A Mechanistic-Empirical Model for Predicting Top-Down Cracking in Asphalt Pavements
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Top-down cracking (TDC) is a major distress mode in asphalt pavements. It initiates from or near the pavement surface then propagates downward through the asphalt layer and is usually observed as the form of longitudinal cracking in the pavement wheelpath. Due to its complexity, few mechanical-based systematic models are available to accurately evaluate the TDC performance and the efforts for the calibration and validation using a large number of field data are missing. To fill these research gaps and improve the understanding of TDC, the main objective of this study is to develop a calibrated and validated mechanistic-empirical model to characterize TDC in asphalt pavement layers. Based on literature review, important factors contributed to the initiation and propagation of TDC are identified. One is non-uniform oxidative aging of the asphalt binders. The surface of the asphalt layer becomes stiffer and more brittle after field aging so that the crack is more prone to initiate at the pavement surface. Direct tension test is selected to analysis the modulus gradient of the field-aged asphalt mixtures. The longterm aging property also needs to be well determined. It is known that the long-term aging has a significant impact on the viscoelastic, healing, fracture and thermal properties of the asphalt mixtures. Two aging shift functions for the dynamic modulus are developed to quantify the effects of the long-term aging and non-uniform aging of in-situ asphalt pavements, respectively. A TDC initiation model is developed using continuum damage and fracture mechanics with field observations including local traffic, material properties, pavement structures and TDC initiation time. A three dimensional finite element model (FEM) and artificial neural network (ANN) models are developed to calculate J-integral (energy release rate) in the crack propagation phase. A preliminary endurance limit model is proposed based on the viscoelastic Griffith crack initiation criterion. A total of 80% and 20% of pavement sections are randomly selected for the calibration and validation purposes, respectively. Finally, this TDC model is coded in C# computer language as a stand-alone program to predict the initiation time and fatigue life of TDC. It can also be utilized as a subroutine in the AASHTOware Pavement Mechanistic-Empirical (ME) Design.
Crack initiation and propagation
Ling, Meng (2017). A Mechanistic-Empirical Model for Predicting Top-Down Cracking in Asphalt Pavements. Doctoral dissertation, Texas A & M University. Available electronically from