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Publication Number: FHWA-HRT-12-031
Date: August 2012

 

User’s Guide: Estimation of Key PCC, Base, Subbase, and Pavement Engineering Properties From Routine Tests and Physical Characteristics

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FOREWORD

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 Mechanistic-Empirical 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 long-term 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 cost-effective 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 Long-Term 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. Pagán-Ortiz
Director, Office of Infrastructure
Research and Development

Notice

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.

Quality Assurance Statement

The Federal Highway Administration (FHWA) provides high-quality 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.

 

Technical Report Documentation Page

1. Report No.

FHWA-HRT-12-031

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. Titus-Glover, 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.

DTFH61-02-C-00138

12. Sponsoring Agency Name and Address

Office of Infrastructure Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, VA 22101-2296

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 long-term performance of the pavement can be predicted using advanced tools like the American Association of State Highway and Transportation Officials Mechanistic-Empirical 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 cost-effective alternative and are equivalent to the level 2 MEPDG inputs. The Long-Term 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 design-related 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:

  • PCC materials: Compressive strength, flexural strength, elastic modulus, tensile strength, and coefficient of thermal expansion.
  • Stabilized materials: Elastic modulus of lean concrete base.
  • Unbound materials: Resilient modulus of fine-grained and coarse-grained materials.
  • Rigid pavement design features: Pavement curl/wrap effective temperature difference for jointed plain concrete pavement and continuously reinforced concrete pavement designs.

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 (8-72) Reproduction of completed page authorized

Table of Contents


SI* (Modern Metric) Conversion Factors

CHAPTER 1. INTRODUCTION.. 1

Background.. 1

Data Needs. 1

Design. 2

Construction. 2

Pavement Management 4

Scope.. 4

CHAPTER 2. MODEL DEVELOPMENT.. 7

list of Models. 8

Practical Guide and Software Program... 10

CHAPTER 3. PCC MODELS. 11

PCC Compressive Strength Models. 11

Compressive Strength Model 1: 28-day Cylinder Strength Model 12

Compressive Strength Model 2: Short-Term Cylinder Strength Model 14

Compressive Strength Model 3: Short-Term Core Strength Model 17

Compressive Strength Model 4: All Ages Core Strength Model 21

Compressive Strength Model 5: Long-Term Core Strength Model 24

Relative Comparison of All Compressive Strength Models. 25

PCC Flexural Strength Models. 29

Validation of Existing Models. 29

Flexural Strength Model 1: Flexural Strength Based on Compressive Strength. 30

Flexural Strength Model 2: Flexural Strength Based on Age, Unit Weight, and w/c Ratio. 33

Flexural Strength Model 3: Flexural Strength Based on Age, Unit Weight, and CMC.. 35

PCC Elastic Modulus Models. 38

Validation of Existing Models. 38

Elastic Modulus Model 1: Model Based on Aggregate Type. 40

Elastic Modulus Model 2: Model Based on Age and Compressive Strength. 42

Elastic Modulus Model 3: Model Based on Age and 28-Day Compressive Strength. 43

Limitations of Elastic Modulus Models. 45

PCC Tensile Strength Models. 45

PCC Tensile Strength Model Based on Compressive Strength. 45

PCC CTE Models. 47

Current Issue with CTE Overestimation in LTPP Data. 47

CTE Model 1: CTE Based on Aggregate Type (Level 3 Equation for MEPDG) 48

CTE Model 2: CTE Based on Mix Volumetrics (Level 2 Equation for MEPDG) 49

CHAPTER 4. RIGID PAVEMENT DESIGN FEATURES MODELS. 53

deltaT—JPCP Design.. 53

Using the JPCP deltaT Model 59

deltaT—CRCP Design.. 60

Using the CRCP deltaT Model 64

 

CHAPTER 5. Stabilized Materials Models. 65

LCB Elastic Modulus Model.. 65

CHAPTER 6. UNBOUND MATERIALS MODELS. 67

Resilient Modulus of Unbound Materials. 67

Constitutive Model Parameter k1 67

Constitutive Model Parameter k2 67

Constitutive Model Parameter k3 68

REFERENCES. 75


List of Figures

Figure 1. Illustration. MEPDG performance prediction during the design and construction stage. 3

