesion energy 0.91 0.95 Adhesion strength 0.88 0.97 signicant correlations of the adhesion energy and strength with the bleeding duration, and for all the pullout results with the powder content (Table 6.10). Both bleeding duration and powder content are related to improve adhesion of the rebar-concrete about half a year after cast. This imply that both are relevant for time related changes at the interface. Table 6.11 shows the correlation matrix for pullout results for vertical specimens, for age range of 47 to 100 days, with some mix composition and properties. The adhesion energy, which was measured for vertical specimens at age range of 47 to 100 days, has positive correlation with total bleeding, bleeding rate, and W/C ratio. All of these are associated with low quality concrete (segregation and low strength). The correlation is only for the energy absorbed during adhesion failure and is not associated with the adhesion strength or the adhesion elasticity (which is represented as the slop). The cement content and the compressive strength are negatively correlated with the adhesion energy. This means that the energy absorbed during pullout for these specimens is grater when the concrete is weak and tend to segregate, independently of the strength of the bond between the steel and the concrete. Table 6.12 shows the correlation matrix for pullout results for vertical specimens, of age range of 174 to 246 days, with some mix composition and properties. A complete table of the correlation matrix is found in appendix . The correlations, which were observed

91

CHAPTER 6. RESULTS


Table 6.11: Correlation matrix between pullout results and some mix composition and properties, for vertical rebars of age range of 47 to 100 days. Correlations with p-value below 0.05 are emphasized. Bleeding Mix composition Compressive Duration Total Rate W/C Cement content Strength Bond strength 0.00 0.08 0.05 0.24 -0.24 0.15 Slop -0.01 0.47 0.40 0.61 -0.49 -0.33 Adhesion energy 0.48 0.93 0.87 0.84 -0.72 -0.71 Adhesion strength -0.06 0.15 0.16 0.28 -0.27 0.06 Table 6.12: Correlation matrix between pullout results and some mix composition and properties, for vertical rebars of age range of 174 to 246 days. Correlations with p-value below 0.05 are emphasized. Bleeding Mix composition Compressive Duration Total Rate W/C Cement content Strength Bond strength 0.88 -0.01 -0.02 0.33 -0.82 0.31 Slop 0.87 -0.28 -0.22 0.12 -0.74 0.47 Adhesion energy 0.92 0.01 -0.03 0.22 -0.62 0.28 Adhesion strength 0.94 0.08 0.06 0.34 -0.72 0.22 for specimens at earlier age, have disappeared. A signicant correlation of the pullout result with the bleeding duration was found, similarly to the horizontal older specimens. A negative correlation of the bond strength with the cement content was found as well. The results suggest that other correlations of the pullout results with cement content may exist, but due to the low number of specimens, these correlations are statistically insignicant. 6.4.2 Pullout correlation with ITZ microstructure

The adhesion energy of the pullout of horizontal rebars is signicantly correlated with maximum and standard deviation of the ITZ thickness, when the measurement was done at age range of 47 to 100 days (Table 6.13). The correlation with the maximum ITZ thickness is positive, that means a larger ITZ at its maximum thickness related to higher energy absorption during rebar pull out. The correlation with the ITZ thickness standard deviation is negative, which means lower energy absorption during pullout when the ITZ is highly varied. Similar correlation was not found when the measurement was done at age range of 174 to 246 days (Table 6.14). ITZ microstructure of vertical specimens and pullout results of measurements which

92

CHAPTER 6. RESULTS


Table 6.13: Correlation matrix between pullout results and ITZ thickness, for horizontal rebars of age range of 47 to 100 days. Correlations with p-value below 0.05 are emphasized. ITZ Thickness Average Maximum Standard deviation Bond strength 0.00 0.24 -0.64 Slop 0.04 0.03 -0.33 Adhesion energy 0.00 0.70 -0.72 Adhesion strength -0.05 0.25 -0.63

Table 6.14: Correlation matrix between pullout results and ITZ thickness, for horizontal rebars of age range of 174 to 246 days. No correlations with p-value below 0.05. ITZ Thickness Average Maximum Standard deviation Bond strength 0.07 0.34 -0.26 Slop 0.04 0.32 -0.22 Adhesion energy 0.10 0.38 -0.24 Adhesion strength 0.07 0.36 -0.24

were done at age range of 47 to 100 days do not signicantly correlate with each other. When the pullout test was taken at specimen age which ranged from 174 to 246 days, all the results of the pullout test were signicantly correlated with the steel-concrete distance (compare Table 6.15 with Table 6.16).

Table 6.15: Correlation matrix between pullout results and ITZ thickness, for Vertical rebars of age range of 47 to 100 days. No correlations have p-value below 0.05. Steel-concrete distance Average Maximum Standard deviation Bond strength -0.35 -0.21 -0.34 Slop -0.21 -0.22 -0.22 Adhesion energy -0.03 -0.10 -0.10 Adhesion strength -0.49 -0.27 -0.49

93

CHAPTER 6. RESULTS

6.5. CHLORIDE THRESHOLD

Table 6.16: Correlation matrix between pullout results and ITZ thickness, for Vertical rebars of age range of 174 to 246 days. All correlations have p-value below 0.05. Steel-concrete distance Average Maximum Standard deviation Bond strength 0.91 0.91 0.90 Slop 0.83 0.82 0.82 Adhesion energy 0.87 0.88 0.85 Adhesion strength 0.85 0.86 0.84 6.5 Chloride threshold

Table 6.17 presents the average chloride threshold obtained for specimens that exhibited both corrosion potential, after more than 14 in NaCl, and corrosion in the visual examination. When problematic specimens (having no visual signs of corrosion by a naked eye, or corrosion initiated after less than 14 days from immersion in NaCl solution) are not ignored, chloride content ranges from 0.34 gr Cl− per kg of concrete ( of concrete) to 8.06 of concrete. When chloride content is expressed as a fraction of the cement content, or chloride concentration in pore solution, it ranges from 0.20 % to 5.17 % of cement weight or 4.7 gr/l to 121 gr/l respectively. This range corresponds with ranges found in the literature which extends from 0.11 % to 8.39 % cement (Table 1.1 on page 23). The average chloride content for all specimens is 3.90 concrete, 2.13 % cement, 52.4 gr/l under assumption of saturated concrete, or 60.1 gr/l under assumption of unsaturated concrete. When the problematic specimens were excluded from the results, chloride threshold range becomes more narrow, and ranges from 0.56 to 7.98 concrete, from 0.28 % to 5.12 % cement, or 7.86 gr/l to 121 gr/l of pore solution (Table 6.17). The highest chloride threshold was found in the concrete mix with the highest water to cement ratio, and the average chloride threshold was: 4.16 concrete, 2.26 % cement, 56.6 gr/l under assumption of saturated concrete, or 65.0 gr/l Under assumption of unsaturated concrete. Figure 6.8 presents the chloride threshold distribution, presented either as gr/l in pore solution or of concrete weight. Examination of the chloride threshold distribution reveals more extremity results for the horizontal rebars than for the vertical rebars. The average chloride threshold for vertical rebars is 4.67 concrete which is higher than the average chloride threshold of horizontal rebars 3.65 concrete. The wide distribution of the threshold values make this dierence negligible, however, for any specic mix, the

94

CHAPTER 6.

RESULTS

6.5.

CHLORIDE THRESHOLD

threshold for the vertical rebars is higher than for the horizontal rebars.

The chloride

threshold concentration in pore solution may be as high as three times that as in sea water.

(a) as gr/l in pore solution

(b) as

Figure 6.8: Distribution of chloride threshold

95

concrete

CHAPTER 6. RESULTS


Table 6.17: Chloride threshold per mix and rebar orientation (free chloride). Mix Rebar Chloride threshold orientation concrete % cement gr/l pore gr/l pore a solution solutionb W40c H 5.80 2.59 77.1 88.7 d W40B2 H 3.91 1.75 53.2 61.4 d W45 H 0.56 0.28 7.9 9.4 W45C04c H 6.35 3.20 88.3 102.8 c W45C08 H 2.22 1.05 28.3 34.1 d W45C12 H 4.07 1.92 48.7 55.5 d W45C16 H 5.29 2.62 70.4 79.9 d W45C20 H 1.68 0.84 22.9 26.8 W52C08d H 2.88 1.62 37.1 42.8 d W52C12 H 3.73 2.12 49.6 60.2 d W52C54 H 3.64 2.53 73.9 80.0 c W55 H 2.76 1.70 37.7 43.5 d W60 H 4.62 2.94 58.2 66.5 W40c V 3.06 1.37 40.7 46.8 c W40B2 V 3.02 1.35 41.1 47.4 c W45C04 V 4.15 2.09 57.7 67.2 c W45C08 V 4.07 1.93 51.9 62.6 d W45C16 V 6.29 3.12 83.7 95.1 W45C20d V 4.44 2.22 60.6 70.9 c W50 V 3.60 1.99 47.8 52.5 c W52C08 V 4.43 2.49 57.1 65.8 c W52C12 V 5.16 2.93 68.6 83.2 W52C17c V 4.64 2.81 65.6 75.0 d W52C54 V 2.16 1.50 43.8 47.5 c W55 V 7.66 4.72 104.6 120.8 d W65 V 7.98 5.12 96.0 104.6 a Under assumption of saturated concrete b Under assumption of un saturated air bubbles c Average of two specimens d One specimen only 96

