1 The effect of aggregate packing on the performance ...

Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

The effect of aggregate packing on the performance of SCC using dune sand S. K. Adekunle1, S. Ahmad2 and M. Maslehuddin3 1

Lecturer-B, Department of Civil Engineering, King Fahd University of Petroleum and Minerals, PO Box 8631, Dhahran-31261, [email protected] 2 Associate Professor, Department of Civil Engineering, King Fahd University of Petroleum and Minerals, PO Box 1403, Dhahran-31261, [email protected] 3 Senior Research Engineer, Research Institute, King Fahd University of Petroleum and Minerals, PO Box 442, Dhahran-31261,, [email protected]

ABSTRACT: Self-compacting concrete (SCC), in spite of its several advantages, is still not adopted in certain parts of the world, because of the unsuitability of locally available SCC raw materials with required quality at such locations – for example, the scarcity of different grades of fine aggregate in countries like Saudi Arabia, where dune sand is often used as fine aggregates in concrete production. Since the optimization of particle size distribution has been identified as a cost effective tool for producing SCC, the absence of various required sizes of particles in dune sand hinders the aggregate packing in the grading zone below 3/32 in. (2.4 mm), adversely affecting the performance of SCC. This paper critically reviews the available information in the literature on aggregate packing theories in the context of SCC, and subsequently highlights how these theories can be used together to arrive at a practical optimum grading for SCC utilizing dune sand and limestone coarse aggregates. The flow results and the compressive strength of an SCC mixture utilizing an optimum aggregate grading are then presented along with that of a control, as an illustrative example. The results indicated that the mixture with optimized aggregate grading has a lower demand for chemical admixtures, while its compressive strength was higher by more than 20%.

Keywords: SCC, Dune sand, particle size optimization, aggregate packing, PSD.

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Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

INTRODUCTION A team of smart engineers in Japan, in the late 80’s, developed the smart concrete material, which is now known as "Self Consolidating Concrete (SCC)," inspired by the challenges faced by construction industries. The SCC possessed the ability to take form shapes without mechanical assistance, while still maintaining the mixture stability. In the wake of the spread of the novel SCC idea, most producers and researchers concerned themselves with achieving the ultimate aim – obtaining a concrete material possessing three basic characteristics: high deformability, good cohesion and a high segregation resistance 1 – by the use of expensive chemicals, which jacks up the material unit cost. Yet SCC, in spite of its high cost, was widely adopted due to the several other benefits of SCC over the conventionally vibrated concrete (CVC), which tend to offset the high cost of production 2, 3. Research efforts had subsequently been made towards reducing the production costs. Since the cost raising components are superplasticizers (SP) and stabilizers/viscosity modifying admixtures (VMA), such efforts include finding cheaper alternatives for the SP and VMA as well as playing with the suitable selection of ingredients and their proportions such that lesser amounts of the cement, SP and VMA are required in the production of SCC mixtures. This paper focuses towards the importance of grading of the aggregates for improved performance of SCC mixtures. The effort made in this direction is also very important as the packing of aggregate particles results in minimal voids content which reduces the required amounts of paste, consequently minimizing the problems related to shrinkage and crack 4, 5 and reduced elastic modulus 5-8, which are of great concern for SCC, in addition to possible cost savings. Further, good packing ensures more workability because more paste is available at the aggregates’ surfaces to work as lubricant for the aggregate particles 5. In this work, a critical review is presented on a number of established models for determining the optimal aggregate particle size distribution (PSD), and as an illustrative example, experimental results are presented for an SCC mixture following a chosen model and the results compared with that of a control SCC mixture as an effort to highlight the relative benefits of achieving enhanced performance of SCC mixtures simply by ensuring an optimum packing of the aggregate particles. OPTIMIZATION OF PARTICLE PACKING DENSITY Experimental and simulation studies Early efforts made on the determination of particle packing density were mostly in the form of packing experiments with groups of mono-sized spherical balls9-14. For example, Standish and Borger 13 conducted packing trials with three groups (6 mm, 9.6 mm and 12.7 mm diameters) of mono-sized spherical balls. Nanthagopalan and Santhanam 15 refereed the very first research work by Feret 16 on concrete particle packing. The outcomes of these trials were used to prepare general aggregate distribution curves, which are popularly used for proportioning concrete aggregates, such as the popular Fuller-Thomson 11 curves used for optimum proportioning of aggregates for concrete 17. With the recent availability of digital computing facilities, these packing experiments have been taken to the virtual environments in a number of numerical studies 17-27 for digital simulation of the packing of concrete aggregates. These computer-based techniques offer relatively easier way to experiment with several trials as compared to what could be done in physical laboratory trials. Just like the early physical experiments, the simulations experiments, with the exception of a few21, 28, 29, still deal with spherical shapes. Although it is a known fact that the real aggregate particles cannot be perfectly spherical, and their actual irregular nature has notable effects on the packing of aggregate particles 30-33, many considered it acceptable to idealize aggregates (natural or artificial) as groups of mono-sized spheres 17, as this simplifies the calculation of the packing densities 20, 34, and provides a reliable test of accuracy for the packing models derived from the experimental results 35. In any case, physical trials or digital simulations, the studies were all about the search for the point on the aggregate volume ratio axis at which the packing density becomes, or close to, maximum (or void ratio/porosity gets to the minimum).

