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REPORT 
This report is an archived publication and may contain dated technical, contact, and link information 

Publication Number: FHWAHRT12031 Date: August 2012 
Publication Number:
FHWAHRT12031
Date: August 2012 
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Material characterization is a basic aspect of pavement engineering and is critical for analysis, performance prediction, design, construction, quality control/quality assurance, pavement management, and rehabilitation. Advanced tools like the American Association of State Highway and Transportation Officials MechanisticEmpirical Pavement Design Guide, Interim Edition: A Manual of Practice, commonly known as the MEPDG, can be used to estimate the influence of several fundamental engineering material parameters on the longterm performance of a pavement.(1) Consequently, there is a great need for more information about material properties, which are addressed only to a limited extent with currently available resources for performing laboratory and field testing. Reliable correlations between material parameters and index properties offer a costeffective alternative, and the derived material property values are equivalent to the level 2 inputs in the MEPDG. This study initially verified data adequacy in the LongTerm Pavement Performance (LTPP) database and also involved retrieving needed data.(2) In the next phase of the study, prediction models were developed to help practicing engineers estimate proper MEPDG inputs. This report describes the basis for selecting material parameters that need predictive models, provides a review of current LTPP program data, and proposes several statistically derived models to predict material properties. The models developed under this effort have been incorporated into a simple software program compatible with current versions of Microsoft Windows^{®} operating system.
Jorge E. PagánOrtiz
Director, Office of Infrastructure
Research and Development
This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document. This report does not constitute a standard, specification, or regulation.
The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this report only because they are considered essential to the objective of the document.
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The Federal Highway Administration (FHWA) provides highquality information to serve Government, industry, and the public in a manner that promotes public understanding. FHWA uses standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. It also periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement.
1. Report No. FHWAHRT12031 
2. Government Accession No. 
3. Recipient's Catalog No. 

4. Title and Subtitle User’s Guide: Estimation of Key PCC, Base, Subbase, and Pavement Engineering Properties from Routine Tests and Physical Characteristics 
5. Report Date August 2012 

6. Performing Organization Code


7. Author(s) C. Rao, L. TitusGlover, B. Bhattacharya, and M.I. Darter 
8. Performing Organization Report No. 

9. Performing Organization Name and Address Applied Research Associates, Inc. 100 Trade Centre Drive, Suite 200 Champaign, IL 61820 
10. Work Unit No. (TRAIS)


11. Contract or Grant No. DTFH6102C00138 

12. Sponsoring Agency Name and Address Office of Infrastructure Research and Development Federal Highway Administration 6300 Georgetown Pike McLean, VA 221012296 
13. Type of Report and Period Covered Interim Report 

14. Sponsoring Agency Code 

15. Supplementary Notes The Contracting Officer’s Technical Representative (COTR) was Larry Wiser, HRDI LTPP data analysis contract. 

16. Abstract Material characterization is a critical component of modern day pavement analysis, design, construction, quality control/quality assurance, management, and rehabilitation. At each stage during the life of a project, the influence of several fundamental engineering material parameters on the longterm performance of the pavement can be predicted using advanced tools like the American Association of State Highway and Transportation Officials MechanisticEmpirical Pavement Design Guide (MEPDG). Consequently, there is a need for more information about material properties, which are addressed only to a limited extent with currently available resources for performing laboratory and field testing. Reliable correlations between material parameters and index properties offer a costeffective alternative and are equivalent to the level 2 MEPDG inputs. The LongTerm Pavement Performance (LTPP) database provides data suitable for developing predictive models for Portland cement concrete (PCC) materials, stabilized materials, and unbound materials, as well as other designrelated inputs for the MEPDG. This user’s guide provides a summary of the models developed, describes their applications for specific project conditions, and lists their limitations. The following models are included:


17. Key Words Pavements, LTPP, material properties, MEPDG, prediction model, Index properties 
18. Distribution Statement No restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161. 

19. Security Classification (of this report) Unclassified 
20. Security Classification (of this page) Unclassified 
21. No. of Pages 86 
22. Price 

