Abstract
The development of field transverse cracking prediction models is highly complicated because of several factors, including the difficulty in differentiating thermal cracking from reflective cracking in the field, the high variability of field conditions, and the potential variability in crack initiation and crack propagation mechanisms. As a result, a statistical-based approach is preferred to a mechanical-based prediction model. In this study, statistical methods, partial least squares regression, and binary logistic regression were used to establish prediction models for field transverse cracking. Results indicated that crack initiation and crack propagation were controlled by predictor variables. Material properties (mixture creep compliance, work density, and percentage passing the No. 200 sieve), pavement structure (overlay thickness), climate (low temperature hour), and traffic (average annual daily truck traffic) were found to be key indicators for transverse crack propagation. Low temperature hour, percentage passing No. 200 sieve, indirect tensile strength, and service life were critical predictor variables for crack initiation. In particular, the crack initiation model, developed by the binary logistic regression, predicted the probability of crack initiation. Both models show good predictability and are well validated. These models appear to work for hot-mix and warm-mix asphalt pavements.