CHAPTER 6. RESULTS


Table 6.18: Parameters for epoxy plug. Time [days] R s [ W] 0.05 7.31E+03 0.19 6.17E+03 2.24 1.19E+04 5.27 1.28E+04

equivalent circuit with capacitance, for electrode in front of

Table 6.19: Parameters for cement plug. Time [days] R s [ W] 0.01 3.70E+03 0.15 4.09E+03 0.96 1.01E+04 2.10 1.77E+04 5.22 7.50E+03

equivalent circuit with capacitance, for electrode in front of

R p [ W]

3.07E+05 5.79E+05 1.28E+06 1.73E+06

R p [ W]

1.66E+05 3.29E+05 1.09E+06 1.50E+06 5.68E+05

Cdl [F]

1.11E-04 7.06E-05 6.97E-05 7.57E-05

Cdl [F]

9.34E-05 1.25E-04 8.78E-05 9.44E-05 1.15E-04

6.6 Simulation of the ITZ

Even-though the control of specied steel-concrete distance failed, the observation on the model contributed for validation of measurement, and revile a phenomenon of preferential crystal growth, which proved to be valuable for the interpretation of the ndings in Subsection 7.3.4. 6.6.1 Electrochemical analysis

Tables 6.18, 6.19, 6.20, and 6.21 present the parameters obtained for the equivalent circuit shown in Figure 5.14. Rs is resistance external to the corrosion process, Rp is the polarization resistance (which is reciprocal to the corrosion current), Cdl is the double layer capacitance, and CPE is constant phase element, which consist of two parameters A and n. Parameter A has units of Ω · secn , where −1 ≤ n ≤ 1; n = 0 for resistor; n = −1 for capacitor; and n is any unnatural value in the range -1 to 1 for CPE. The resistance polarization (Rp ) tends to increase with time, although a certain decrease was seen in the last measurement for the WE in front of the cement plug (Figure 6.9a). Since this experiment was short and did not repeat, no conclusion may be derived regarding the potential drop of the last measurement. The values obtained are in the range reported in the literature for measurement of steel embedded in concrete [111, 44, 74].

97

CHAPTER 6. RESULTS


Table 6.20: Parameters for plug. Time [days] R s [ W] 0.05 3.04E+03 0.19 3.04E+03 2.24 3.04E+03 5.27 3.04E+03

equivalent circuit with CPE, for electrode in front of epoxy R p [W]

3.79E+05 7.31E+05 1.63E+06 2.52E+06

A CPE [Ω · secn ]

1.89E+04 2.99E+04 2.66E+04 2.88E+04

n CPE

-0.79 -0.79 -0.85 -0.82

Table 6.21: Parameters for equivalent circuit with CPE, for electrode in front of cement plug. Time [days] R s [ W] R p [W] ACPE [Ω · secn ] n CPE 0.01 1.12E+03 1.93E+05 1.87E+04 -0.81 0.15 3.80E-09 4.54E+05 1.75E+04 -0.78 0.96 1.58E+02 1.59E+06 2.69E+04 -0.79 2.10 1.42E+03 2.82E+06 3.21E+04 -0.75 5.22 2.32E-11 8.59E+05 2.41E+04 -0.74 Figure 6.9b shows the relationship between the resistance polarization and the electrochemical potential. The electrochemical potential of the WE located in front of the epoxy plug seems to be correlated with the resistance polarization, whereas the WE in front of the cement plug does not. This indicate that the measurement of electrochemical potential alone is not a foolproof indicator for active corrosion, so the presence of active corrosion should be veried by other method. 6.6.2 Surface analysis

Some visible deposits were observed on the electrodes placed against the cement plug, which was also covered with deposits. A distinct border was observed between the deposits on the part of the cement plug in front of the steel electrode and those on the other parts of the plug. The deposits on the steel surface, which was located in front of the cement plug, were discrete rhombic crystals spread quite evenly in a random pattern (Figures 6.10 and 6.11). The deposits on the carbon electrode and epoxy parts, which were located in front of the cement plug, were composed of long, prism-shaped crystals, often aggregated in dense clusters that were often long and thin (Figure 6.12). Analysis of both deposits reveals that the crystals were made of calcium carbonate. The dierent geometry of the crystals suggests that the crystals on the steel surface were calcite, whereas those on the 98

CHAPTER 6.

RESULTS

6.6.

(a) Change of resistance polarization over time

SIMULATION OF THE ITZ

(b) Potential versus resistance polarization

Figure 6.9: Electrochemical behavior of WE

carbon and epoxy surfaces were aragonite.

99

CHAPTER 6.

RESULTS

6.6.


Figure 6.10: Deposits on the steel surface located in front of the cement plug. 6.11 is a close-up of one crystal

100

Figure

CHAPTER 6.

RESULTS

6.6.


Figure 6.11: Single crystals of deposits on the steel surface located in front of the cement plug.

101

CHAPTER 6.

RESULTS

6.6.


Figure 6.12: Deposits on the carbon surface located in front of the cement plug.

102

CHAPTER 6.

RESULTS

6.6.


Figure 6.13: Steel electrode in front of epoxy plug at the end of the experiment

103

CHAPTER 6. RESULTS


Figure 6.14: Carbon electrode located in front of the epoxy plug, at end of experiment

No deposits were found on either the steel (Figure 6.13) or the carbon (Figure 6.14) parts of the electrode located in front of the epoxy plug. Two types of deposits were observed on the cement plug. (i) Deposits on the area in front of the steel electrode were composed of large crystals that looked like they were emerging from the cement paste (Figures 6.15 and 6.16). Table 6.22 presents the composition of these crystals, according to EDS. (ii) Crystals deposited on the cement plug in front of the epoxy and carbon electrode that had the same composition and a similar geometry as the crystals deposited on the epoxy and carbon electrode, but were spread evenly over the surface with no clustering (Figure 6.17). The transition from the

104

CHAPTER 6. RESULTS


region in front of the steel electrode to the area in front of the epoxy electrode is very sharp (Figure 6.18).

This experimental arrangement was found to be inappropriate for a study of the inuence of the distance-chloride threshold. Even-though, the results spot the inuence of the steel on the participation of dierent minerals in its proximity.

Table 6.22: Composition of crystal deposited on cement plug in front of the steel electrode, by EDS. Element Weight % Atomic % C 8.24 13.18 O 57.20 68.69 Al 6.88 4.90 Si 0.82 0.56 S 0.21 0.13 Ca 24.94 11.96 Fe 1.70 0.58

105

CHAPTER 6.

RESULTS

6.6.


Figure 6.15: The cement plug in front of the steel electrode. Figure 6.16 shows a close-up view.

106

CHAPTER 6.

RESULTS

6.6.


Figure 6.16: The cement plug in front of the steel electrode. A zoom into Figure 6.15.

107

CHAPTER 6.

RESULTS

6.6.


Figure 6.17: The cement plug in front of the epoxy and carbon electrode

108

CHAPTER 6. RESULTS

6.7. CORROSION VALIDATION

Figure 6.18: The transition line between the part of the cement plug in front of the epoxy (upper) and the part in front of the steel electrode (lower).

6.7 Corrosion validation 6.7.1 EIS measurement of specimens vs. electrochemical potential

EIS spectra were parameterized according to the respective equivalent circuit that appears in Table 6.23 and Figure 5.14b.

The relationship between the electrochemical

potential and the resistance polarization, derived from EIS, can be seen in Figure 6.19. The measurements are clearly divided into two distinct groups. The right-hand group (B) refers to specimens subjected to wetting and drying cycles of water without sodium chloride, or the internal reference electrode, located farther away from the chloride source.

109

CHAPTER 6. RESULTS


Table 6.23: Parameters of equivalent circuit for measurements in concrete specimens and potential versus SCE

R s [ W]

R p [ W]

ACPE [Ω · secn ]

nCPE

E [mV]

138

1.33E+03

159

-0.621

-358

173

5.67E+03

151

-0.758

-283

100

6.76E+03

356

-0.774

-303

79

6.96E+03

200

-0.789

-289

186

1.36E+04

248

-0.762

-265

178

1.42E+04

229

-0.777

-258

196

1.42E+04

230

-0.766

-271

170

1.63E+04

346

-0.742

-240

555

1.75E+04

165

-0.740

-209

115

1.71E+05

207

-0.759

-129

150

3.48E+05

373

-0.752

-167

706

3.82E+05

312

-0.821

-90

226

5.56E+05

311

-0.813

-119

145

1.17E+13

92

-0.734

-182

183

9.71E+13

94

-0.743

-165

835

4.64E+15

643

-0.734

-96

572

1.61E+16

113

-0.578

-94

(a)

Figure 6.19:

Rp

(b)

versus potential in concrete specimens. (a) Two groups of results. (b)

Zoom into group A.

