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FHWA-NJ-2002-026

Correlation of Surface Texture, Segregation, and Measurement of Air Voids FINAL REPORT October 2002

Submitted by Dr. Jay N. Meegoda Mr. Chamil H. Hettiarachchi Civil & Environmental Engineering Dept. New Jersey Institute of Tech. Newark, NJ 07102

Dr. Geoffrey M. Rowe Dr. Nishantha Bandara Mr. Mark J. Sharrock Abatech, Inc. 1274 Rt. 113, PO Box 356 Blooming Glen, PA 18911

NJDOT Research Project Manager Mr. Anthony Chmiel In cooperation with New Jersey Department of Transportation Bureau of Research and U.S. Department of Transportation Federal Highway Administration

DISCLAIMER STATEMENT The contents of this report reflect the views of authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the New Jersey Department of Transportation or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation.

1. Report No.

TECHNICAL REPORT STANDARD TITLE PAGE 3. Recipient’s Catalog No.

2.Government Accession No.

FHWA-NJ-2002-026 4. Title and Subtitle

5. Report Date

October 2002 Correlation of Surface Texture, Segregation and Measurement of Air Voids

6. Performing Organization Code

NJIT/Abatech, Inc. 7. Author(s)

8. Performing Organization Report No.

Jay N. Meegoda, Geoffrey M. Rowe, Chamil H. Hettiarachchi, Nishantha Bandara and Mark J. Sharrock

FHWA-NJ-2002-026

9. Performing Organization Name and Address

10. Work Unit No.

New Jersey Department of Transportation PO 600 Trenton, NJ 08625

11. Contract or Grant No.

12. Sponsoring Agency Name and Address

13. Type of Report and Period Covered

Task order No. 29 Jan. 2001- Sep 2002

Federal Highway Administration U.S. Department of Transportation Washington, D.C.

14. Sponsoring Agency Code

15. Supplementary Notes 16. Abstract

Laser based systems were used in this research to quantify segregation during paving of hot mix asphalt concrete pavements. Two segregated test sections and a control test section were evaluated to determine the applicability of laser texture method to detect and quantify segregation. Laser texture data were gathered from all three sites. Sand patch and nuclear density tests were also performed at 25-feet intervals. In addition to the above, visual surveys were performed to confirm the measurements. The laser texture data consistently showed texture peaks indicating the presence of segregation, which occurred at approximately 100-feet intervals for one site suggesting end-of-truck-load segregation. Test results from the control section were used to establish a correlation between the sand patch test (a quantitative segregation test) and the laser texture data. The poor correlation between nuclear density and texture from the sand patch method for the test sections suggested that nuclear density measurements could not be used for quantification of segregation. However, the high nuclear density values for the control section when compared with those for segregated sections suggested that it could be used as a confirmation test. By combining the concepts described above, a computer program, NJTxtr, to detect segregation was developed. This program uses pavement texture data and determines the acceptability of the pavement section based on the level of segregation present within the pavement section. Ratios of texture in segregated areas to that in non-segregated areas were used as the basis for detection of different levels of segregation. By combining levels of segregation and the extent of each level of segregation, an AREA index was developed to determine the acceptance of a pavement section. When a pavement section is acceptable, the software determines the pay adjustment factor to be used. If segregation is present, it suggests remedial actions for each segregated area. NJTxtr was evaluated using the data collected from one control and two segregated test sections and satisfactory results were obtained. 17. Key Words

18. Distribution Statement

Monitoring Construction, Surface Texture, Segregation, Air Voids, Nuclear Density, Sand Patch, LASER, Mean Profile Depth, Estimated Profile Depth, Asphalt Pavements, and Computer Program 19. Security Classif (of this report)

20. Security Classif. (of this page)

Unclassified

Unclassified

Form DOT F 1700.7 (8-69)

21. No of Pages

94

22. Price

Acknowledgements The New Jersey Department of Transportation (NJDOT) sponsored this research. The program manager at the NJDOT is Mr. Anthony Chmiel and the NJDOT customer for this project is Mr. Andris Jumikis. Authors wish to acknowledge the efforts of the NJDOT project manager and the NJDOT customer. It would not have been possible to complete this project without the assistance of Mr. Nicholas Gephart of Pavement Management Unit, NJDOT. Authors also would like to acknowledge the contributions of Mr. Cliff Wu of NJIT, Mr. Anthony Orsi, Mr. Joseph Maloney, and Mr. Fred Kern of the Bureau of Materials, NJDOT and Mr. Jaroslaw Hucul, Mr. Charles Isiadinso, Mr. Raymond Micharski, Jr., and Mr. Kevin Hall of the Pavement Management Unit, NJDOT. The editorial assistance from Ms. I. Martyn Nichols and technical assistance from Mr. Kurt Huber and Mr. Paul Harbin of Roadware Group Inc., Canada, and Joseph Biggica and Michael Manno of Newark Asphalt, NJ is highly appreciated.