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Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

Many particle parking experiments such as that of Standish and Borger 13 involved ternary blends of particle grades, the results from which is more useful than binary groups, since we usually require minimum of three aggregate size groups (two coarse and one fine). In this way, many studies 15, 36, 37 have been devoted to generating dozens of points on what is referred to as “ternary packing diagrams (TPD)” (or triangles), which is very helpful in determining the optimum aggregate combinations in ternary blends for maximum packing density. Ternary diagrams can also be used for binary systems. Figure 1 shows an example of TPD. The basic limitation of the ternary diagram in the determination of the optimum packing density of aggregates lies in the fact that only the sizes used for its preparation can be optimized. For other combinations of sizes, another one has to be generated. This makes it useful only for ready mixed concrete firms and large scale construction projects and not for an individual user15.

(a) Original experimental points.

(b) Final ternary packing diagram.

Figure 1 — Ternary Packing Diagram15. Particle packing models As a result of the difficulty involved in conducting the packing trials for each new set of aggregate sizes, generalizations were made from many of the packing experiments, in the form of mathematical/semiempirical models 9, 33, 38-47 for predicting the packing density (or void ratio) of any combination of aggregate groups. As obtained from the literature, these models take two main forms. The first form of packing models involves the generation of optimum volume ratios of the individual aggregate grades (say two coarse types and one fine type) to produce the best packing of the particles (least voids) per unit volume. In this class of models, the input parameters are the individual aggregate type physical properties (such as PSD, bulk densities, etc), which must be determined or known. Included in this class are the models of Stovall 46, as modified by de Larrard 48, Toufar 47, as modified by Goltermann 45, Larrard – Linear Packing Model (LPM) 41, 42, Solid Suspension Model (SSM) 42 and Compressible Parking Model (CPM) 40 – and Dewar 43. Packing models in the second class follow the idea of generating geometrical series of masses of individual particle sizes in the entire particulate system within certain size ranges, for the densest packing in these ranges. These series of sizes can then be used to plot the optimum grading curves, after which the available groups of concrete constituent particles can be subjected to simple trial and error calculations to obtain the least practical deviation from the optimum curve. Included in this category of packing model are Funk and Dinder 33 (modified form of Andreasen 38 ) and Carneiro 39 models. Volume ratio-based packing models — The packing models in this group were derived from the results of many packing trials with spherical balls as rationalized in the previous section, and as such, it was argued elsewhere 49 that they are more suitable for rounded particles, as against the irregularly shaped particles with rough surfaces of most of the concrete constituents. This may be corroborated by other