110

CHAPTER 6. RESULTS


The left hand group (A) refers to all the rebars located close to the chloride source. It was assumed that the points in group A indeed represent the experimental environment, thus the use of potential measurement as indication to corrosion initiation is valid. Plotting electrochemical potential versus Rp shows two distinct groups. One group (on the right) demonstrates a much higher Rp than the other, for equal potential. When the specimens belong to the right-hand group (B in Figure 6.19a), even a relatively low potential may not correspond to active corrosion. In the case presented here, measurements were taken on rebars where no corrosion was expected, and where the potential range is limited. These results do, however, indicate that the use of potential alone can not be generalized for the indication of corrosion of embedded reinforcing steel, unless additional information is known, as in this study. The model used to interpret EIS measurements is a simplied one, and does not represent the real equivalent circuit. As long as Rp is low, the model ts the impedance spectra quite well, at least for the low frequencies (Figures 6.20a and 6.20c) ; however, as can be seen, the high frequencies are ill-tted. Additional time domains (imaginary impedance) appear when Rp is very high, like is the case for the spectra shown in Figures 6.20e and 6.20f. These time domains may be a result of the dierent nature of the rebar surface but they may also exist in all of the measurements but remain unseen. If these time domains are unseen when the Rp is low (as can be seen in Figure 6.20a and 6.20c), then this means that a parallel high frequency branch exists in the circuit. It is dicult to t the spectra to other, more complicated circuits that have additional impedance components. The observable small deviations of the tted graph from the experimental results minimize the gradient of error along the axis of the added components. The algorithm that ts parameters to the circuit nds a local optimum (where the error is minimal), but may miss the global optimum, where the parameters are best tted [108]. This prevents us from obtaining a better t to more complex equivalent circuits. Furthermore, the equivalent circuits that were used use a constant phase element (CPE) that is not directly related to any electrochemical process. No reasonable tting can be achieved without the use of CPE. This result is not, however, unique to this work, but appears in other electrochemical investigations as well [17]. Even though a circuit with capacitor instead of CPE does not t the data well, the calculated Rp of such circuit is similar to that calculated for a circuit with CPE, and the same trends are observed. Furthermore, the low variation of the CPE properties in the investigated spectra. both for concrete as well as the simulation (Tables 6.23 and 6.21 on pages 110 and 98, respectively) reduces the inuence of this element, whose physical meaning is 111

CHAPTER 6. RESULTS


(a)

(b)

(c)

(d)

(e)

(f)

Figure 6.20: Equivalent circuits that t the EIS spectra. (a) Niquiest of specimen with low Rp . (b) Bode of (a). (c) Niquiest of specimen with medium Rp . (d) Bode of (c). (e) Niquiest of specimen with high Rp . (f) Bode of (e).

112

CHAPTER 6. RESULTS


unknown, on the results, and enhances the validity of measuring Rp through equivalent circuit parameterization. 6.7.2 Visual examination of specimens

Visual examination of the rebars from the corrosion specimens validated the presence of corrosion and hinted as to the processes involving the concrete-rebar system. The observed results were corrosion (Figures 6.21a, 6.21b, 6.21d, 6.21e, and 6.21f), lack of corrosion (Figure 6.21c), and the presence of white, worm-like deposits (Figure 6.21f). The corroded rebars were classied into localized corrosion (Figure 6.21b, 6.21d, and 6.21e), overall corrosion (Figure 6.21a and 6.21f), aerobic corrosion (red deposit, Figures 6.21a, 6.21b, and 6.21f), and suspected as anaerobic corrosion (black-green deposit, Figure 6.21d, and 6.21e). Specimens not exhibiting corrosion were eliminated from the chloride threshold analysis. On all the horizontal specimens aerobic corrosion was observed, 76 % of which was general corrosion (and 24 % localized). The general corrosion was at the bottom of steel surface (regarding the orientation during cast), and for minority of specimens, in addition to the bottom, it was observed partially on other parts of the rebar. On 17 % of the vertical rebars no corrosion signs could be observed. On 23 % of the rebar the corrosion covered continuous large surface (several square centimeters), which is regarded as general corrosion. On the remain 60% of rebars localized corrosion was observed. Orange corrosion products of localized corrosion could be found in millimeter scale voids, several millimeters away from the rebar, while the corroding spot could be less than millimeter wide. About 28 % of the incidents of specimens with localized corrosion was of black greenish corrosion product.

113

CHAPTER 6. RESULTS


(a)

(b)

(c)

(d)

(e)

(f)

Figure 6.21: Rebars from the corrosion experiment. (a) Overall corrosion. (b) Localized corrosion. (c) No visable corrosion. (d) Unaerobic corrosion. (e) Unaerobic corrosion. (f) Worm like deposites

114

7 Discussion In this chapter, we rst discuss issues related to the characterization and creation of the ITZ. Characterization of the ITZ is essential for the main subject of this work: the ITZ-chloride threshold relationship. Controlling ITZ properties, and particularly those concerning the ITZ-chloride threshold relationship, can be used to improve concrete mixes for better durability. Electrochemical methods used to determine the threshold are discussed next. Although these methods seem reliable, they are not always accurate and results are not always similar to those results of others in the literature. Next, we discuss the chloride threshold, its relationship with the ITZ, the inuence of the ITZ on the chloride threshold, and what this relationship tells us about the ITZ's structure and chemistry. Finally, some unresolved questions will be discussed. 7.1 Relationship between the ITZ and mix properties

The ITZ around horizontal and vertical rebars was found to be inuenced by several dierent parameters. 7.1.1 ITZ around horizontal rebars 7.1.1.1 ITZ thickness

The thickness of the ITZ around horizontal rebars cannot be related to any fresh mix property or composition in the range (Table 7.1). The standard deviation of the ITZ thickness around horizontal rebars however is, inuenced by the size of the large void below the rebar. It increases with the increase in water content, and water-to-powder ratio (eect of mix composition), and slump ,total bleeding, and bleeding rate (eect of fresh mix properties). Relationships exist between water content, slump and total bleeding and between water-to-powder ratio and bleeding rate (Table 6.1 on page 73). Thus, it can be summarized that two independent segregation factors (total bleeding and bleeding rate) inuence the standard deviation of the ITZ thickness around horizontal 115

CHAPTER 7. DISCUSSION

7.1. ITZ VS. MIX PROPERTIES

rebars. The large void beneath the horizontal rebar (Figure 5.7b on page 50) is the main factor that determines the average ITZ thickness around horizontal rebars, and the cause for variation in the thickness around the rebar. This void is clearly caused by bleeding water beneath the rebar, which is a result of segregation that produces a denser microstructure above the rebar and a lighter micro-structure below it [112]. The quantity of the water in the mix seems to have some eect on the variation of the ITZ thickness (Table 7.1). The variation of the ITZ thickness around the rebar is important because not only it indicates the thickness in the specic section where the images were taken, but also alludes to the probable maximum thickness along the entire rebar. The standard deviation of ITZ thickness increases due to bleeding. The bleeding rate and the bled water volume depends on the water-to-powder ratio of the mix itself, on the water content, and the slump. The correlation of the ITZ thickness at its widest region (the big void below the rebar, maximum in Table 7.1) with mix content and fresh concrete properties, is too weak to derive any concrete conclusion about the void's formation mechanism. Thus, the void can be the result of a mechanism which is proposed hereafter, and its verication is out of the scope of this work. According to this mechanism, water accumulates beneath the rebar, until it breaks through the side of the rebar and continues up toward the surface, thus emptying the water lens and reducing its thickness. The "breaking through" pressure depend on the rheological properties of the concrete surrounding the rebar. Since the concrete becomes thicker with time, this cycle stops at a certain point of time. Thus freezing the water lens in its last position. (Figure 7.1). Therefore, more bleeding water or high rate of bleeding may enhance this phenomenon, leading to more variation in the nal thickness, expressed by higher values of standard deviation. This mechanism explains also the dependency of maximum value of ITZ thickness with the water content, or bleeding values, while the average value remain unaected. 7.1.1.2 Steel-concrete distance

No clear conclusion can be drawn as to which parameters inuence the steel-concrete distances for horizontal rebars. The small distances found seem to be the result of ne concrete ingredients that adhere to the rebar surface or of minerals that precipitate on the steel surface. Each of these mechanisms creates a dierent chemical environment on the steel surface that may aect the corrosion initiation. Preferential deposition was found after splitting the concrete specimens from the corrosion test (Figure 6.21 on page 114). Deposits and preferential deposition were found even on the steel in the model (Figure 6.10 on page 100). Deposits of carbonate on metals are known to precipitate on 116


7.1. ITZ VS. MIX PROPERTIES

Figure 7.1: Changes of ITZ thickness below horizontal rebar oxygen reduction sites and have an inhibitive impact on oxygen reduction [29]. Hence, these observed deposits may play some role in the corrosion process. The factors that may cause such precipitation or adhesion of ne particles are beyond the scope of this study. They may be the result of a combination of several variables such as slump, bleeding and vibration during consolidation, and such combinations can not be found using the relatively small number of mixes produced in this work. Furthermore, the image analysis in this work did not distinguish between deposits and adhered particles. It should be noted that the chemical environment is expected to be dierent for each type of solid found on the steel surface. Hence, the inability to dierentiate between them adds more noise to the results of the corrosion and chloride threshold measurements. 7.1.2 ITZ around vertical rebars

The images of the ITZ around vertical rebar present a completely dierent case. In this case, it is clear that the steel-concrete distances are inuenced by water content, slump and bleeding duration (Table 7.2). Higher water content reduces the steel-concrete distance as do higher slump values. Since the slumps of the mixes in this work were correlated with water content (Table 6.1 on page 73), the correlation between the slump and steel-concrete distance may be the result of its correlation with water content, or vice verse. Thus, we can deduce that the ITZ around vertical rebars is highly inuenced by the consolidation of the concrete, which not only is inuenced by the rheological properties of the concrete, but also by the vibration and ow of concrete during casting. Since the consolidation procedure was not a controlled parameter in this work, it may have caused an uncontrolled variation in the results. The bleeding duration is correlated with all of the steel-concrete distance statistics, and is uncorrelated with water content 117

horizontal

casts.

Correlations with p-value below 0.05 are

Duration

Bleeding

Rate

Slump Total

W/P

Powders

W/C

Cement

Mix content

Table 7.1: Correlation between the ITZ and the mix properties, emphasized.

