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A platform for research: civil engineering, architecture and urbanism
Calibration of mechanics-based pavement predictive framework for top-down cracking performance of flexible pavement considering wheel wander effect
https://orcid.org/0000-0003-2849-5263
https://orcid.org/0000-0003-0889-6078
Highlights A new pavement analysis framework is proposed for top-down cracking (TDC) performance of flexible pavements. The effect of lateral wheel wander is captured using Monte Carlo simulation. A failure-curve calibration methodology is implemented using well-documented pavement performance histories from Florida. The loading characteristic of autonomous trucks is simulated by a non-wheel wonder loading scheme. Very accurate prediction of TDC within ± 2 years from the observed crack initiation time.
Abstract This study presents a pavement analysis framework for top-down cracking (TDC) for flexible pavements with considerations of the effects of lateral wheel wander on the long-term pavement performance. A mechanics-based pavement analysis framework developed for predicting TDC is adopted and extended to account for the influence of lateral wheel wander on long-term pavement performance. The mechanics-based pavement analysis model was developed based on the HMA Fracture Mechanics with thresholds introduced to capture the changes in the asphalt concrete mixture resistances to damage and fracture. The fracture resistance and healing propensity of mixture are correlated to mixture morphology to account for the contribution of mixture morphology to these key mixture properties. A failure-curve calibration methodology is developed and implemented using pavement sections with well-documented performance histories from Florida. By accounting for a more realistic representation of the traffic conditions, the calibrated framework delivered accurate predictions of the long-term top-down cracking performance of the pavement sections considered in the study. The TDC performance of pavement for non-wheel wander scenario is evaluated to simulate the loading characteristic of autonomous trucks. A comparative evaluation of the pavement performance for traffic conditions with lateral wheel wander, and the non-wheel wander case was performed. As expected, the results indicate a higher level of accumulated damage in the non-wheel wonder case. The crack initiation time (i.e., the number of years to initiation of cracks) of all pavement sections for non-wheel wander cases is earlier than those with lateral wheel wander. The results show that the non-wheel wander case (autonomous truck scenario) can account for about a 20% reduction in pavement life depending on the traffic volume, pavement structure, materials, and climatic conditions. In addition, the results further exhibit that pavement sections with different traffic volumes exhibited a varying degree of sensitivity to the effect of wheel wander on the long-term performance.
Calibration of mechanics-based pavement predictive framework for top-down cracking performance of flexible pavement considering wheel wander effect
https://orcid.org/0000-0003-2849-5263
https://orcid.org/0000-0003-0889-6078
Highlights A new pavement analysis framework is proposed for top-down cracking (TDC) performance of flexible pavements. The effect of lateral wheel wander is captured using Monte Carlo simulation. A failure-curve calibration methodology is implemented using well-documented pavement performance histories from Florida. The loading characteristic of autonomous trucks is simulated by a non-wheel wonder loading scheme. Very accurate prediction of TDC within ± 2 years from the observed crack initiation time.
Abstract This study presents a pavement analysis framework for top-down cracking (TDC) for flexible pavements with considerations of the effects of lateral wheel wander on the long-term pavement performance. A mechanics-based pavement analysis framework developed for predicting TDC is adopted and extended to account for the influence of lateral wheel wander on long-term pavement performance. The mechanics-based pavement analysis model was developed based on the HMA Fracture Mechanics with thresholds introduced to capture the changes in the asphalt concrete mixture resistances to damage and fracture. The fracture resistance and healing propensity of mixture are correlated to mixture morphology to account for the contribution of mixture morphology to these key mixture properties. A failure-curve calibration methodology is developed and implemented using pavement sections with well-documented performance histories from Florida. By accounting for a more realistic representation of the traffic conditions, the calibrated framework delivered accurate predictions of the long-term top-down cracking performance of the pavement sections considered in the study. The TDC performance of pavement for non-wheel wander scenario is evaluated to simulate the loading characteristic of autonomous trucks. A comparative evaluation of the pavement performance for traffic conditions with lateral wheel wander, and the non-wheel wander case was performed. As expected, the results indicate a higher level of accumulated damage in the non-wheel wonder case. The crack initiation time (i.e., the number of years to initiation of cracks) of all pavement sections for non-wheel wander cases is earlier than those with lateral wheel wander. The results show that the non-wheel wander case (autonomous truck scenario) can account for about a 20% reduction in pavement life depending on the traffic volume, pavement structure, materials, and climatic conditions. In addition, the results further exhibit that pavement sections with different traffic volumes exhibited a varying degree of sensitivity to the effect of wheel wander on the long-term performance.
Calibration of mechanics-based pavement predictive framework for top-down cracking performance of flexible pavement considering wheel wander effect
https://orcid.org/0000-0003-2849-5263
https://orcid.org/0000-0003-0889-6078
Highlights A new pavement analysis framework is proposed for top-down cracking (TDC) performance of flexible pavements. The effect of lateral wheel wander is captured using Monte Carlo simulation. A failure-curve calibration methodology is implemented using well-documented pavement performance histories from Florida. The loading characteristic of autonomous trucks is simulated by a non-wheel wonder loading scheme. Very accurate prediction of TDC within ± 2 years from the observed crack initiation time.
Abstract This study presents a pavement analysis framework for top-down cracking (TDC) for flexible pavements with considerations of the effects of lateral wheel wander on the long-term pavement performance. A mechanics-based pavement analysis framework developed for predicting TDC is adopted and extended to account for the influence of lateral wheel wander on long-term pavement performance. The mechanics-based pavement analysis model was developed based on the HMA Fracture Mechanics with thresholds introduced to capture the changes in the asphalt concrete mixture resistances to damage and fracture. The fracture resistance and healing propensity of mixture are correlated to mixture morphology to account for the contribution of mixture morphology to these key mixture properties. A failure-curve calibration methodology is developed and implemented using pavement sections with well-documented performance histories from Florida. By accounting for a more realistic representation of the traffic conditions, the calibrated framework delivered accurate predictions of the long-term top-down cracking performance of the pavement sections considered in the study. The TDC performance of pavement for non-wheel wander scenario is evaluated to simulate the loading characteristic of autonomous trucks. A comparative evaluation of the pavement performance for traffic conditions with lateral wheel wander, and the non-wheel wander case was performed. As expected, the results indicate a higher level of accumulated damage in the non-wheel wonder case. The crack initiation time (i.e., the number of years to initiation of cracks) of all pavement sections for non-wheel wander cases is earlier than those with lateral wheel wander. The results show that the non-wheel wander case (autonomous truck scenario) can account for about a 20% reduction in pavement life depending on the traffic volume, pavement structure, materials, and climatic conditions. In addition, the results further exhibit that pavement sections with different traffic volumes exhibited a varying degree of sensitivity to the effect of wheel wander on the long-term performance.
Calibration of mechanics-based pavement predictive framework for top-down cracking performance of flexible pavement considering wheel wander effect
Huang, Kai (author) / Onifade, Ibrahim (author) / Birgisson, Bjorn (author)
2021-09-01
Article (Journal)
Electronic Resource
English
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