Form DOT F 1700.7 (872) Reproduction of completed page authorized
SI* (Modern Metric) Conversion Factors
Practical Guide and Software Program
PCC Compressive Strength Models
Compressive Strength Model 1: 28day Cylinder Strength Model
Compressive Strength Model 2: ShortTerm Cylinder Strength Model
Compressive Strength Model 3: ShortTerm Core Strength Model
Compressive Strength Model 4: All Ages Core Strength Model
Compressive Strength Model 5: LongTerm Core Strength Model
Relative Comparison of All Compressive Strength Models
Flexural Strength Model 1: Flexural Strength Based on Compressive Strength
Flexural Strength Model 2: Flexural Strength Based on Age, Unit Weight, and w/c Ratio
Flexural Strength Model 3: Flexural Strength Based on Age, Unit Weight, and CMC
Elastic Modulus Model 1: Model Based on Aggregate Type
Elastic Modulus Model 2: Model Based on Age and Compressive Strength
Elastic Modulus Model 3: Model Based on Age and 28Day Compressive Strength
Limitations of Elastic Modulus Models
PCC Tensile Strength Model Based on Compressive Strength
Current Issue with CTE Overestimation in LTPP Data
CTE Model 1: CTE Based on Aggregate Type (Level 3 Equation for MEPDG)
CTE Model 2: CTE Based on Mix Volumetrics (Level 2 Equation for MEPDG)
CHAPTER 4. RIGID PAVEMENT DESIGN FEATURES MODELS
CHAPTER 5. Stabilized Materials Models
CHAPTER 6. UNBOUND MATERIALS MODELS
Resilient Modulus of Unbound Materials
Constitutive Model Parameter k_{1}
Constitutive Model Parameter k_{2}
Constitutive Model Parameter k_{3}
Figure 1. Illustration. MEPDG performance prediction during the design and construction stage
Figure 2. Screenshot. View of Correlations user interface
Figure 3. Equation. Prediction model 1 for f_{c,}_{28d}
Figure 4. Graph. Predicted versus measured for 28day cylinder compressive strength model
Figure 5. Graph. Residual error plot for 28day cylinder compressive strength model
Figure 6. Graph. 28day compressive strength model sensitivity to w/c ratio
Figure 7. Graph. 28day compressive strength model sensitivity to CMC
Figure 8. Equation. Prediction model 2 for f_{c,t}
Figure 9. Graph. Predicted versus measured for shortterm cylinder compressive strength model
Figure 10. Graph. Residual errors for shortterm cylinder compressive strength model
Figure 11. Graph. Shortterm cylinder compressive strength sensitivity to CMC
Figure 12. Graph. Shortterm cylinder compressive strength sensitivity to w/c ratio
Figure 13. Graph. Shortterm cylinder compressive strength sensitivity to age
Figure 14. Equation. Prediction model 3 for f_{c,t}
Figure 15. Graph. Predicted versus measured for shortterm core compressive strength model
Figure 16. Graph. Residual errors for shortterm core compressive strength model
Figure 17. Graph. Shortterm core compressive strength sensitivity to CMC
Figure 18. Graph. Shortterm core compressive strength sensitivity to unit weight
Figure 19. Graph. Shortterm core compressive strength sensitivity to MAS
Figure 20. Graph. Shortterm core compressive strength sensitivity to w/c ratio
Figure 21. Graph. Shortterm core compressive strength sensitivity to fine aggregate FM
Figure 22. Graph. Shortterm core compressive strength sensitivity to age
Figure 23. Equation. Prediction model 4 for f_{c,t}
Figure 24. Graph. Predicted versus measured for all ages core compressive strength model
Figure 25. Graph. Residual errors for all ages core compressive strength model
Figure 26. Graph. All ages core compressive strength sensitivity to w/c ratio
Figure 27. Graph. All ages core compressive strength sensitivity to CMC
Figure 28. Graph. All ages core compressive strength sensitivity to unit weight
Figure 29. Graph. All ages core compressive strength sensitivity to age
Figure 30. Equation. Prediction model 5 for f_{c,LT}
Figure 31. Graph. Predicted versus measured for longterm core compressive strength model
Figure 32. Graph. Residual errors for longterm core compressive strength model
Figure 33. Graph. Model compressive strength prediction for varying CMC
Figure 34. Graph. Model compressive strength prediction for varying w/c ratio
Figure 35. Graph. Model compressive strength prediction for varying unit weights
Figure 36. Graph. Strength gain in the shortterm predicted by three models
Figure 37. Graph. Longterm strength gain predicted by the models
Figure 39. Graph. Predicted versus measured for validating 0.5 power flexural strength model
Figure 40. Graph. Predicted versus measured for validating 0.667 power flexural strength model
Figure 41. Equation. Prediction model 6 for MR
Figure 43. Graph. Residual errors for flexural strength model based on compressive strength
Figure 44. Graph. Comparison of flexural strength models based on compressive strength
Figure 45. Equation. Prediction model 7 for MR_{t}
Figure 48. Equation. Prediction model 8 for MR_{t}
Figure 50. Graph. Residual errors for flexural strength model based on age, unit weight, and CMC
Figure 51. Graph. Sensitivity of flexural strength predictions to CMC
Figure 52. Graph. Sensitivity of flexural strength predictions to w/c ratio
Figure 53. Graph. Sensitivity of flexural strength predictions to unit weight
Figure 54. Graph. Sensitivity of flexural strength predictions to age
Figure 55. Equation. E_{c} as a function of square root of compressive strength
Figure 56. Equation. Model form for E as a function of compressive strength with slope and intercept
Figure 58. Equation. E as function of unit weight and compressive strength
