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From page 140... ... 140 Materials Table 4 presents the materials selected for the systematic aging study. The selected component materials encompass different aggregate sources and binder types. Read the entire page →
From page 141... ... Section Asphalt Binder Grade/Modification Aggregate Type Climatic Region Project Mix ID LTPPSPS 8 South Dakota LSD 120-150 Pen Granite and Limestone Dry/Freeze Washington LWA AR-4000 N/A Wet/Non-Freeze Texas LTX AC-20 Granite and Limestone Wet/Non-Freeze FHWA ALF ALF-Control ACTRL 70-28/SBS Granite Wet/Non-FreezeALF-SBS ASBS 70-22 Granite SHRP AAD SAAD PG 58-28 Granite -AAG SAAG PG 58-10 Granite Note: LTPP-SPS is Long-Term Performance Program Specific Pavement Study; FHWA ALF is Federal Highway Administration Accelerated Load Facility; SHRP is Strategic Highway Research Program; LSD is LTPP South Dakota mix; LWA is LTPP Washington State mix; LTX is LTPP Texas mix; ACTRL is FHWA ALF Control mix; ASBS is FHWA ALF styrene-butadiene-styrene mix; SAAD is SHRP AAD mix; and SAAG is SHRP AAG. Table 4. Read the entire page →
From page 143... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 143 -3 -2 -1 0 1 2 3 0 15 30 45 LTX-STA LTX-4D LTX-8D LTX-17D -3 -2 -1 0 1 2 3 0 15 30 45 LSD-STA LSD-4D LSD-8D LSD-16D Temperature (°C) Temperature (°C) Read the entire page →
From page 144... ... 144 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results Sigmoidal Function Fit The logistic sigmoidal model presented by Witczak and his colleagues (Pellinen, Witczak, and Bonaquist 2004; Fonseca and Witczak 1996; Mirza and Witczak 1995) is applied in the Pavement ME Design program to model the behavior of asphalt mixtures. Read the entire page →
From page 145... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 145 ( ) Read the entire page →
From page 146... ... 146 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results determined, and second, these parameters must be able to provide the means to estimate the material's behavior for a wide range of temperatures or loading histories. A study of binder aging behavior suggests that the parameter M may be a suitable property that can indicate and define a binder's susceptibility to oxidative aging. Read the entire page →
From page 147... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 147 the behavior was not linear. It seems that the changes in values occurred rapidly at the beginning and became slower at longer aging durations. Read the entire page →
From page 148... ... 148 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results ACTRL LTX LSD LWA ASBS SAAD SAAG Binder M 0.743 0.880 0.747 0.865 0.623 1.104 0.572 Slope of β/γ 0.1638 0.1780 0.1624 0.1741 0.1168 0.1914 0.0918 Table 6. Binder M and sigmoidal parameters slopes with aging. Read the entire page →
From page 149... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 149 0 0.05 0.1 0.15 0.2 0.25 0.5 0.7 0.9 1.1 1.3 β/ γ Sl op e M Slope of β/γ Fit β/γ =-0.0123M-3.9005+0.1990 R2=0.9934 Figure 23. Slope of aging durations versus binder M Read the entire page →
From page 150... ... 150 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results than the linear correlation; however, the power function's constants differed significantly among the mixtures. Using the average of those constants created more problems in the shape of the predicted master curves compared to the linear correlations. Read the entire page →
From page 152... ... 152 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results 1.0E+4 1.0E+5 1.0E+6 1.0E+7 1.0E+8 1.0E-5 1.0E-3 1.0E-1 1.0E+1 1.0E+3 \|E * Read the entire page →
From page 153... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefcients 153 ASBS SAAD SAAG Reduced Freq. Read the entire page →
From page 154... ... 154 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results α1 values at different aging durations. When compared with actual α1 values, this approximation resulted in adequate predictions for the different mixtures. Read the entire page →
From page 155... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 155 ACTRL Actual LTX Actual STA 4D 8D 16D STA 4D 8D 17D Temp. Read the entire page →
From page 156... ... 156 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results Figure 35. Examples of broken aggregates in the crack surface for LTPP Texas. Read the entire page →