Water

-0.01

Property

-0.06

0.13

-0.26

-0.30

0.19

0.06

-0.62

0.36

-0.29 0.34

0.17

-0.63

0.37

0.13 0.23

0.24

-0.25

0.32

-0.42

0.46

0.25

-0.31

0.20

0.00

-0.42

0.02

0.22

-0.08

-0.34

0.17

-0.17

0.55

0.19

-0.38

0.37

-0.17

0.65

0.40

0.00

-0.10

0.61

0.14

0.20

-0.20

-0.21

0.53

0.35

0.05

0.26

0.09 0.42

-0.37

-0.08

-0.50

0.40

0.20

-0.27

Average Standard deviation

0.11

0.08

Maximum

Average

Maximum

-0.01

porosity

ITZ

-0.12

0.67

Maximum

thickness

Over all maximum

Standard deviation

concrete

Average standard deviation

Average

Averaged maximum

-0.19

-0.25

-0.17

-0.04

-0.09

-0.01

0.19

0.11

0.22

-0.10

-0.07

-0.12

-0.24

-0.20

-0.26

-0.13

-0.18

-0.13

-0.21

-0.20

-0.25

-0.20

-0.18

-0.23

-0.05

-0.12

-0.04

Steel-

distance

118

ITZ VS. MIX PROPERTIES 7.1. DISCUSSION CHAPTER 7.


7.2. PULLOUT VS. MIX & ITZ PROPERTIES

and slump. This may allude to the fact that bleeding duration (which is associated with the water-to-powder ratio) plays a role in the formation of the ITZ around vertical rebars. Such a mechanism may act after the concrete is consolidated and so may be unaected by consolidation and human operator parameters.

7.2 Relationships between pullout, mix properties and ITZ properties

The relationships between the pullout, mix properties and ITZ properties, which were found are dierent for specimens tested at age of less than 100 days and for these tested at age of more than 174 days. The small number of mixes at every age range (10 mixes were measured at age of less than 100 days, and 6 mixes were measured at age of more than 174 days) made the results susceptible to variation. Since it was impossible to distinguish the results that dier from the other based on a physical explanation, it was decided to ignore all the results from the pullout test

7.3 Chloride threshold

In order to evaluate the inuence of one parameter on the chloride threshold, only that parameter should be changed while others remain as constant as possible. Unfortunately, it is impossible to change one parameter of concrete mix without changing other parameters as well. Furthermore, we were not yet able to establish control over the independent parameter in this work, namely the ITZ around steel rebar. As a result, several independent variables changed simultaneously. This, on one hand, interfered with the results obtained for the analysis of the ITZ-chloride threshold relationship but, on the other hand, enabled to investigate the other variables and identify the one with the greatest inuence on the chloride threshold. To enable unbiased interpretation of the data, the ANOVA statistical method was applied. This method allows to dene the variable that is related to the chloride threshold. 119

vertical casts.

Correlations with p-value below 0.05 are

-0.22

0.00

Water

-0.03

0.26

-0.01

0.22

Cement

0.03

0.06

0.01

-0.05

-0.19

-0.10

Powders

0.07

-0.11

0.08

0.00

-0.23

-0.11

-0.19

-0.42

-0.06

-0.06

-0.06

0.00

0.01

0.03

-0.63

0.15

-0.19

0.22

0.37

0.03

0.39

-0.24

-0.05

0.02

0.02

-0.10

-0.14

-0.06

Total

-0.16

0.06

0.11

0.25

-0.02

-0.06

0.11

Rate

0.68

0.06

0.14

0.18

0.25

0.16

0.13

Duration

Bleeding

0.00

-0.10

0.28

Slump

Average

Maximum

-0.03

0.09

W/P

Standard deviation

-0.05

-0.38

W/C

porosity

Maximum

Average

Maximum

0.01

Mix content

Table 7.2: Correlation between the ITZ and the mix properties, emphasized.

ITZ

-0.71

Property

thickness

Over all maximum

Standard deviation

concrete

Average standard deviation

Average

Averaged maximum

-0.60

-0.60

-0.44

-0.09

-0.03

0.06

0.33

0.31

0.36

-0.16

-0.22

-0.24

-0.46

-0.45

-0.43

-0.43

-0.34

-0.33

-0.32

-0.36

-0.29

-0.29

-0.33

-0.31

0.62

0.58

0.53

Steel-

distance

120

CHLORIDE THRESHOLD 7.3. DISCUSSION CHAPTER 7.



Figure 7.2: Schematic presentation of the chloride threshold correlations with ITZ and mix variables for both rebar orientations

Fresh Ingredients concrete

ITZ properties

Table 7.3: Correlation coecients of the chloride threshold concentration with concrete and ITZ properties, for rebars in both rebars orientations. Correlations with p-value below 0.05 are emphasized. Chloride content Concrete property % gr/l pore Concrete Cement solution a Maximum Porosity -0.41 -0.45 -0.42 SteelMaximum -0.61 -0.58 -0.60 concrete Average -0.58 -0.53 -0.56 distance Standard deviation -0.59 -0.54 -0.56 ITZ Average -0.47 -0.44 -0.47 thickness Maximum -0.47 -0.37 -0.47 Standard deviation -0.42 -0.45 -0.47 Water 0.33 0.23 0.08 Mix Cement -0.16 -0.42 -0.28 content W/C ratio 0.35 0.59 0.36 Powders -0.32 -0.47 -0.24 W/P ratio 0.42 0.53 0.27 Slump 0.06 0.18 0.13 Total 0.47 0.60 0.34 Bleeding Rate 0.42 0.56 -0.31 a

Average value

121



Figure 7.3: Schematic presentation of the chloride threshold correlations with ITZ and mix variables for horizontal rebar


ITZ properties

Table 7.4: Correlation coecients of the chloride concentration with concrete and ITZ properties, for horizontal rebars. Correlations with p-value below 0.07a are emphasized. Chloride content Concrete property % gr/l pore Concrete Cement solution b Maximum Porosity -0.26 -0.34 -0.32 SteelMaximum -0.55 -0.56 -0.54 concrete Average -0.55 -0.55 -0.52 distance Standard deviation -0.54 -0.55 -0.52 ITZ Average -0.62 -0.65 -0.60 thickness Maximum -0.57 -0.44 -0.57 Standard deviation -0.37 -0.52 -0.57 Water 0.00 -0.23 -0.30 Mix Cement 0.20 -0.12 -0.02 content W/C ratio -0.22 0.05 -0.10 Powders 0.17 0.08 0.21 W/P ratio -0.17 -0.17 -0.31 Slump -0.18 0.08 0.10 Total -0.24 -0.17 -0.35 Bleeding Rate -0.10 0.00 -0.16 a 0.07 instead of b Average value

0.05, due to smaller number of data

122



Table 7.5: Correlation coecients of the chloride concentration with concrete and ITZ properties, for vertical rebars. Correlations with p-value below 0.05 are emphasized. Chloride content Concrete property % gr/l pore Concrete Cement solution a Maximum Porosity -0.37 -0.34 -0.40 SteelMaximum -0.67 -0.58 -0.61 concrete Average -0.71 -0.59 -0.61 distance Standard deviation -0.67 -0.54 -0.57 ITZ Average -0.22 -0.11 -0.25 thickness Maximum -0.18 -0.08 -0.17 Standard deviation -0.26 -0.21 -0.31 Water 0.63 0.52 0.45 Mix Cement -0.38 -0.57 -0.49 content W/C ratio 0.67 0.82 0.69 Powders -0.62 -0.73 -0.58 W/P ratio 0.77 0.82 0.68 Slump -0.17 0.00 -0.07 Total 0.76 0.82 0.67 Bleeding Rate 0.63 0.71 0.54 Fresh concrete

Ingredients

ITZ properties

a

Average value

Table 7.3 presents the correlation coecients of the chloride threshold and ITZ properties, mix composition, and mix properties for both rebar orientations, the correlations are also presented schematically in Figure 7.2. Tables 7.4 and 7.5 and Figures 7.3 and 7.4 present the same data for horizontal and vertical orientations, respectively. The chloride threshold is presented in three dierent ways, as described in Sub-section 5.7.1 on page 56. The correlations among the parameters representing the steel-concrete distance and the chloride threshold are signicant and valid for all rebar orientations and chloride representation methods. The correlations presented in these tables will be discussed in Subsections 7.3.2 to 7.3.4. 123

CHAPTER 7.

DISCUSSION

7.3.