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Correlation of Surface Texture, Segregation, and Measurement of Air Voids -Table of ContentsSummary Problem Statement Objectives Introduction Field Evaluations Test Results Data Interpretation and Application Development of Numerical Procedures NJTxtr Software for Segregation Monitoring Summary and Conclusions References Appendix 1: Visual Evaluation Data for Route 9 Test Section Appendix 2: Standard Test Method for Surface Texture Appendix 3: NJTxtr Results for the Route 9 Data

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1 2 3 3 17 21 29 37 44 54 60 62 71 82

List of Figures Figure 1: Schematic Representation of Surface Texture Laser 8 Figure 2: Example of Laser Surface Texture Measurement Over a 0.6m Length 8 9 Figure 3: The Dynatest RSP (from http://www.dynatest.com) Figure 4: Greenwood Engineering System (from http://www.greenwood.dk) 10 Figure 5: Automatic Road Analyzer (ARAN) (from http://www.roadware.com) 10 Figure 6: WDM - HSTM Trailer Mounted Device (from http://www.wdm.co.uk) 12 Figure 7: ARRB Multi Laser Profiler (from http://www.arrb.org.au/index.htm) 12 Figure 8: ROSANv (http://www.surfan.com) 14 Figure 9: Variable ETD for a Section of Asphalt Pavement (http://www.surfan.com) 15 Figure 10: Four Meter Wide Analysis Window Showing Probable Aggregate Segregation (http://www.surfan.com) 16 Figure 11: General View of Rt. 9 (note area in foreground is patched due to disintegration of materials as a result of segregation, markings on pavement at 5-feet intervals) 19 Figure 12: Sand Patch Tests at 25 ft intervals, Alternating between Tests on Each of Three Test Lines on Rt. 9 19 Figure 13: Sand Patch Tests Performed on Route I-195 20 Figure 14: Definition of Mean Texture Depth (MTD) in the ARAN Software 22 Figure 15: RMS Summary Data for Three Test Lines 23 Figure 16: MTD Summary Data for Three Test Lines 24 Figure 17: Variation of Sand Patch MTD along Three Test Sections. 25 Figure 18: Variation of Nuclear Density along Three Test Sections. 27 Figure 19: Variation of Air Voids Percentage along the Test Section 28 30 Figure 20: Correlation between Textures from the ARAN and Sand Patch Method Figure 21: Frequency Distribution Curves for the ARAN and Sand Patch (SP) Test Results, Normalized by Maximum Value 30 Figure 22: Correlation between Textures from the ARAN and Sand Patch Method for Rt. I-195 Control Section 32 Figure 23: Variation of Sand Patch Test Results and Predicted Texture Depths from ARAN for Rt. I-195 Control Section 33 Figure 24: Typical View of Pavement Surface of Route 9 34 Figure 25: Mean Segment Depth (MSD) Plot for the Test Line 1 of the Route 9 Data 35 Figure 26: Longitudinal Paths for Measurement for Each Lot 37 Figure 27: Determination of Mean Profile Depth (MPD) from a 100mm Base-length 39 Figure 28: Variation of Mean Segment Depth with Base-length 40 Figure 29: Variation of Texture Depth Ratios with Base-length 41 Figure 30: Computation Flow Chart of NJTxtr Software 45 Figure 31: Block Average of Mean Segment Depth for100mm Blocks of Control Section of the Route I-195 Data. 47-49 Figure 32: Block Average of Mean Segment Depth for 100mm Blocks of Test Section of the Route I-195 Data. 50-52 Figure 33: NJTxtr Suggested Treatments for 40-50 Meters of Route I-195 53 iv

Figure 34: Block Average of Mean Segment Depth for 100mm Blocks of Test Line 1 of the Route 9 Data Figure 35: Zoomed Mean Segment Depth (MSD) Plot for the Test Line 1 of the Route 9 Data Figure 36: NJTxtr Suggested Treatments for 0-10 Meters of Route 9

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55 56 57

List of Abbreviations and Symbols AASHTO ARAN ARRB ASTM DOT ETD FHWA Gsb GPS GPR HMA HSTS IRI JMF MLP MMSD MP MP MPD MSD MTD NCHRP NJDOT PC QC/QA ROSANv RMS RN RSP SP TFHRC TR