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Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

reviewers 50, 51, who identified such packing models as the Aim 9 and Furnas 44 models as being unsuitable for proportioning concrete constituents. Nevertheless, a number of these models have been shown 45, 50 to predict close enough to experimental results after some modifications. Packing models, such as that of Dewar 43, and (modified) Toufar 45, both have things in common: they were developed for binary mixtures, and so applying them to multi-particle blends involves the application of the model to the combination of two groups of particles. Next the combination is treated as one and the next group of particle is integrated, until the overall packing density of the entire system is obtained. The basic difference between the two lies in the order of treatment: the former proceeds from the finest two materials towards the coarser direction, while the latter proceeds in the reverse. A well seasoned review by Jones 35 contained a detailed comparative analysis of these two models, in addition to the Larrard’s LPM 41, 42 and CPM 40 models. In this work, Jones 35 identified the CPM 40 as being the easiest of the models analyzed, and as such proposed a modification to the model, as none of those models performed consistently satisfactory enough on the set of tested experimental data 35. The modified model CPM 35 consistently outperformed all other, not only on both the aggregate and powder phases of the tested data, but also on other two published data – one by Goltermann et al 45 on binary aggregate blend, and the other for ternary mixture of mono-sized steel balls by Standish and Borger 13. Nevertheless, the modified CPM 35, just like many other reliable ones around, needs further testing on several data before its accuracy in predicting the packing density of concrete constituents can be ascertained. Grading curve-based packing models — Two models are considered here, namely the Dinder-Funk (DF) 33 (modified form of Andreasen 38 ) and Carneiro 39 models. D-F 33 model: In a work 33 on particle packing models, Funk and Dinder identified the Andreasen model as the best available (1994) packing theory for the determination of the densest particle packing, as it was developed to deal with continuously graded particulate systems, as against the other models, such as that of Furnas 52, which was based on the packing of mono-disperse systems. The Andreasen’s equation is given by 38

(1) where

F(d) = the percent finer than a given particle size d, dL = the largest particle size in the particulate system, and n = ‘distribution modulus’ The distribution modulus, n, is a characteristic of the system being considered, and it takes a value from 0 to 1. Andreasen 38 obtained the best packing of the system when n was between 0.33 and 0.5. A major defect of his procedure was the assumption that the system contains all particle sizes, including infinitely small size particles, which is impossible. A log-log plot of his grading curve (F(d) vs. d) yields a straight line that does not approach zero on either axis (Figure 2). He recognized this defect, but seems to believe the error is minimal. However, Funk and Dinder 33 contend this, and corrected this anomaly by adopting the Furnas 52 idea of recognizing the smallest particle size in the system, resulting in the D-F model, given by (2) where ds is the smallest particle size in the system 33. D-F equation (2) yields constant ratios between consecutive amounts of each size class (sieve sizes), and so it's a geometric progression just like the original Andreasen's equation. As can be seen in Figure 2 (with dL = 19 mm, dS = 19 mm and n = 0.25), the D-F equation yields a curve that bends downward towards the smallest particle size and zero value of F(d).

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Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

D-F model (dL = 19 mm, dS = 19 mm, n = 0.25) Andreasen model (dL = 19 mm, n = 0.25)

% finer (M/M)

100.00

10.00

1.00 0.01

0.1

1 10 Particle size (mm) Figure 2 — Andreasen and Dinder-Funk Particle Packing Models.

Carneiro 39 model: The particle packing model published by Carneiro 39 operates in a similar way as the Andreasen/D-F model explored in the last section. It’s also based on the idea of constant ratio of mass retained between a pair of consecutive sieve sizes. The Carneiro equation 39 is given by (3) where

𝑃1 = first term of the geometric progression, 𝑃𝑟 = constant ratio of mass of particles retained between a pair of sieves, and 𝑁 = number of sieve intervals.