From page 157... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefcients 157 FHWA ALF Control y = 0.4516x y = 0.4555x y = 0.4356x y = 0.4053x 0.0E+0 2.0E+4 4.0E+4 6.0E+4 8.0E+4 0.E+00 5.E+04 1.E+05 2.E+05 2.E+05 Cu m ul at iv e (1 -C ) Nf (Cycle) Read the entire page →
From page 158... ... 158 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results LTPP South Dakota 0.0 0.2 0.4 0.6 0.8 1.0 0.0E+00 5.0E+04 1.0E+05 1.5E+05 2.0E+05 C S LSD-STA LSD-4D LSD-8D LSD-16D (a) Read the entire page →
From page 159... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 159 FHWA ALF SBS 0.0 0.2 0.4 0.6 0.8 1.0 0.0E+0 2.0E+5 4.0E+5 6.0E+5 C S ASBS-STA ASBS-21D (a) Read the entire page →
From page 160... ... 160 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results Prediction of Damage Characteristic Curves at Different Aging Levels The prediction of damage characteristic curves for different aging durations helps simulate performance predictions over a pavement's lifespan. Likewise, for the prediction of dynamic modulus master curves, the best approach to predict the pseudo stiffness (C) Read the entire page →
From page 161... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 161 y = 0.0013e-0.063x R² = 0.7884 y = 0.0022e-0.086x R² = 0.999 y = 0.0030e-0.0612x R² = 0.8872 y = 0.002e-0.081x R² = 0.9884 y = 0.0008e-0.0404x y = 0.003e-0.036x y = 0.0072e-0.127x 0.0001 0.001 0.01 0 5 10 15 20 25 C 11 Aging Duration (Days) ACTRL LTX LSD LWA SAAG ASBS SAAD Figure 44. Read the entire page →
From page 162... ... y = -0.1722x + 0.0655 R² = 0.9821 -0.14 -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.00 0 0.5 1 1.5 Sl op e of C 11 M (a) Read the entire page →
From page 163... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 163 0.0 0.2 0.4 0.6 0.8 1.0 0.E+00 1.E+05 2.E+05 3.E+05 4.E+05 C S ACTRL-STA ACTRL-4D ACTRL-8D ACTRL-16D Predicted-ALF CTRL-4D Predicted-ALF CTRL-8D Predicted-ALF CTRL-16D ACTRL 0.0 0.2 0.4 0.6 0.8 1.0 0.E+00 1.E+05 2.E+05 3.E+05 4.E+05 C S LTX-STA LTX-4D LTX-8D LTX-17D Predicted-LTX-4D Predicted-LTX-8D Predicted-LTX-17D LTX 0.0 0.2 0.4 0.6 0.8 1.0 0.0E+00 1.0E+05 2.0E+05 3.0E+05 C S LSD-STA LSD-4D LSD-8D LSD-16D Predicted-LSD-4D Predicted-LSD-8D Predicted-LSD-16D LSD 0.0 0.2 0.4 0.6 0.8 1.0 0.E+00 1.E+05 2.E+05 3.E+05 4.E+05 C S LWA-STA LWA-4D LWA-16D Predicted-LWA-4D Predicted-LWA-8D Predicted-LWA-16D LWA 0.0 0.2 0.4 0.6 0.8 1.0 0.E+00 2.E+05 4.E+05 6.E+05 C S ASBS-STA ASBS-21D Predicted-ASBS-21D ASBS 0.0 0.2 0.4 0.6 0.8 1.0 0.E+00 2.E+05 4.E+05 6.E+05 C S SAAD-STA SAAD-9D Predicted-SAAD-9D SAAD 0.0 0.2 0.4 0.6 0.8 1.0 0.E+00 2.E+05 4.E+05 6.E+05 C S SAAG-STA SAAG-19D Predicted-SAAG-19D SAAG Figure 48. Measured and predicted damage characteristic curves. Read the entire page →
From page 164... ... 164 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results Prediction of Failure Criteria Using Binder Aging Properties Predicting fatigue resistance is more challenging than predicting stiffness-related properties such as dynamic modulus master curves. Figure 49 shows the evolution of the DR criterion with regard to aging for the different mixtures. Read the entire page →
From page 165... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 165 inaccurate. If the master curves are shifted, different tTS factor functions are obtained for each age level when constructing these master curves ahead of shifting. Read the entire page →
From page 166... ... 166 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results The horizontal shift factors needed to create the smooth aging master curve represent total shift factors constituting both tTS factors and time-aging shift (tAS) factors. Read the entire page →
From page 167... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 167 The observation that the trend of tAS factors with aging duration is similar to that of log \|G* \| with aging duration calls for an attempt to fit the tAS factors with a functional form similar to that of Equation (1) Read the entire page →
From page 168... ... 168 Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results individually. As can be seen in Table 11, however, the ranking of the mixtures using N and M is evidently very different. Read the entire page →
From page 169... ... Prediction of Mixture Properties Through the Rate of Change of Model Coefficients 169 kc′ are used (Equation (2) and Equation (3) Read the entire page →

From page 140...

... 140 Materials Table 4 presents the materials selected for the systematic aging study. The selected component materials encompass different aggregate sources and binder types.