Top-Down Cracking of Hot-Mix Asphalt Layers: Models for Initiation and Propagation (2010) / Chapter Skim
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85 CHAPTER 4 FINDINGS: THE HMA-FM-BASED SYSTEM The primary feature of the HMA-FM-based crack propagation model was to account for effects of macro cracks during crack propagation. This model was comprised of the following key elements: 1.
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86 Figure 0-1. Framework of the top-down cracking performance model 4.1.1 Inputs Module and Indirect Tensile Test The inputs module provides pavement material and structural properties, temperatures within HMA layer (as predicted using an enhanced integrated climatic model)
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87 Table 0-1. Input required for sub-models of the HMA-FM-based system Sub-model Sub-model component Input requirement Material property model AC stiffness aging model - Basic mixture characteristics (gradation, binder type, mix volumetrics)
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88 Table 0-1. Continued Sub-model Sub-model component Input requirement Pavement fracture model Crack initiation model - Load and thermal-induced stresses (from response models)
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89 The Superpave indirect tensile test (IDT) developed as part of the Strategic Highway Research Program (SHRP)
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90 where Sf is the tensile stiffness at a loading time of 1800 seconds that can be obtained from the AC stiffness aging model by considering a reduction factor from compression to tension. The constants an are as follows: 0 1 2 3 4 5 a 284.01, a 330.02, a 151.02, a 34.03, a 3.7786, a 0.1652 = = − = = − = = − The FE limit surface aging model was conceived and expressed in the following form: ( )
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91 The development of a healing model was completed in two steps. First, a mixture level healing model was obtained based on the research by Kim and Roque (32)
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92 The daily normalized healing parameter depends on depth, time, and temperature. In this study, hdn was correlated with the daily lowest stiffness (Slow)
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93 surface were then predicted using 3-dimensional (3-D) linear-elastic analyses (LEA)
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95 were estimated by applying the stress intensity factor (SIF) of an edge crack to the thermal stresses predicted using the thermal response model.
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96 4.1.5 Outputs The outputs for the predictive system are presented in the forms of (1) crack depth versus time and/or (2)
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97 D is tr es s Loads/Age Failure Critical Condition Crack Initiation Before Onset of Crack Crack Propagation Figure 0-2. Critical conditions for crack initiation and propagation Figure 0-3.
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98 a 3-D LEA program. The CCI module is called to compute the amount of induced damage, as well as damage recovery and accumulation in a step-wise manner until the critical condition is identified (usually in several years)
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99 Knowing the initiation time and location of the initial crack, the CGS module starts by discretizing the pavement structure using 2-D displacement discontinuity boundary elements. The CCI module is then called to compute damage accumulation for each time step.
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100 parameters to evaluate these factors. To limit the number of runs, the following conditions were assumed: • A pavement structure used to demonstrate the thermal effect was selected.
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101 The values selected for each variable are listed in Table 0-2. For each variable, the value used in the example to demonstrate the thermal effect is indicated by shading.
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102 4.2.1 Effects of Material and Structural Properties The material and structural properties investigated included initial fracture energy, fracture energy aging parameter, binder viscosity, base modulus, and AC layer thickness. The influence of each is discussed in the following subsections.
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103 Figure 0-8. Maximum healing potential (surface)
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104 Figure 0-9. Effect of initial fracture energy on cracking performance 4.2.1.2 Effect of Fracture Energy Aging Parameter Fracture energy aging parameter k1 is an input parameter that governs the shape of the fracture energy aging curve.
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105 The effect of the fracture energy aging parameter is shown in Figure 0-11. As shown, the increase of k1 value results in longer time to crack initiation and a shallower slope of the crack growth curve.
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106 Figure 0-12. Effect of binder type on cracking performance 4.2.1.4 Effect of Base Modulus The effect of base modulus was investigated using the material properties listed in Table 0-3 and the values of base modulus given in Table 0-2.
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107 Figure 0-13. Effect of base modulus on cracking performance 4.2.1.5 Effect of AC Layer Thickness The following AC layer thicknesses were selected for this analysis (see Table 0-2)
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108 0.35 and 0.32 in./year. It can be seen from these comparisons that, in general, crack growth is slower in a thicker layer.
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109 Traffic Lane 12 ft Passing Lane Shoulder AC Layer (≥ 7.5 in.) (Potential)
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110 A comparison of Figure 0-16 and Figure 0-14 suggests that thick pavements may perform even worse than a pavement with a medium thickness AC layer. However, the simplified model does not address some potential factors affecting the near-tire mechanism, including the effects of the wander and stress state, which will result in less damage than predicted, and therefore, the comparison may not be accurate without considering these factors.
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111 Figure 0-17. Effect of traffic volume on cracking performance 4.2.3 Effect of Climate Three typical climatic environments were selected for this analysis: a hard-freeze (HF)
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112 Figure 0-18. Effect of climatic environment on cracking performance This finding was expected because the climatic environment influences damage development in the pavement in two ways.
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113 the colder climate because the pavement in the warmer climate is subjected to a higher creep rate for longer time and thus more damage. Figure 0-19.
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114 4.3.1.1 Selection of pavement sites Thirteen pavement sections were used for calibration/validation. These sections were selected based on quality of data in terms of both laboratory testing and field observation.