CHLORIDE THRESHOLD

Figure 7.4: Schematic presentation of the chloride threshold correlations with ITZ and mix variables for

vertical rebar

Figure 7.5: Chloride threshold versus steel-concrete distance

124



7.3.1 The presentation of the chloride threshold

Theory, references, and practice emphasize several parameters as being the most important in inuencing the chloride threshold (as reviewed in Chapter 1.2.4.4 on page 19)1 . The rst variable to be discussed here is cement content. For both rebar orientations, the cement content, which is considered to be an important parameter for the determination of the chloride threshold [6, 7], has no statistical relationship with the chloride threshold (Tables 7.3, 7.4, and 7.5, on pages 121 to 123), except for the case of horizontal bars and threshold values expressed as% of cement (correlation coecient of -0.57). However, it should be noticed that if the chloride content for the threshold is assumed to be constant for a certain concrete volume or weight, the correlation between the chloride threshold (expressed as % cement) with the cement content might be an artifact caused only by the measuring unit itself; i.e. increasing the cement content at a constant chloride content per unit weight of concrete reduces the chloride content when the latter is expressed as % of cement. Moreover, the negative values of the correlation coecients indicate that if the cement content has any inuence on the chloride threshold, it is negative, i.e. increasing the cement content lowers the chloride threshold. The discussion above raises the question of how the chloride threshold should be presented. There are four options: i) as a weight fraction of cement; ii) as the Cl− /OH − ratio; iii) as the chloride concentration in pore solution (in g/l or molarity); or iv) as a weight fraction of concrete mass. The use of the % cement units is common in the literature [15, 18, 56, 57, 63]; however this unit is problematic since it is not related to the pore solution chemistry. The pore solution is in equilibrium with the hydrated cement solids, hence it is saturated relative to the solids. Consequently, the cement content should not inuence the pore solution chemistry, and it is irrelevant for corrosion chemistry. The use of this unit to express the chloride threshold may create artifacts whose relationship with other parameters, such as the cement content, stems from the measuring unit itself, as mentioned above. Furthermore, the cement content is unknown for existing structures or cannot be measured accurately, and speculation as to its value introduces additional error into the calculated value. Cl− /OH − ratio is another common way of expressing the chloride threshold value in the literature [15, 18, 54, 59]. This way of presentation is a result of an assumption 1 These can be summarized as follow: [7, 6] claim the most important parameter is cement content; [53, 52] claim it to be the void in the concrete-steel interface; the localized corrosion model presented in [32] indicates that the buer pH and its distance from the metal are the most important parameters for active corrosion initiation.

125



whereby as the pH increases, the chloride concentration needed for corrosion initiation will increase respectively [7]. In practice, the range of Cl− /OH − presented in [7] as threshold is very high, and ranges from 0.22 to 40. High threshold ranges are found for any method of chloride threshold presentation, but since the hydroxide concentration changes on a logarithmic scale, small changes in pH mean signicant changes in chloride concentration for the same Cl− /OH − ratio. Since the solubility of sodium chloride is about 6 M at pH of 13.6, any ratio above 15 is practically impossible. So the assumption of a constant Cl− /OH − ratio threshold value, when all parameters except pH are xed, cannot be valid. Only two papers expressed the chloride threshold using chloride concentration in solution as found in Glass and Buenfeld [7] review. Although from a chemical perspective it make sense to transform chloride concentration from of concrete units (as measured) to concentration in the pore solution, this also introduces an error resulting from the determination of water content in the mature concrete. Concrete water content may be determined by measuring the porosity assuming that the pore are fully saturated, or directly by drying the concrete and measuring the weight dierence. The measurement error of porosity and the deviation of the water content from the assumed value, or deviation of the concrete water content at corrosion initiation from the water content at sampling, increase the measurement error of chloride concentration.

The use of Concrete units to represent the threshold is preferable, not only due to the above consideration, but since it yields better relationships with other parameters investigated in this work (Tables 7.3 to 7.5 on pages 121 to 123). This method of presentation has an additional advantage of being an engineering unit that can be easily adopted for eld surveys.

7.3.2 Inuence of steel-concrete distance on the chloride threshold The correlation between the chloride threshold and the statistical parameters of the steelconcrete distance is independent of rebar orientation, and is the only correlation for which rebar orientation is insignicant (Tables 7.3,7.4,7.5, and Figure 7.5), i.e. the threshold become larger when the steel-concrete distance decreases. This implies a clear relation between the steel-concrete distance and the initiation mechanism of chloride induced corrosion. 126



(a) Chloride threshold versus maximum ITZ thick-

(b) Residue of steel-concrete distance regression

ness

with chloride threshold versus maximum ITZ thickness of horizontal rebars

Figure 7.6: Relationship between chloride threshold and ITZ thickness

7.3.3 Inuence of ITZ thickness on the chloride threshold In addition to the correlation with the steel-concrete distance, the horizontal rebars exhibits correlation with the ITZ thickness, especially with its maximum value (Table 7.4 and Figure 7.6a). The ITZ thickness and the average steel-concrete distance are not correlated with each other (correlation coecient value of 0.19, see Table 6.4, and p-value of about 3.6 · 10−6 , which means close to 100 % probability of being un related). Hence, the correlation with the ITZ thickness is a stand alone correlation, not a result of co-linearity of ITZ thickness and steel-concrete distance. Assuming linear relation between the chloride threshold and steel-concrete distance, the eect of the steel-concrete distance on the chloride threshold may be nulled by calculating the chloride threshold for every steel-concrete distance (assuming linear relationship) and subtracting this value from the measured chloride threshold. The dierence between the measured chloride threshold and the estimated chloride threshold calculated for the same steel-concrete distance is dened as the error of estimation (EE) of the chloride threshold by the steel-concrete distance. Figure 7.6b shows the EE of chloride threshold by the steel-concrete distance, versus the maximum ITZ thickness of horizontal rebars. Table 7.6 shows the correlation coecient of the chloride threshold EE with some variables. The EE of chloride threshold nulled by the steel-concrete distance, is correlated with the maximal ITZ thickness of horizontal rebars (Figure 7.6b), having a correlation coecient of -0.68. Same calculation for vertical rebars yields a correlation 127



Table 7.6: Correlation coecient of the chloride threshold error of estimation (EE) with some variables. Correlations with p-value below 0.05 are emphasized. variables Horizontal Vertical Averaged max Porosity 0.09 -0.49 Steel-concrete Maximum 0.11 -0.08 distance Averaged standard deviation 0.04 -0.02 ITZ Average -0.27 -0.29 Thickness Maximum -0.68 -0.22 Averaged standard deviation -0.15 -0.32 Water -0.07 0.23 Cement 0.16 -0.64 Mix composition W/C ratio -0.25 0.75 Powders 0.32 -0.38 W/P ratio -0.33 0.48 Fresh Slump -0.08 0.30 concrete Bleeding Total -0.30 0.63 properties Rate -0.22 0.55

coecient of -0.22 only. Other variables do not correlate with the chloride threshold EE calculated for horizontal rebars, but some variables correlate with the EE calculated for vertical rebars. The cement content is negatively correlated with the chloride threshold EE, which is calculated for vertical rebars. The W/C ratio and total bleeding are positively correlated with the EE for vertical rebars. If a correlation is identied for one orientation only, it could not be related directly to the corrosion mechanism, because the rebar orientation at casting cannot inuence that mechanism (all specimens were tested with their rebars in horizontal position during the corrosion test). The above mentioned correlations of cement content, W/C ratio, and total bleeding, with the chloride threshold of vertical rebars, can be tracked back by the correlations already found in Tables 7.2, 7.5, which visually presented in Figure 8.2. The cement content is negatively correlated with the bleeding rate and total bleeding, which are correlated with the chloride threshold. Hence, it is expected to be negatively correlated with the chloride threshold. No correlation was found between bleeding rate or total bleeding and ITZ properties of vertical rebars (Table 7.2). Thus, another mechanism, not related to the properties of ITZ studied here, is probably responsible to this observation. The W/C ratio indirectly correlates with the chloride threshold through its correlation with the steel concrete distance.

128



Table 7.8 summarizes the statistical analysis of the results of this work, which are presented in the former sections (Tables 7.3, 7.4, 7.5, and Figure 7.5 on pages 121 to 124). The correlation between the parameters representing the steel-concrete distance and the chloride threshold was found to be independent of the rebar orientation. The correlation between the steel-concrete distance and the chloride threshold is moderate. Examination of the standard deviation of the steel-concrete distance reveals it to be a very scattered phenomenon (see the dierence between the maximum and average in Table 6.5 on page 87), which can explain the relatively low correlation values observed.

7.3.3.1 Curve tting of the chloride threshold-ITZ relationship

The relationship between the chloride threshold, Clth , and ITZ properties may be approximated as linear (Equation 7.1). (7.1)

Clth = a · x + b

where Clth is the chloride threshold ( concrete), and x is the observable ITZ property, i.e. steel-concrete distance. Steel-concrete distance is the ITZ property used to calculate the chloride threshold. In the case of horizontal rebars, a correction factor based on the maximum ITZ thickness can be used as well, in addition to calculating the chloride threshold using the steel-concrete distance. (7.2)

Clth(corrected) = Clth + a · x + b

where Clth(corrected) is the chloride threshold calculated with correction ( concrete); x is maximal ITZ thickness in this case which is used as correction factor; and b is the corresponding b which was calculated using the error of estimation (EE). The corresponding constants (Equations 7.1 and 7.2) and corresponding standard errors of estimation (SEE) for chloride threshold estimation are given in Table 7.7: 129



Table 7.7: Parameters and SEE for calculating the chloride threshold using the ITZ characteristics Rebar x a b SEE SEE orientation (mm) ( concrete ( ( (% of /mm) concrete) concrete) range)

Horizontal and vertical Horizontal

Correction factor Vertical

Average steel-concrete distance Average steel-concrete distance Maximal ITZ thickness Average steel-concrete distance

0.152

6.09

1.43

19

1.54

6.98

1.32

18

0.0199

7.33

0.97

13

0.308

7.52

1.35

18

The chloride threshold for vertical rebars is correlated with many mix properties (Table 7.5): water and powder content, water-to-cement and water-to-powder ratios, total bleeding, bleeding rate. All of these properties are co-linear (as can be inferred from the high correlation coecients presented in Table 6.1). These properties are correlated with the maximum steel-concrete distance as well (a correlation coecient of -0.61). Hence, a better estimation of the chloride threshold for vertical rebars may be calculated from mix composition. No signicant correlation was found between pullout or compressive strengths and the chloride threshold.