Figure 2. Screenshot. View of Correlations user interface. 10

Figure 3. Equation. Prediction model 1 for fc,28d 12

Figure 4. Graph. Predicted versus measured for 28-day cylinder compressive strength model 13

Figure 5. Graph. Residual error plot for 28-day cylinder compressive strength model 13

Figure 6. Graph. 28-day compressive strength model sensitivity to w/c ratio. 14

Figure 7. Graph. 28-day compressive strength model sensitivity to CMC.. 14

Figure 8. Equation. Prediction model 2 for fc,t 14

Figure 9. Graph. Predicted versus measured for short-term cylinder compressive strength model 15

Figure 10. Graph. Residual errors for short-term cylinder compressive strength model 16

Figure 11. Graph. Short-term cylinder compressive strength sensitivity to CMC.. 16

Figure 12. Graph. Short-term cylinder compressive strength sensitivity to w/c ratio. 16

Figure 13. Graph. Short-term cylinder compressive strength sensitivity to age. 17

Figure 14. Equation. Prediction model 3 for fc,t 17

Figure 15. Graph. Predicted versus measured for short-term core compressive strength model 18

Figure 16. Graph. Residual errors for short-term core compressive strength model 18

Figure 17. Graph. Short-term core compressive strength sensitivity to CMC.. 19

Figure 18. Graph. Short-term core compressive strength sensitivity to unit weight 19

Figure 19. Graph. Short-term core compressive strength sensitivity to MAS. 19

Figure 20. Graph. Short-term core compressive strength sensitivity to w/c ratio. 20

Figure 21. Graph. Short-term core compressive strength sensitivity to fine aggregate FM... 20

Figure 22. Graph. Short-term core compressive strength sensitivity to age. 20

Figure 23. Equation. Prediction model 4 for fc,t 21

Figure 24. Graph. Predicted versus measured for all ages core compressive strength model 22

Figure 25. Graph. Residual errors for all ages core compressive strength model 22

Figure 26. Graph. All ages core compressive strength sensitivity to w/c ratio. 23

Figure 27. Graph. All ages core compressive strength sensitivity to CMC.. 23

Figure 28. Graph. All ages core compressive strength sensitivity to unit weight 23

Figure 29. Graph. All ages core compressive strength sensitivity to age. 24

Figure 30. Equation. Prediction model 5 for fc,LT. 24

Figure 31. Graph. Predicted versus measured for long-term core compressive strength model 25

Figure 32. Graph. Residual errors for long-term core compressive strength model 25

Figure 33. Graph. Model compressive strength prediction for varying CMC.. 26

Figure 34. Graph. Model compressive strength prediction for varying w/c ratio. 26

Figure 35. Graph. Model compressive strength prediction for varying unit weights. 27

Figure 36. Graph. Strength gain in the short-term predicted by three models. 27

Figure 37. Graph. Long-term strength gain predicted by the models. 28

Figure 38. Equation. Mr 29

Figure 39. Graph. Predicted versus measured for validating 0.5 power flexural strength model 30

Figure 40. Graph. Predicted versus measured for validating 0.667 power flexural strength model 30

Figure 41. Equation. Prediction model 6 for MR.. 30

Figure 42. Graph. Predicted versus measured values for flexural strength model based on compressive strength  31

Figure 43. Graph. Residual errors for flexural strength model based on compressive strength. 32

Figure 44. Graph. Comparison of flexural strength models based on compressive strength. 33

Figure 45. Equation. Prediction model 7 for MRt 33

Figure 46. Graph. Predicted versus measured values for flexural strength model based on age, unit weight, and w/c ratio. 34

Figure 47. Graph. Residual errors for flexural strength model based on age, unit weight, and w/c ratio. 35

Figure 48. Equation. Prediction model 8 for MRt 35

Figure 49. Graph. Predicted versus measured values for flexural strength model based on age, unit weight, and CMC.. 36

Figure 50. Graph. Residual errors for flexural strength model based on age, unit weight, and CMC.. 36