Figure 59. Equation. Prediction model 9 for E_{c}
Figure 60. Graph. Predicted versus measured for elastic modulus model based on aggregate type
Figure 61. Graph. Residual errors for elastic modulus model based on aggregate type
Figure 62. Equation. Prediction model 10 for E_{c,t}
Figure 64. Graph. Residual errors for elastic modulus model based on age and compressive strength
Figure 65. Equation. Prediction model 11 for E_{c,t}
Figure 68. Equation. Prediction model 12 for f_{t}
Figure 69. Graph. Predicted versus measured for tensile strength model
Figure 70. Graph. Residual errors plot for tensile strength model
Figure 71. Graph. Sensitivity of tensile strength prediction model to change compressive strength
Figure 73. Equation. Prediction model 14 for CTE_{PCC}
Figure 74. Graph. Predicted versus measured for CTE model based on mix volumetrics
Figure 75. Graph. Residual errors for CTE model based on mix volumetrics
Figure 78. Graph. Sensitivity of the CTE model to coarse aggregate content
Figure 79. Equation. Prediction model 15 for deltaT/inch
Figure 80. Graph. Predicted versus measured for JPCP deltaT gradient model
Figure 81. Graph. Residual errors for JPCP deltaT gradient model
Figure 82. Graph. Predicted versus measured deltaT based on the JPCP deltaT gradient model
Figure 83. Graph. Sensitivity of predicted deltaT to temperature range during month of construction
Figure 84. Graph. Sensitivity of predicted deltaT to slab width
Figure 85. Graph. Sensitivity of predicted deltaT to slab thickness
Figure 86. Graph. Sensitivity of predicted deltaT to PCC slab unit weight
Figure 87. Graph. Sensitivity of predicted deltaT to PCC w/c ratio
Figure 88. Graph. Sensitivity of predicted deltaT to latitude of the project location
Figure 89. Graph. Predicted deltaT for different locations in the United States
Figure 90. Equation. Prediction model 16 for deltaT/inch
Figure 91. Graph. Predicted versus measured for CRCP deltaT model
Figure 92. Graph. Residual errors for CRCP deltaT model
Figure 93. Graph. Effect of maximum temperature on CRCP deltaT prediction model
Figure 94. Graph. Effect of temperature range on CRCP deltaT prediction model
Figure 95. Graph. Effect of slab thickness on CRCP deltaT prediction model
Figure 96. Graph. Effect of geographic location on CRCP deltaT prediction model
Figure 97. Equation. Prediction model 17 for E_{LCB}
Figure 98. Graph. Predicted versus measured for the LCB elastic modulus model
Figure 99. Graph. Residual errors for the LCB elastic modulus model
Figure 101. Equation. Prediction model 18 for k_{1}
Figure 102. Equation. Prediction model 19 for k_{2}
Figure 103. Equation. Prediction model 20 for k_{3}
Figure 109. Graph. Effect of material type (AASHTO soil class) on predicted resilient modulus
Figure 110. Graph. Effect of percent passing ^{1}/_{2}inch sieve on predicted resilient modulus
Figure 111. Graph. Effect of liquid limit on predicted resilient modulus
Figure 112. Graph. Effect of optimum moisture content on predicted resilient modulus
Figure 113. Graph. Effect of No. 80 sieve on predicted resilient modulus
Figure 114. Graph. Effect of gravel content on predicted resilient modulus
Figure 115. Graph. Effect of effective size on predicted resilient modulus
Table 1. Regression statistics for selected prediction model for 28day PCC cylinder strength
Table 2. Range of data used for 28day PCC cylinder strength
Table 3. Regression statistics for shortterm cylinder strength model
Table 4. Range of data used for shortterm cylinder strength model
Table 5. Regression statistics for shortterm core strength model
Table 6. Range of data used for shortterm core strength model
Table 7. Regression statistics for all ages core strength model
Table 8. Range of data used for all ages core strength model
Table 9. Regression statistics for longterm core strength model
Table 10. Range of data used for longterm core strength model
Table 11. Power models developed for flexural strength prediction using LTPP data for validation
Table 12. Regression statistics for flexural strength model based on compressive strength
Table 13. Range of data used for flexural strength model based on compressive strength
Table 14. Regression statistics for flexural strength model based on age, unit weight, and w/c ratio
Table 15. Range of data used for flexural strength model based on age, unit weight, and w/c ratio
Table 16. Regression statistics for flexural strength model based on age, unit weight, and CMC
Table 17. Range of data used for flexural strength model based on age, unit weight, and CMC
Table 18. Range of data used for elastic modulus model based on aggregate type
Table 19. Regression statistics for elastic modulus model based on age and compressive strength
Table 20. Range of data used for elastic modulus model based on age and compressive strength
Table 22. Range of data used for elastic modulus model based on age and 28day compressive strength
Table 23. Model statistics for tensile strength prediction model
Table 24. Range of data used for tensile strength prediction model
Table 25. Prediction model 13 for PCC CTE based on aggregate type, x 10^{6} inch/inch/°F
Table 26. Statistical analysis results for CTE model based on mix volumetrics
Table 27. Range of data used for CTE model based on mix volumetrics
Table 28. Regression statistics for JPCP deltaT model
Table 29. Range of data used for JPCP deltaT model
Table 30. Regression statistics for CRCP deltaT model
Table 31. Range of data used for CRCP deltaT model