Obviously, it’s a 2-parameter model, unlike the 3-parameter D-F model. It’s noteworthy to highlight the fact that both the D-F 33 and Carneiro 39 models produce exactly the same results for certain values of parameter combinations. For example the same grading is produced by the former with dS = 0.075 mm, dL = 19 mm and n = 0.25, and the latter with N = 8 (8 sieve intervals between 19 mm – 0.075 mm), and Pr = 0.5. Application of particle packing models to SCC mixture proportioning As was emphasized earlier, not all the particle parking models are suitable for proportioning concrete 50, 51, due to the fact that concrete systems are composed of continuously graded particles, as against the monodispersed state assumed in developing most of the models 33. Also, generally speaking, any particulate system in which some levels of workability is imposed, such as concrete and slurries, will require to be somewhat away from the densest packing state, as the mixture viscosity at that state becomes infinitely large that workability is severely impaired 33, 39, 51. In line with the foregoing argument, Jones et al 35 had considered the maximum packing density satisfying Day’s mix suitability factor (MSF) 53, as the absolute maximum packing density suggested by the models produced harsh CVC mixtures. Obviously this will be more serious in the case of SCC where a higher degree of workability is required. That is why, in their work, Melo and Carneiro 4 considered the suggested aggregate grading by Carneiro model 39 satisfying the imposed constraints of acceptable aggregates volume ratios suitable for SCC mixtures, as established by previous studies. In the present work, as an illustrative example, Carneiro 39 and D-F 33 models were used for the optimization of the aggregate proportions of SCC made with limestone aggregates and dune sand. As

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Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

obtained in previous studies 35, more significant degree of packing was observed in the granular phase than in the powder phase, therefore, this study concentrated on the optimization of the granular phase (fine and coarse aggregates) of the SCC mixture. EXPERIMENTAL PROGRAM Materials used in the development of SCC mixes Aggregate and gradation — The coarse aggregates used in this study were crushed limestone sourced from a local quarry in Abu Hadriah, Eastern Province of Saudi Arabia Each of the two types of crushed limestone has a maximum aggregate size of 19 mm, specific gravity of 2.60 and absorption of 1.4%. Dune sand, a vastly available material in the Kingdom, was used as fine aggregate. The specific gravity of the fine aggregate was 2.56, and the absorption was 0.4%. Two packing models were considered for optimization of the aggregate PSD. The Carneiro model (Equation (3) 39) parameter of Pr = 0.70, which was established by Melo and Carneiro 4 as being consistent with the constraints of acceptable aggregates’ volume ratios for SCC mixtures, was adopted in this study. The alternative PSD considered was the D-F model (Equation (2) 33, 39), with parameter n = 0.25. In the former case, with eight sieve intervals between 19 mm – 0.075 mm, N = 8, corresponding to, in the latter case, dL = 19 mm and dS = 0.075 mm. Table 1 shows the PSD calculated using Equations 2 and 3 and two sets of trial PSDs of the selected blends of fine and coarse aggregates. Figure 3 shows the four PSDs presented in Table 1. The PSD data calculated using Carneiro model (Equation (3) 39) and shown in Table 1, indicate that around 70% of the total aggregate are above 2.4 mm size. Following this trend of PSD obtained using Carneiro model, a blend of 70% of the available crushed limestone coarse aggregate (with type CA-1/type CA-2 ratio of 1.22) and 30% fine aggregate (dune sand: FA) was considered as first trial grading. PSD of this trial grading is shown in Table 1. As can be seen in Figure 3, the poor grading of dune sand having far much higher percentage passing of finer particles creates a serious deviation in the PSD below 2.4 mm. Besides this deviation of the lower portion of the PSD, the trial mixture with this proportion was harsh and far from resembling a flowable concrete. Therefore adjustment to the first trial gradation was made by increasing the dune sand percentage to 40% and decreasing the coarse aggregate percentage to 60% (maintaining the same type CA1/type CA-2 ratio of 1.22). As can be seen from Figure 3, the adjusted PSD with 40% dune sand is more of following the D-F distribution than the Carneiro grading. Since the second trial grading has shown a better flowability, it was selected for preparing specimens for strength testing. Table 1 — Grading of Coarse and Fine Aggregates, and Combined Gradings Considered. % Finer (w/w) PSD PSD PSD with PSD with Sieve sizes CA-1 CA-2 FA Carneiro; D-F; CA/FA ratio CA/FA ratio (mm) Pr = 0.7 n = 0.25 70/30 60/40 19.000 100.00 100.00 100.00 100.00 100.00 100.00 100.00 9.500