Figure 51. Graph. Sensitivity of flexural strength predictions to CMC.. 37

Figure 52. Graph. Sensitivity of flexural strength predictions to w/c ratio. 37

Figure 53. Graph. Sensitivity of flexural strength predictions to unit weight 38

Figure 54. Graph. Sensitivity of flexural strength predictions to age. 38

Figure 55. Equation. Ec as a function of square root of compressive strength. 38

Figure 56. Equation. Model form for E as a function of compressive strength with slope and intercept 39

Figure 57. Equation. Ec 39

Figure 58. Equation. E as function of unit weight and compressive strength. 39

Figure 59. Equation. Prediction model 9 for Ec 40

Figure 60. Graph. Predicted versus measured for elastic modulus model based on aggregate type. 41

Figure 61. Graph. Residual errors for elastic modulus model based on aggregate type. 41

Figure 62. Equation. Prediction model 10 for Ec,t 42

Figure 63. Graph. Predicted versus measured for elastic modulus model based on age and compressive strength. 43

Figure 64. Graph. Residual errors for elastic modulus model based on age and compressive strength  43

Figure 65. Equation. Prediction model 11 for Ec,t 43

Figure 66. Graph. Predicted versus measured for elastic modulus model based on age and 28-day compressive strength. 44

Figure 67. Graph. Residual errors for elastic modulus model based on age and 28-day compressive strength  45

Figure 68. Equation. Prediction model 12 for ft 45

Figure 69. Graph. Predicted versus measured for tensile strength model 46

Figure 70. Graph. Residual errors plot for tensile strength model 46

Figure 71. Graph. Sensitivity of tensile strength prediction model to change compressive strength. 47

Figure 72. Graph. Comparison of average values from other sources and recommended CTE values based on aggregate type from LTPP data. 49

Figure 73. Equation. Prediction model 14 for CTEPCC. 49

Figure 74. Graph. Predicted versus measured for CTE model based on mix volumetrics. 50

Figure 75. Graph. Residual errors for CTE model based on mix volumetrics. 50

Figure 76. Graph. Comparison of CTE model prediction with average values reported in literature for each aggregate rock type. 51

Figure 77. Graph. CTE model prediction versus average values reported in literature for each aggregate rock type  51

Figure 78. Graph. Sensitivity of the CTE model to coarse aggregate content 52

Figure 79. Equation. Prediction model 15 for deltaT/inch. 53

Figure 80. Graph. Predicted versus measured for JPCP deltaT gradient model 54

Figure 81. Graph. Residual errors for JPCP deltaT gradient model 55

Figure 82. Graph. Predicted versus measured deltaT based on the JPCP deltaT gradient model 55

Figure 83. Graph. Sensitivity of predicted deltaT to temperature range during month of construction  56

Figure 84. Graph. Sensitivity of predicted deltaT to slab width. 56

Figure 85. Graph. Sensitivity of predicted deltaT to slab thickness. 56

Figure 86. Graph. Sensitivity of predicted deltaT to PCC slab unit weight 57

Figure 87. Graph. Sensitivity of predicted deltaT to PCC w/c ratio. 57

Figure 88. Graph. Sensitivity of predicted deltaT to latitude of the project location. 57

Figure 89. Graph. Predicted deltaT for different locations in the United States. 58

Figure 90. Equation. Prediction model 16 for deltaT/inch. 60

Figure 91. Graph. Predicted versus measured for CRCP deltaT model 61

Figure 92. Graph. Residual errors for CRCP deltaT model 62

Figure 93. Graph. Effect of maximum temperature on CRCP deltaT prediction model 62

Figure 94. Graph. Effect of temperature range on CRCP deltaT prediction model 63

Figure 95. Graph. Effect of slab thickness on CRCP deltaT prediction model 63

Figure 96. Graph. Effect of geographic location on CRCP deltaT prediction model 63

Figure 97. Equation. Prediction model 17 for ELCB. 65

Figure 98. Graph. Predicted versus measured for the LCB elastic modulus model 65

Figure 99. Graph. Residual errors for the LCB elastic modulus model 66

Figure 100. Equation. Mr 67

Figure 101. Equation. Prediction model 18 for k1 67

Figure 102. Equation. Prediction model 19 for k2 67

Figure 103. Equation. Prediction model 20 for k3 68

Figure 104. Graph. Resilient modulus parameter k1 for unbound material types included in the model development database. 68