7.3.4 Inuence of ITZ on chloride threshold: integration of model and experimental results According to [32], the distance of the metal from a buer should have a strong eect on the corrosion mechanism. Voids in concrete at the steel-concrete interface are, in the micro scale, a bare metal surface in touch with aqueous solution (pore solution) and buering solids (concrete solids). This structure is analogues to simplied pit in metal (Figure 7.7). The simplied pit that may corrode only at its base, is lled with aqueous solution, and the chemical 130


Table 7.8: Summery of factors which inuence the Chloride threshold Rebar orientation H V Maximum Porosity

SteelMaximum ⇓ ⇓ concrete Average ⇓ ⇓ distance Standard deviation ⇓ ⇓ ITZ Average ⇓

thickness Maximum ⇓

Standard deviation

Water

⇑ Mix Cement

content W/C ratio

⇑ Powders

⇓ W/P ratio

⇑ Slump

Total

⇑ Bleeding Rate

⇑


ITZ properties


⇑ Tend to increase the chloride threshold

⇓ Tend to decrease the chloride threshold

No statistical signicant inuence on the chloride threshold

environment at its opening is xed (Figure 1.1). These conditions create concentration polarization which may lead to active corrosion at the pit base (see Subsection 1.2.3 and [32]). The chloride concentration is related to the corrosion rate at the passive state. Chloride ions reduce the electrical resistance of oxide layer, known as the passive layer [17]. Reduction of the electrical resistivity of this layer cause higher corrosion rate. When the corrosion rate inside a void at the steel-concrete interface meet a critical rate, which depend on the specic geometry, active corrosion is initiated. Figures 7.8 and 7.9 and Table 7.9 show the critical product of pit depth and current density (which is dened as the point at which pH drops below 10 due to depassivation of iron [12]) according to the model for concentration polarization proposed by [32]. The critical product of pit depth and current density changes by about one order of magnitude for every pH unit from pH of 10 upward, to a maximum of about 10−5 A/m for pH of 13, which is the value expected for hydrated cement paste [113]. Fixing the pit depth to 10 mm, which is the order of magnitude of the average steelconcrete distance expected from Table 6.5 on page 87, and calculating the in-pit current density needed to create concentration polarization (critical current density), yields a

131

CHAPTER 7.

DISCUSSION

7.3.

CHLORIDE THRESHOLD

Figure 7.7: Void at the steel-concrete interface as analogue to pit

Figure 7.8: pH as a function of the product of pit depth and current density

132



Figure 7.9: Critical product of pit depth and current density as a function of external pH value result of 1 A/m2 . Taking into account that the maximal distance at which corrosion is expected to initiate may be ten times the average distance due to the high variation, this current density may be as low as 0.1 A/m2 . In addition, deviation of the pH from the assumed pH, for example from 13.0 to 12.0, reduces the critical current density by 90 %. Figure 7.9 shows the critical depth-current density product as a function of the buered environment at the pit opening. It can be seen that for pH values over 10.5, the relationship is logarithmic. It may be summarized that the steel-concrete distance has a linear inuence on the critical current density whereas the pH has an exponential inuence. The pH of the cement paste can be assumed to be constant, but since the steel-concrete distance is highly variable, it causes variation in the results. This variation makes it impossible to accurately identify the critical current density, but it can be calculated to be in the range of 0.1 to 0.01 A/m2 . This range results from xing the steel-concrete distance (the analogue of pit depth) at 100 mm, and changing the pH value from 13 to 12. This range of current density is higher than the threshold for passive corrosion dened in [5, 18]. But since the corroding area at corrosion initiation is only a small fraction of the steel surface, the general corrosion rate may be unchanged at corrosion initiation. 133



This range of in pit current densities mentioned above is lower than the current density reported for pitting initiation in stainless steels (6.9 A/m2 in [114]) and resemble the stable pitting density of 0.013 A/m2 reported in [114] with a general current density of 0.001 A/m2 (which is dened in [5, 18] as passive corrosion). 7.3.4.1 Micro-structure of ITZ and chloride threshold

Since a change of one pH unit changes the critical depth by an order of magnitude, for a constant current density, Table 7.9: Critical proddepositions on the steel surface may inuence the result siguct of pit depth nicantly (pH of deposits may range from 8.2 for calcite, and current density for difto 13.6 for C-S-H gel). In Subsection 6.6.2 it was found ferent external that deposits close to the steel surface may be completely pH values dierent from these on other materials or common cement paste. Calcite1 deposition (like those observed in the ITZ pH x · i (A/m) 13 1.02E-05 simulation, Figure 6.10 on page 100) can barely inhibit iron oxidation by the pH buering mechanism, but may indeed 12.5 3.22E-06 12 1.01E-06 inhibit oxygen reduction and reduce current density by depositing on high pH spots caused by oxygen reduction. This 11.4 2.50E-07 inhibitive mechanism was not included by Galvele [32] in 10.8 5.56E-08 his localized corrosion model, and is not quantied by the 10.6 3.47E-08 measurement of ITZ properties performed in this work. The parameters related to ITZ thickness exhibit good correlation for the horizontal rebars only, whereas steel concrete distance correlated well in both directions. The correlation of the ITZ thickness with the chloride threshold can be explained by the nature of the ITZ below horizontal rebars. This ITZ is composed of one single large void only. Hence, the ITZ thickness represents the distance of the bulk concrete from the steel. The correlation coecients of the ITZ thickness with the steel-concrete distance are low and so the correlation between ITZ thickness and chloride threshold can not be explained by co-linearity of the steel-concrete distance and ITZ thickness as discussed earlier. It should be noted that the steel-concrete distance is measured from the steel surface to the closest concrete component. Examination of the images shows a thin layer of material adhered to the steel surface in the large pore located below the horizontal rebars. This phenomenon makes the steel-concrete distance smaller by up to two orders of magnitude relative to the ITZ thickness in horizontal rebars (Table 6.5). 1

Calcite is a minor component that may be found in Portland cement, a component of the aggregates, and a major component of the ler used in this work.

134



In the absence of the aforementioned thin layer, the maximum ITZ thickness around horizontal rebars would have been the representing value for the diusion distance from the environment to the pit bottom, which is buered by the dissolution of concrete solids, exactly as presented in the model described in [32]. The relationships of the chloride threshold with the ITZ thickness and with the steel-concrete distance are not interrelated. This can be explained only if the pH buering capacity of the layer adjacent to the rebar surface is lower than that of the concrete located farther from the steel surface, or if the dissolution rate of this layer is the limiting factor of its buering capacity. A combination of both mechanisms may be possible as well. If the layer is composed of carbonates, which act as an oxygen reduction inhibitor [29], it would not explain the observation that the steel-concrete distance lays on the same line for vertical and horizontal rebars when plotted versus the chloride threshold (Figure 7.5), since it buers to a pH of 8.4 only, which is too low to induce passivation.

(a) Cement grains adhered to

(b) Deposition on steel surface

(c) Corrosion product on steel

steel surface

surface

Figure 7.10: Solids on steel surface This thin layer seems to have some buering capacity, which may render some corrosion protection. The pH of this buering capacity is dened by the layer chemistry and is limited by the dissolution rate of the solids composing the layer. This layer can be composed of: i) cement paste and its hydration products, which adhered to the steel during the formation of the void (Figure 7.10a); ii) minerals that precipitated preferentially on the steel surface, as shown in Sub-section 6.6.2 on page 98, and reported in [106], and discussed hereafter (Figures7.10b and 6.21f); or iii) corrosion products (Figure 7.10c). All of these components may be found on a single rebar, each with its own pKb and dissolution rate. Modeling this system, in order to understand its mechanisms, requires identication of the solids and their mentioned properties, which was beyond the time scope of this work. 135



7.3.4.2 pH of hydrated cement paste

Fortunately, it is possible to suggest a reasonable interpretation based on information found in the literature and from other parts of this work. First, if the layer is composed mainly of CSH gel, which results from the hydration of nearby cement grains (which envelop the vertical rebars, and may be adhered to the rebar even in the large void below horizontal rebar) with a pH above 12.5, then this pH will annul any other buering eect of the farther cement paste. If the dissolution rate of such gel is a limiting factor for buering, the same phenomenon would expected to be observed for vertical rebars as well. Since it is not, CSH gel can not be the main component of the layer in these observations. Carbonates of calcium, although identied in the simulation presented both in this work and in [106], has a pH that is too low to ensure passivation. The carbonate ion itself, however, as an ion of a weak acid, has a buering eect that may reduce concentration polarization [115]. Glass et al. [33] titrated pulverized cement paste with acid and measured the acid quantities needed for pH reduction. Every peak of acid consumption for pH reduction indicates a phase with specic buering capacity at this pH. A review of the results in [33], for CEM I, reveals components with buering capacities at pH 12.5 and 11.4 and another component with buering capacity that ranges from 10.6 to 10.8. The pH of 11.4 is 1.1 units lower than the pH of another component in the same cement paste1 . This dierence in pH is proportional to the dierence between the steel-concrete distance and the ITZ thickness below horizontal rebars, which is about one order of magnitude. Thus, if the material composing the thin layer is the material with pH of 11.4, it will have an equivalent inuence on corrosion initiation as has the farther cement paste, which has a pH of 12.5. Figure 7.11 shows an example of the expected concentration polarization of hydroxide ions (pH) as a function of current density for two cases: i) a buer of pH 12.5 located 146 mm from the steel surface; and ii) a buer of pH 11.4 located 9.1 mm from the steel surface. This example simulates the phases indicated in [33]2 , whereby the buer with pH 12.5 is located at a distance equal to the average ITZ thickness of specimen W65 H (Table 6.5) and the second buer, with pH 11.4 is located at a distance equal to the average steel-concrete distance of the same specimen. The lines representing the concentration polarization are similar. Because the ITZ thickness and the steel-concrete distance dier from their averages, and this variation is independent, one of the buers can protect the 1 2

These values are for OPC. Other cement types have dierent values. These values are for OPC. Other cement types have dierent values.