30.00

84.75

100.00

68.16

78.77

68.25

72.78

4.750

10.00

37.15

100.00

45.88

60.91

45.55

53.33

2.400

0.00

0.00

100.00

30.28

46.11

30.00

40.00

1.200

0.00

0.00

100.00

19.36

33.45

30.00

40.00

0.600

0.00

0.00

96.20

11.72

22.81

28.86

38.48

0.300

0.00

0.00

61.40

6.37

13.86

18.42

24.56

0.150

0.00

0.00

21.90

2.62

6.33

6.57

8.76

0.075

0.00

0.00

1.00

0.00

0.00

0.30

0.40

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Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

% finer (M/M)

Caneiro

D-F

30% Dune sand

40% Dune sand

100 90 80 70 60 50 40 30 20 10 0 0

0

1 10 Particle size (mm) Figure 3 — Adopted Aggregate Grading in the Context of Carneiro 39 and D-F 33 models. Cementitious materials/powders and other constituents — Type I cement conforming to ASTM C 150 and having a specific gravity of 3.15 was used. The Fly Ash (FA) utilized in this study was sourced from a local ready-mixed concrete company. The superplasticizer (SP) used was a new generation polycarboxylic based ether hyperplasticiser, while the Stabilizer/Viscosity Modifying Admixture (VMA) was a highmolecular weight synthetic copolymer for enhancing the rheological properties of a flowing concrete mixture. The SP and VMA were sourced from a local supplier. The normal sweet water available in the laboratory tap was used throughout for the trial mixing preparation of test specimens for evaluation of hardened properties of successful mixes, and curing of the test specimens. Preparation and testing of SCC mixtures Using the selected aggregate grading corresponding to a CA/FA ratio of 60/40 as shown in Table 1, an SCC mixture (designated as M1) is considered to evaluate the effect of aggregate grading by comparing the results of mixture M1 with a control SCC mixture (designated as M2) developed in a previous study 54. Except the aggregate gradings and dosages of SP and VMA, all other mix parameters were kept same for both mixtures M1 and M2. Mixture proportions of M1 and M2 are presented in Table 2. Table 2 — Mixture Proportions. M1

Material Cement (Kg/m3)

M2

400

400

100

100

500

500

CA-1 (Kg/m )

541

831

CA-2 (Kg/m3)

442

-

FA, dune Sand (Kg/m )

656

832

FA/total aggregate ratio

40

50

SP (Kg/m )

6.5

7.0

VMA (Kg/m3)

5.5

6.0

Water/powder ratio

0.3

0.3

Fly Ash (Kg/m3) 3

Powder content (Kg/m ) 3

3

3

The SP and VMA dosages shown were the final quantities at which satisfactory levels of selfcompactibility were obtained. The self-compactibility tests conducted on the mixtures were slump flow, V-