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115 The parametric study presented in Section 4.2 showed that AC layer thickness governs the cracking mechanism. For pavements with thin to medium thickness AC layers such as those of Group I (which ranges from 2 to 8 in.)
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116 Table 0-5. Data from SuperPave IDT resilient modulus and tensile strength tests at 10°C Section MR St εf FEf DCSEf Aged Time Code (Gpa)
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117 The AC stiffness aging model estimates the stiffness of the asphalt mixture as a function of temperature and time. The input information for this model is as follows: • Percent passing 3/4, 3/8, #4, and #200 sieves by weight • Binder type and mean annual air temperature (MAAT)
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118 I75-1A, which was 15 years old at the time of coring. Then, the FEi value of the mixture can be determined by using Equation 0-3, in which the normalized stiffness Sn(t)
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119 • The criterion for determination of yearly based healing, hyn, is identical to the criterion for daily based healing except an averaged daily lowest stiffness, Slowa, for a prolonged period was used instead of Slow to obtain the healing potential of any year. Details of these models are provided in Appendix B
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120 • The coefficient of thermal contraction of the asphalt concrete mixture The values used for thickness and hourly pavement temperatures are similar to those described for the load-response model. The coefficient of thermal contraction was assumed to be 1.2E-5 ε/°C for all test sections.
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121 The crack growth model uses the load-induced stresses predicted by the 2-D DDBE program, thermally induced stresses predicted by the thermal response model combined with the stress intensity function for an edge crack, and the energy-based failure criterion to compute the increase of crack depth with time. The mixture fracture and healing properties determined by the material property model are also required during the process.
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122 In Phase Two, predictions obtained using the calibrated model were presented and compared with field observations for all test sections, including those of Group II (also presented in Table 0-9) , which was obtained from Minnesota Department of Transportation (MnDOT)
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123 general message would be the same. In short, slight changes in these boundaries will not significantly affect model calibration/validation results.
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124 Table 0-10. Predicted versus observed cracking performance for different k1-values (all test sections in Group I)
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125 • Two of the two Level I sections were predicted to be level I sections. • Two of the three Level II sections were predicted to be Level II sections and one section was predicted to be a Level III section.
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126 Section 5 may have overwhelmed the sensitivity of the results. Therefore, Test Section 5 was excluded from the final calibration, but it was included in the validation process.
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127 Observed Cracking Performance I II III IV V Pr ed ic te d C ra ck in g Pe rf or m an ce I 2 II 2 1 III 1 2 IV 1 V 1 Figure 0-22. Predicted versus observed cracking performance levels (without Section 5)
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128 could not be obtained in the project and the available data set was small such that it could not be split, the PRESS method was selected for use in this study. 4.3.3.1 PRESS procedure In the PRESS procedure, the unknown parameters in the model are estimated when one data point is removed at a time from the data set (i.e., for a data set with n data points, the model is calibrated with (n-1)
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129 The validation process involved two steps. First, similar to the final calibration of the performance model presented in the Model Calibration section, Test Section 5 was excluded; only the other ten test sections were included.
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130 the observed initiation time to evaluate the predictive capability of the model. Results of this evaluation are presented in the following sub-section.
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131 independent predictions (using PRESS) for crack initiation time compared with predictions from the full model, which demonstrates the predictive ability of the model.
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132 Observed Cracking Performance Level I II III IV V Pr ed ic te d C ra ck in g Pe rf or m an ce L ev el I 2 1 II 2 1 III 1 2 IV 1 V 1 Figure 0-24. Predicted versus observed cracking performance levels (from PRESS)
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133 Table 0-13. Predicted crack depths versus time for an aging parameter (k1)
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134 Table 0-13. Continued Section I10-8 I10-9 SR471 SR19 SR997 I94-4 I94-14 Thickness (inch)
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135 4.3.4.1 Predicted versus observed cracking performance The predicted cracking performance for all test sections is compared with the observed performance in terms of both crack initiation time and cracking performance level in Figure 0-25. As shown, the predictions generally agree well with the observed performance levels (except for Section SR80-1 which is off by two levels)
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136 The crack propagation time determined for each test section using the calibrated model was plotted against the corresponding crack initiation time (Figure 0-26)
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137 4.3.4.3 Crack amount development with time The final model predictions are expressed in terms of crack amount versus time for each test section and presented according to the performance level (I to V) with respect to observed performance in Figures 0-27 to 0-30.
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138 Parametric studies have shown that the system can reasonably capture the effects of climate, traffic, and material and structural properties on top-down cracking performance. It was also shown that structural characteristics may define the form of possible top-down cracking mechanisms (bending mechanism − suitable for HMA layers with thin to medium thickness, and near-tire mechanism − dominant for thick HMA layers)
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139 SR19 0 50 100 150 200 250 300 350 400 0 2.5 5 7.5 10 Time (year)
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140 I10-8 0 50 100 150 200 250 300 350 400 0 2.5 5 7.5 10 12.5 15 17.5 20 Time (year)
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141 I75-1B 0 50 100 150 200 250 300 350 400 0 5 10 15 20 25 Time (year)
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142 SR80-2 0 50 100 150 200 250 300 350 400 0 5 10 15 20 25 30 35 40 45 Time (year)
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