Figure 105. Graph. Resilient modulus parameter k2 for unbound material types included in the model development database. 69

Figure 106. Graph. Resilient modulus parameter k3 for unbound material types included in the model development database. 69

Figure 107. Graph. Plot of measured versus predicted resilient modulus (using k1, k2, and k3 derived from figure 101 through figure 103) 70

Figure 108. Graph. Plot showing predicted and measured resilient modulus versus bulk stress for fine- and coarse-grained soils. 70

Figure 109. Graph. Effect of material type (AASHTO soil class) on predicted resilient modulus. 71

Figure 110. Graph. Effect of percent passing 1/2-inch sieve on predicted resilient modulus. 72

Figure 111. Graph. Effect of liquid limit on predicted resilient modulus. 72

Figure 112. Graph. Effect of optimum moisture content on predicted resilient modulus. 73

Figure 113. Graph. Effect of No. 80 sieve on predicted resilient modulus. 73

Figure 114. Graph. Effect of gravel content on predicted resilient modulus. 74

Figure 115. Graph. Effect of effective size on predicted resilient modulus. 74


List of Tables

Table 1. Regression statistics for selected prediction model for 28-day PCC cylinder strength. 12

Table 2. Range of data used for 28-day PCC cylinder strength. 12

Table 3. Regression statistics for short-term cylinder strength model 15

Table 4. Range of data used for short-term cylinder strength model 15

Table 5. Regression statistics for short-term core strength model 17

Table 6. Range of data used for short-term core strength model 18

Table 7. Regression statistics for all ages core strength model 21

Table 8. Range of data used for all ages core strength model 21

Table 9. Regression statistics for long-term core strength model 24

Table 10. Range of data used for long-term core strength model 24

Table 11. Power models developed for flexural strength prediction using LTPP data for validation. 29

Table 12. Regression statistics for flexural strength model based on compressive strength. 31

Table 13. Range of data used for flexural strength model based on compressive strength. 31

Table 14. Regression statistics for flexural strength model based on age, unit weight, and w/c ratio. 34

Table 15. Range of data used for flexural strength model based on age, unit weight, and w/c ratio. 34

Table 16. Regression statistics for flexural strength model based on age, unit weight, and CMC.. 35

Table 17. Range of data used for flexural strength model based on age, unit weight, and CMC.. 36

Table 18. Range of data used for elastic modulus model based on aggregate type. 41

Table 19. Regression statistics for elastic modulus model based on age and compressive strength. 42

Table 20. Range of data used for elastic modulus model based on age and compressive strength. 42

Table 21. Regression statistics for elastic modulus model based on age and 28-day compressive strength. 44

Table 22. Range of data used for elastic modulus model based on age and 28-day compressive strength. 44

Table 23. Model statistics for tensile strength prediction model 46

Table 24. Range of data used for tensile strength prediction model 46

Table 25. Prediction model 13 for PCC CTE based on aggregate type, x 10-6 inch/inch/°F. 48

Table 26. Statistical analysis results for CTE model based on mix volumetrics. 49

Table 27. Range of data used for CTE model based on mix volumetrics. 50

Table 28. Regression statistics for JPCP deltaT model 54

Table 29. Range of data used for JPCP deltaT model 54

Table 30. Regression statistics for CRCP deltaT model 60

Table 31. Range of data used for CRCP deltaT model 61



 


The Federal Highway Administration (FHWA) is a part of the U.S. Department of Transportation and is headquartered in Washington, D.C., with field offices across the United States. is a major agency of the U.S. Department of Transportation (DOT).
The Federal Highway Administration (FHWA) is a part of the U.S. Department of Transportation and is headquartered in Washington, D.C., with field offices across the United States. is a major agency of the U.S. Department of Transportation (DOT). Provide leadership and technology for the delivery of long life pavements that meet our customers needs and are safe, cost effective, and can be effectively maintained. Federal Highway Administration's (FHWA) R&T Web site portal, which provides access to or information about the Agency’s R&T program, projects, partnerships, publications, and results.
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