136



steel when the other is varying to a value higher than the average. So, inability to keep the steel-concrete distance at small value will not cause higher susceptibility to corrosion, as long as the ITZ thickness is small, and vice verse.

Figure 7.11: The eect on the critical current density of two buering sources located at dierent distances from steel surface A review of pH data of probable minerals found in cement paste, or of similar minerals, reveals that Hillebrandite (Ca6 Si3 O9 (OH)6 ) has a pH of 11.51 [21], which is in good agreement with the pH found in [33]. Another mineral with a pH of about 11.4 is Foshagite (Ca4 Si3 O9 (OH)2 ) which has a pH of 11.37 [21]. Hydration products in the ITZ have higher Ca/Si ratios than in the bulk, where it is about 1.66 [116]. The Ca/Si ratio of the hydration product in the ITZ ranges from about 2 to 4, with most of the data about slightly more than 2 [117]. Hence, minerals, which are similar to Hillebrandite, 137



are expected to be found among the hydration products. The higher ratio, which was observed at [117], is not necessarily a result of very high Ca/Si ratio of the minerals, but might be a result of the presence of calcium hydroxide or carbonate in the interaction volume of the EDS analysis. If the minerals which are precipitated late during the hydration buer at lower pH, the ambiguous inuence of both the steel-concrete distance and ITZ thickness on the measured chloride threshold of horizontal rebars, may be explained as discussed above.

7.3.4.3 Diculties with the model Since the inuence of the steel-concrete distance on the chloride threshold is the same for vertical and horizontal rebars, the concrete component closest to the rebar for the vertical rebars should be made from a material that has the same pH as for the horizontal rebars. The above observations, of [116] and [117], support the assumption that a Hillebranditelike mineral may be deposited on the pore-paste and pore-steel interfaces. Yet, if these deposits envelop the pores and make it impervious, so farther solids with pH of 12.5 should not inuence the pH in the steel vicinity. If it is pervious, the high pH solids, which are microns away from the vertical rebar (the ITZ thickness in the vertical case is characterized by a gradual change of porosity, not as a big pore as in the horizontal case), should inuence the local pH in the anodic site more than the low pH deposits. Some explanation have to be given to the question: why do the solids with pH = 12.5 inuence the threshold only in the case of horizontal rebar? The answer to this question may lay in the form of these deposits. The steel-concrete distance exists because the deposition is not a homogeneous layer. It has areas well covered and many aws. If the layer was consistent, then the distance would have been zero. The aws make the steel-concrete distance a variable, and allow the diusion of ions from the pore solution to the steel surface. The average steel-concrete distance is smaller for vertical rebars (see Table 6.5 on page 87). That suggest better diusion of ions from outer layers to inuence the environment chemistry, in the horizontal case. It can be speculated, that to prevent the high pH solids, around vertical rebar, from protecting the steel by its high pH, the high pH solids of the cement paste should be covered with impervious coating. This coating could be made by the same deposits which cover the steel, but it should be denser in the vertical case, so it will hinder high pH solid dissolution. That means that the spatial arrangements of deposits in micron-size pores (as found around vertical rebars) may be dierent from that in hundred-micron size pores (as found below horizontal rebars), and may be a result of the deposits' dierent morphology. This assumption should be conrm by further work. 138



It can be concluded, that the variation of the chloride thresholds found in laboratory (Table 1.1 on page 23) and eld [46] can be explained by the inuence of the ITZ on the chloride threshold. The most important characters of the ITZ for corrosion inhibition, in the mixes which have been tested in this work, is the steel-concrete distance and ITZ thickness (the later for horizontal rebars only). Based on [33], the relevant concrete solids in ordinary Portland cement concrete are expected to be two phases: one with pH of about 12.5, and the other about 11.4, where the later is expected to be related to the solids located closer to the steel surface. This eect, which may result from farther higher pH solids, was distinctive only for the horizontal rebars, in this study.

Eect of long time at low corrosion rate. It is possible that long time exposed to low corrosion rate (passive state) before active corrosion initiation, may lead to consumption of the buering materials at the steel-concrete interface, which may aect the result as well. Long time to corrosion can be a result of low eective diusion rate, resulting from dense and ne microstructure of the concrete, low temperatures, or low surface chloride concentration. For the case of the thin layer of solid deposits on the lower part of horizontal rebars and low chloride penetration rate, the thin layer may lose its buering capacity long before the chloride threshold, found in this work, is achieved. As sequence, the buering eect will result from the remote solids only. These solids are very close to the vertical rebar, but are as far as the ITZ thickness from the horizontal rebar. Hence, the dierence of the threshold value between the vertical and horizontal rebar may be more pronounced. In addition, while only two specimens in the present work demonstrated anaerobic corrosion, anaerobic corrosion was common in other work which was carried with duplicated specimens at Aachen, Germany [118]. Ergo, the corrosion mechanism might be dierent in dierent conditions, leading to dierent results. For example, the temperature at which the corrosion experiments were performed in Aachen was lower than that in Israel. Temperature changes the coecient of diusion [119], corrosion rate, and the solubility of solids in various manners. As a result it is dicult to predict how temperature should aect the results.

7.3.4.4 Quality of the relationship

The standard error of estimation (SEE) values of the chloride threshold, based on the ITZ properties (Table 7.7) are high. Considering the the smaller number of data for the correction factor (for correcting the threshold value by the ITZ thickness) for the horizontal rebar, the correction factor cannot give a reliable estimation of the lowest expected 139

CHAPTER 7.

DISCUSSION

1

chloride threshold .

7.3.

CHLORIDE THRESHOLD

Yet, the average chloride threshold can be estimated.

Reviewing

Tables 6.4 to 6.7 reveals that the standard error of ITZ structure measurement is even in a higher magnitude than the error of the chloride threshold versus steel-concrete distance relationship. Furthermore, the steel-concrete distance inuences the chloride threshold based on theoretical considerations, which were mentioned earlier on this sub-section. Hence, the source of the high SEE should be the diculty in accurately determining the ITZ properties, which is prone to large error due to the high ITZ variability.

So, the

problem of the poor SEE may not be: calculating the chloride threshold based on the steel-concrete distance , but: accurately evaluating the steel-concrete distance .

7.3.5 Practical aspects It should be noted that corrosion damage is the main observation of the chloride threshold application.

A higher chloride threshold does not necessarily mean a longer time-to-

corrosion nor a lower corrosion rate. After all, the time-to-corrosion depends also on the diusion of chlorides.

The range of chloride thresholds found in this study, as well as

in other works [46] makes the chloride threshold, in practice, more important for service life than the transport properties of ordinary concretes. For example, time to corrosion of two concrete mixes with dierent water-to-cement ratios was estimated by Life-365, a software model designed to estimate service life and life-cycle costs [120]. These mixes are (1)

W/C

ratio 0.60, which is considered to be a low-quality concrete in terms of

durability; and (2)

W/C

ratio 0.45, which is considered to be a high-quality concrete in

terms of durability. The exposure environment was set to a typical Israeli, Mediterranean coast. Figure 7.12 shows the chloride concentration at a depth of 20 mm as function of time from exposure. It is clear, that if the chloride threshold is set as a constant value for both mixes (for example 0.46 % of concrete mass, as shown in Figure 7.12), the timeto-corrosion of the mix with the higher

W/C

ratio will be shorter. However, setting the

threshold values to 0.8 % and 0.46 % of concrete mass for

W/C = 0.6

and

W/C = 0.45,

respectively, as were found for some mixes in this work, reveal dierent results. Almost tree times longer time-to-corrosion of the higher lower

W/C

W/C

mix, compared with the mix with

ratio. The value of the threshold at a certain

W/C

ratio varies signicantly

(Table 6.17), thus it is quit common to use a constant value as the limiting one, and refer mainly to the diusion rate. As demonstrated above, increasing the chloride threshold may be of greater importance for the RC life cycle than reducing the apparent corrosion rate or diusion rate;

1

Because of student distribution, for the same condence interval the SEE should be multiplied by larger number when the sample size is smaller.