American Association of State Highway and Transportation Officials Automatic Road Analyzer Australian Road Research Board American Society for Testing of Materials Department of Transportation Estimated Texture Depth Federal Highway Administration Bulk Specific Gravity Global Positioning System Ground Penetrating Radar Hot Mix Asphalt High Speed Texture System International Roughness Index Job Mix Formula Multi-Laser Profiler Mean of the Sean segment Depths Mile Post Materials Procedure Mean Profile Depth Mean Segment Depth Mean Texture Depth National Cooperative Highway Research Program New Jersey Department of Transportation Personal Computer Quality Control/Quality Assurance Road Surface Analyzer - vehicle-mounted Root Mean Square Ride Number Road Surface Profiler Sand Patch Turner-Fairbank Highway Research Center Texture Ratio

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Summary This report describes the research funded by the NJDOT to develop an automated technology to quantify segregation of hot mix asphalt concrete pavements. Laser-based systems were evaluated in this research. From the laser-based systems reviewed, the ROSAN system appears to be the most advanced, with respect to algorithms developed, to determine segregation in asphalt. Currently it has the widest application in this area (Stroup-Gardiner and Brown, 2000). However, the application of the algorithms developed could be applied to the data collected from other laser devices. The level of technology available in the ARAN device is considered acceptable for the application of texture measurement. Consequently, since NJDOT owns this piece of equipment, the ARAN was selected as the field texture measuring device and the development work applied to the output from the laser sensors of the ARAN. Two segregated test sections and a non segregated control section were tested to evaluate the applicability of the laser texture method to detect and quantify segregation. Laser texture data was gathered from all three sites, and the sand patch and nuclear density tests were performed in three sections at intervals of 25 feet. In addition to the above, visual surveys were performed to confirm the measurements. The poor correlation between the nuclear density and the texture from the sand patch method for test sections suggested that nuclear density test should not be used as a quantitative method to predict segregation. The nuclear density measurements can locate the areas of low density, which is a volume measurement, and may be due to segregation. Whereas, the sand patch test can locate areas of segregation, as indicated by surface texture. Hence a poor correlation between the nuclear density and the texture from sand patch method is expected. Since low nuclear density measurements may be due to segregation, the high nuclear density values for the control section suggested that nuclear density measurements might be used as a confirmation test.

The laser texture data showed the presence of segregation with consistent texture peaks that occurred at approximately 100-feet intervals. Test results from the control

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section were used to establish a correlation between the sand patch test (a quantitative test to determine segregation) and the laser texture data. A computer program, NJTxtr, to detect and monitor segregation was developed by combining the above concepts. This program uses the ARAN-collected pavement texture data and determines the acceptability of the pavement section based on the level of segregation present within the pavement section. Ratios of texture in segregated areas to that in non-segregated areas were set for detection and monitoring of different levels of segregation.

By

combining levels of segregation and the extent of each level of segregation, an AREA index can be developed to determine the acceptance or non-acceptance of a pavement section. When a pavement section is acceptable, the software determines the pay adjustment factor to be used. Remedial actions are suggested when segregation is present. NJTxtr was evaluated using data collected from one non segregated control section and two segregated sections and found to be satisfactory based on predictions from NJTxtr with visual observations.

Problem Statement The correlation between air voids and pavement durability is well documented. Some projects have experienced high air voids and segregation of the surface mixes due to poor construction practices or equipment problems. By establishing a relationship between surface texture measurements, surface segregation, and air voids, the NJDOT will have a screening tool to identify variations in surface texture that are typical of segregation and potentially locate pavement sections with high air voids.

The project evaluated the current technology to develop a screening tool for assessing surface texture as a means of locating segregated areas of the surface pavement and potentially high air void locations. The proposed technology was compared to another method that measures texture, namely the sand patch test, to establish acceptance limits. Once segregation was located, cores from the pavement layer were taken and used to determine a correlation of these areas compared to the average air void content for the lot.

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Objectives The objectives of this study were to: 1. Develop a vehicle-mounted screening device to measure variations in surface texture that are typical for segregated sections of pavement. 2. Correlate the air voids and density of these sections compared to the average texture for the lot. 3. Recommend development of the NJDOT specifications for implementation of the surface texture measurement methods.

The technical approach to the work included specific tasks that are identified as follows: 1. Literature search. 2. Compare air voids and density from cores taken from segregated areas to the remainder of tested lots. •

Develop laboratory correlations between surface texture, segregation, and measurement of air voids.

•

Field evaluation of laboratory correlations between surface texture, segregation, and measurement of air voids.

3. Evaluate current technology to develop a screening device that can: a. Measure surface texture from a moving vehicle, b. Locate segregated sections of pavement surface, •

Benchmark results against standardized techniques.

