Prediction Of Reflection Cracking In Hot Mix Asphalt Overlays

Reflection cracking is one of the main distresses in hot-mix asphalt (HMA) overlays. It has been a serious concern since early in the 20th century. Since then, several models have been developed to predict the extent and severity of reflection cracking in HMA overlays. However, only limited research has been performed to evaluate and calibrate these models. In this dissertation, mechanistic-based models are calibrated to field data of over 400 overlay test sections to produce a design process for predicting reflection cracks. Three cracking mechanisms: bending, shearing traffic stresses, and thermal stress are taken into account to evaluate the rate of growth of the three increasing levels of distress severity: low, medium, and high. The cumulative damage done by all three cracking mechanisms is used to predict the number of days for the reflection crack to reach the surface of the overlay. The result of this calculation is calibrated to the observed field data (severity and extent) which has been fitted with an S-shaped curve. In the mechanistic computations, material properties and fracture-related stress intensity factors are generated using efficient Artificial Neural Network (ANN) algorithms. In the bending and shearing traffic stress models, the traffic was represented by axle load spectra. In the thermal stress model, a recently developed temperature model was used to predict the temperature at the crack tips. This process was developed to analyze various overlay structures. HMA overlays over either asphalt pavement or jointed concrete pavement in all four major climatic zones are discussed in this dissertation. The results of this calculated mechanistic approach showed its ability to efficiently reproduce field observations of the growth, extent, and severity of reflection cracking. The most important contribution to crack growth was found to be thermal stress. The computer running time for a twenty-year prediction of a typical overlay was between one and four minutes.