140



Figure 7.12: Chloride concentration (% of concrete weight) vs. time, and predicted timeto-corrosion and reducing the steel-concrete distance may achieve the desired goal. It is yet to be investigated whether it is possible to control this character of the ITZ, and how exactly can such control be achieved. It is important to remember that the chloride threshold is not necessarily related to the corrosion rate. Once the corrosion begins, the corrosion rate may be reduced by deposition of carbonates on the oxygen reduction sites, reduction in oxygen concentration or partial pressure, and by the increase in resistant to ionic current between the anode and cathode due to the accumulation of corrosion products. When corrosion products accumulate within the small voids, the pressure of crystallization is not high enough to crack the concrete; however, if the voids are large enough, the crystallization pressure may crack the concrete. Concrete with large voids may, therefore, exhibit corrosioninduced cracking before concrete with small voids does, assuming the same degree of steel reinforcement degradation. This can explain the common observation of extensive corrosion damage along horizontal reinforcement as opposed to vertical reinforcement [52]. 141


7.4. OPEN QUESTIONS

7.4 Open questions

Several questions were raised during this work that have yet to be answered. These questions can be categorized into three categories: ITZ, cement paste chemistry, and corrosion mechanisms. 7.4.1 ITZ structure

Our assumption at the beginning of this work was that the ITZ around rebars behaves similar as the ITZ around aggregates. We used the known technique to control the ITZ properties, but the outcome attainment was limited. The results of this work show that the ITZ around rebar acts in a dierent manner, and it is obvious that additional study must be conducted to answer the following questions: How the ITZ around vertical rebars is formed? What role do the rheological properties of the concrete play? What is the role of concrete consolidation? Why bleeding is an important parameter? Is bleed water that ows up around the rebar responsible for the ITZ's micro-structure? How the ITZ formed around horizontal rebars is formed? Is the maximum thickness below the rebar a function of the rebar shape, the bleeding quantity, the bleeding rate, or a combination thereof? What are the deposits on the steel surface? How do they form? 7.4.2 Cement and concrete chemistry

What is the inuence of the pore solution chemistry, and especially of its pH and carbonate, calcium, and silicate concentrations? The literature emphasizes preferential deposition of calcium hydrate. Results from the ITZ simulations demonstrate that this is not the only species that is deposited preferentially, and that the location (the surfaces below the deposits and in front the deposit) is important. Since the chemistry of the cement paste change with time, it is important to know not only what the deposits are, but also how the deposits interact with the surrounding environment. What is the relationship of these deposits to the pore solution? Silicate may have an inhibitive eect on iron corrosion. Its size and electrical charge make it easy to adsorb to surfaces, better than hydroxide and chloride. Silicate is an important ion in cement hydration. Issues that have yet to be investigated include the inuence of cement chemistry on pore solution composition in early and mature concrete; the eect of pore solution chemistry, and the silicate in particular, on the corrosion behavior; and the mechanism in which both steel-concrete distance and ITZ thickness play a role in determining the chloride threshold. 142

CHAPTER 7.

DISCUSSION

7.4.

OPEN QUESTIONS

These questions can not be answered by this work and should be addressed in future investigations. . .

143

8 Conclusions 8.1 ITZ

The ITZ around horizontal rebars is composed of three regions: dense region above the rebar, large void below the rebar, and transition between these two. The ITZ around vertical rebar is dense like that above horizontal rebars. Where large voids present near the rebar, like the large void below horizontal rebar, deposits can be seen on the rebar surface. These deposits are usually precipitations, but adhered cement grains and corrosion product have been seen as well. The ITZ is highly variable, so a large number of images is needed in order to characterize it. 8.1.1 Characterization

For processing a large number of images an automatic method is needed. Common image analysis based on gray level thresholding is doomed to fail. Image analysis which is based on pixel neighborhood was successfully used. The ITZ thickness and steel-concrete distance are lower for vertical rebars relatively to horizontal rebars. Typical range of ITZ thickness is hundreds of microns, and the typical range of steel-concrete distance is a few microns to tens of microns. 8.1.2 Formation

The relationship between the steel-concrete distance and the mix properties is unclear. In vertical rebar casts, the steel-concrete distance tends is be smaller as the mix becomes more uid (i.e. contain less powders, more water, and has a higher slump). In horizontal rebar casts, the steel-concrete distance might be inuenced by processes that occur during hydration. Hence, for a constant concrete chemistry, as was maintained in this work, controlling the steel-concrete distances in the horizontal case is more important. It can be achieved by using SCC, but more work is needed to nd if it is possible and how it can be done with common concrete. The ITZ thickness around horizontal rebars and its variation depend on mix properties related to bleeding. Bleed water may form a water lens below the rebar, but the size of

145

CHAPTER 8. CONCLUSIONS

8.2. PULLOUT

that lens is not directly related to the quantity of water bled. Bleed water accumulated below the rebar may break through the paste at the rebar side, and thus reduce the void size. Rheological properties of the mix, such as slump, may aect the maximum size of the void attained before such a breakthrough. This may explain the correlation of the standard deviation of the ITZ thickness around horizontal rebar with the average ITZ thickness. No clear relationship between the ITZ thickness around vertical rebars and other properties was found. 8.1.3 Simulation

A simulation of an ITZ by controlling the distance between polished hydrated cement paste and polished steel have been failed in term of controlling the distance. Dierent crystals have been grown in front of the steel and at other places. 8.2 Pullout

The bond of smooth rebar might be very sensitive to minor changes at rebar surface as rebar deformation or corrosion on rebar surface. As a consequence correlations with other variables have not been found, and it is not clear from this what the micro-mechanism of the pullout test is, and how the pullout test results relate to the ITZ's micro-structure, mix properties, and concrete strength. 8.3 Corrosion validation

It was found that the relationship between potential and polarization resistance of embedded rebar or steel in simulated pore solution can be associated to one of two dierent curves. As a result, half cell potential is not an explicit sign of depassivation. Nevertheless, potential measurement can be used for corrosion initiation indication, if the corrosion state is veried later. 8.4 Chloride Threshold

The range of chloride thresholds obtained in this work overlaps the range found in the literature. The most appropriate form for expressing the chloride threshold is by weight of concrete. For xed concrete chemistry (binder composition), the only parameter that inuences the chloride threshold seems to be the ITZ micro-structure. The steel-concrete distance is 146

CHAPTER 8. CONCLUSIONS


inversely correlated with the chloride threshold: as the distance decreases, the threshold increases. The ITZ thickness inuences the chloride threshold of the horizontal rebars only: as the ITZ thickness decreases, the threshold increases. Mix proportions and properties, which are correlated with the chloride threshold, are related to ITZ microstructure formation. The relationships between mix proportions and properties, ITZ microstructure, and chloride threshold are presented schematically in Figures 8.1 and 8.2. The micro-structure of the ITZ can explain the variation in the chloride thresholds found in laboratory and eld research. Yet, the techniques and results of this work require further development in order to be used for controlling the ITZ and to accurately evaluate the chloride threshold. The model oered by Galvele [32] is appropriate for explaining corrosion initiation when the geometry and external pH are set to those found in concrete around embedded rebar. It is possible that deposits on the surface of horizontal rebars form chemical environment which is dierent from by hydration product formed farther from the rebar. Hence two eect can be seen for horizontal rebars: the eect of deposits on the rebar (distance of ~10 mm), and the eect of the bulk concrete (distance of ~100 mm and more).

147

CHAPTER 8.

CONCLUSIONS

8.4.

CHLORIDE THRESHOLD

Figure 8.1: Visual presentation of correlations between mix composition, fresh mix properties, ITZ, and chloride threshold, for

148

horizontal rebars.

CHAPTER 8.

CONCLUSIONS

8.4.

CHLORIDE THRESHOLD

Figure 8.2: Visual presentation of correlations between mix composition, fresh mix properties, ITZ, and chloride threshold, for

149

vertical rebars.

Appendix, MATLAB scripts for image analysis

A.1 Image classication % this function is intended to use Mean Shift algorithm to cluster % the whole function [steel, pore, concrete, p_modes, ClusterError, PhaseError, k, l]... = MSBigIm(I) % reducing the black frame BW=edge(I,'canny'); [H,theta,rho] = hough(BW); P = houghpeaks(H,5,'threshold',ceil(0.3*max(H(:)))); x = theta(P(:,2)); y = rho(P(:,1)); lines = houghlines(BW,theta,rho,P,'FillGap',100); xy2=[]; for k = 1:length(lines) xy = [lines(k).point1; lines(k).point2]; xy2=cat(1,xy2,xy); end if length(lines)>1 I=I((min(xy2(:,2))+2):(max(xy2(:,2))-2)... ,(min(xy2(:,1))+2):(max(xy2(:,1))-2)); end clear map BW H theta rho P x y xy2 xy k lines % creating more dimensions

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Appendix

A.1.

IMAGE CLASSIFICATION

nrows = size(I,1); ncols = size(I,2); I2(:,:,1)=I; h = fspecial('gaussian', [15 15], 2.5); I2(:,:,2) = imfilter(I,h); h = fspecial('unsharp', 0.1); I2(:,:,3) = imfilter(I,h); E = entropyfilt(I,ones(15,15)); I2(:,:,4)=uint8(E.^4.*255/max(max(E))^4); E2 = entropyfilt(I2(:,:,3),ones(5,5)); I2(:,:,5)=uint8(E2.^2.*255/max(max(E2))^2); clear h E E2 ab = double(I2); nrows = size(ab,1); ncols = size(ab,2); ab = reshape(ab,nrows*ncols,5); clear I2 I data = ab(1:100:length(ab),:); % clustering cl=1; k=10; l=15; while cl901 cl=1; end k=k+10; l=l*0.85; end % using the cluster centers in p_modes % first step is to find the distance of every point in ab from every cluster 1

This condition is ommited at rst run. A second run with this condition is performed if the classication result are rejected by visual quality verication.

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Appendix

A.1.

IMAGE CLASSIFICATION

% center. the distance is the sum of the squers of the differences for % every dimension. le = ones(size(ab,1),1); rmin=le.*10000; ind=zeros(size(le)); for i = 1:size(p_modes,1) Dis=cat(2,le.*p_modes(i,2),le.*p_modes(i,3),le.*p_modes(i,4),le.*... p_modes(i,5),le.*p_modes(i,6)); Dis=abs(Dis-ab); Dis=sum(Dis')'; ind=ind.*(rmin