•

Assess accuracy, repeatability, and reliability of collected data.

•

Perform testing that replicates field conditions.

4. Develop the standard Materials Procedure (MP) for testing.

Introduction Segregation may be defined as the lack of homogeneity of constituents in Hot Mix Asphalt concrete (HMA) pavements that accelerates pavement distresses. Constituents of HMA are asphalt cement, aggregates, additives, and air voids. Segregation produces repetitive patterns of non-uniformity. Therefore, standard quality control/quality assurance (QC/QA) procedures that randomly define sampling locations would have a

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low probability of adequately identifying this problem. Ideally, some type of longitudinal pavement profile, using one or more nondestructive measurements at selected transverse locations, can be identified. An alternative methodology is needed to address random but localized areas of non-uniformity.

The most common HMA segregation has been identified as gradation segregation. Gradation segregation is the non-uniform distribution of coarse and fine aggregate materials in the finished HMA pavements. Gradation segregation can occur as the result of aggregate stockpiling and handling, production, storage, truck-loading practices, construction practices, and equipment adjustments. Localized pavement areas rich in coarse aggregate are typically associated with high air voids and low asphalt contents. These conditions can lead to moisture damage as well as to durability-related pavement distresses such as fatigue cracking, pothole formation, and raveling. Conversely, pavement areas rich in fine aggregate are associated with low air voids and high asphalt contents, making them susceptible to rutting.

There are several traditional and emerging methods to detect and quantify texture, so that a quality control/quality assurance program can be built into the design and construction of HMA pavements. Following is a description of those methods. Traditional Methods for Detecting Segregation: Visual identification: Visual identification of non-uniform surface texture has been used to locate segregation (Brook et al., 1996). This is a subjective approach, which can lead to disagreements between the agency and representatives of the contractor. Usually visual detection of non-uniform areas is used as the baseline for other quantitative approaches. Cross et al., 1997, studied four Kansas field projects with suspected segregation problems. They concluded that visual observations are better able to identify segregation in mixtures with larger aggregate sizes and coarser (below the maximum density line) gradations, but it was difficult to visually identify segregation for mixtures with smaller sized aggregates and finer gradations.

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Sand Patch Testing: The sand patch test method has been used to quantify visual observations of differences in the surface macro-texture. The ASTM E965 test method (ASTM 2001) indicates that the precision of the test method is approximately one percent of the measured depth in millimeters and the operator variation is about two percent. Good correlation was found between visual observations of non-uniform textured areas and the sand patch test results for measuring surface macro-texture. Nuclear Density Gauges: These gauges can be used to identify segregated areas by profiling the longitudinal density of the pavement mats. An assumption is made that segregation will be seen as low density. However, literature indicates limited success for this method. There are two reasons: First, a common assumption for using these gauges is that density decreases with increasingly coarse aggregate segregation. However, this assumption does not consider the relationship of the gradation to the maximum density line. If the Job Mix Formula (JMF) begins above this line, separation of the coarse aggregate in this type of mix may result in a higher density as the gradation shifts toward the maximum density line. Second, different types of aggregates have different effects on gauge variability. If a mixture is composed of a mixture of different aggregate types the change in testing variability in coarse aggregate-rich and fine aggregate-rich areas may make it difficult to adequately detect or measure segregation or both. Of the three traditional methods described above, the sand patch test seems to be most objective and accurate method to detect texture and hence it was used in this research. Innovative Technologies for Detecting Segregation: Three new technologies have been identified as having potential to selectively identify HMA segregation: Thermal Imaging: All objects emit infrared radiation in the form of heat, which can be detected by an infrared scanner. These natural impulses are converted into electrical pulses and then processed to create a visual image of the object's thermal energy.

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The colors used to represent the thermal imaging can be user-selected to represent surface temperature changes, such as blue for colder regions and red for warmer regions. The thermal imaging technology will indicate high-void regions, as thermal capacity of air is minimal compared to that of aggregates and asphalt cements. If one assumes that high segregation causes high void ratios, then the technology can be easily adopted to detect segregation. The primary component of any thermal imaging system is an optical scanner, a unit that is used to detect infrared radiation from an object. Other essential components of the system are a display monitor, a video camera, and a computer with appropriate software for data acquisition, analysis, and storage. Minimum resolution requirements and the height of the equipment above the surface determine the area surveyed by the camera. A full-lane width can be surveyed at one time with an appropriately placed camera. Usually liquid-nitrogen-cooled scanners provide improved resolution over other types of scanners. Although the current technology is vehicle-mounted, operation at highway speeds (>80 kph or 50 mph)) tends to blur the image. Resolution is improved substantially by operating the equipment at slower speeds (