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Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

funnel and U-box tests, all in accordance with the requirements of EFNARC 3. Segregation resistance was assessed visually by mortar bandwidth method 3. For each of the two SCC mixtures, 100 mm × 100 mm × 100 mm concrete cube specimens were prepared and cured at a fairly constant laboratory temperature of 25°C. The specimens were evaluated for compressive strength at 14 and 28 days using an automatic compressive testing machine of hydraulic type. RESULTS AND DISCUSSION Table 3 shows the fresh properties and compressive strengths for the two SCC mixtures evaluated in this study. The reported compressive strengths are averages of three specimens in each group. It can be seen from Table 3 that although both mixtures have required self-compactability, the flow table result for M1 is better than that of M2, even though the SP and VMA required, for the reported flow and the observed stability, was slightly lower in the former. However, there seems to be no significant difference between the V-funnel (the other filling ability test) and U-box (passing ability test) results. Table 3 — Fresh properties and compressive strengths of SCC mixtures. M1 M2 Strength ratio Flow Table (650 – 800 mm) 3

780

680

10.5

10.0

8

5

7-day strength (MPa)

67.6

52.6

129 %

28-day strength (MPa)

85.3

71.0

120 %

V-Funnel Time (6 – 12 s) U-Box (0 – 30 mm)

3

3

From the above observation, most obvious benefit in the mixture with optimized PSD (M1) lies in the higher slump flow at lower dosages of SP and VMA as compared to the other mixture. This is expected, as more paste is available for lubricating the particles’ surfaces when particles are better packed 5. For the compressive strength, it is obvious from Table 3 that the mixture with optimized PSD (M1) performed better than the control mixture, with 29% and 20% higher compressive strength at 7 and 28 days, respectively. Since everything (w/p ratio, cement content, fly ash content) is the same for both mixtures, this extra gain in strength and at a lesser requirement for SP and VMA in case of M1 are attributed to a better aggregate interlocking obtainable in more tightly packed system 15, 55 of M1, as compared to M2 system. This point may be more clearly illustrated in Figure 4. The pictures in the Figure 4 are the views of the cross-sections of compressive strength specimens each of M1 and M2 mixtures taken after crushing them at 7 days of curing. As can be noticed from the Figure 4, M1 specimens with better PSD look more densely packed than those of M2 samples.

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Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

(a) M1 (b) M2 Figure.4 — Compressive strength specimens after crushing at 7 days. CONCLUSIONS In the present paper, various particle packing models have been discussed in the context of their development and use, both experimentally and numerically. As a matter of classification, the models discussed were shown to be either based on generating the optimum volume ratios of the individual aggregate grades to produce the best packing of the particles 40-43, 46, 48, or based on generating geometrical series of masses of individual particle sizes in the entire particulate system within certain size ranges, for the densest packing in these ranges. 33, 38, 39. It was highlighted that not all the available particle packing models can be used for concrete proportioning, as had been argued by many authors 50, 51 previously. Worth mentioning is the exploitation of the fact that the ‘densest packed state’ does not, in most cases, produce acceptable workability in concrete generally 33, 39, 51 , and more importantly in SCC 4, requiring the necessary imposition of certain constraints on the packing degree of concrete constituents 4, 35, 39, 53. Finally, to briefly explore the relative benefits of using appropriate PSD in SCC, an illustrative example of selecting a suitable PSD for available limestone aggregates and dune sand utilizing Carneiro 39 and DinderFunk 33 models, showed not only better self-compactibility behavior for the SCC mixture with better PSD, but also 20% higher compressive strengths than that of the control mixture. ACKNOWLEDGEMENT The authors gratefully acknowledge the financial support received from King Fahd University of Petroleum & Minerals (KFUPM), Saudi Arabia, under the CRG funding (Project No. RG1001-1 and RG1001-2). The support of Civil & Environmental Engineering Department and the Centre for Engineering Research at the Research Institute, KFUPM are also acknowledged. REFERENCES 1.

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Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

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Proceedings of the Fifth North American Conference on the Design and Use of Self-Consolidating Concrete, Chicago, Illinois, USA, May 12–15, 2013 –Adekunle, Ahmad & Maslehuddin

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