Prediction Model for Field Rut Depth of Asphalt Pavement Based on Hamburg Wheel Tracking Test Properties

The Hamburg wheel tracking (HWT) test has been found to be a promising test to evaluate the field rutting performance of asphalt pavements and has been implemented as a material screening test during the mix design process by several state departments of transportation. However, the rutting performance of an asphalt pavement depends not only on the material properties, but also on many other factors such as pavement structure and traffic. To date, there are few performance models that have integrated the Hamburg rutting parameters for pavement rutting prediction. In addition, mechanistic-empirical-based prediction models have been found to have some difficulties in reasonably predicting field rut depth, especially when field variables and confounding factors have to be considered. Therefore, the objective of this paper is to evaluate the relationship between the HWT test results and the field rut depth, then develop a predictive model for field rut depth based on the HWT test results. Field projects consisting of 51 hot mix asphalt (HMA) and warm mix asphalt (WMA) pavements were included in the analysis. These projects were located in different climatic zones with varying traffic levels, pavement structures, and material properties. Through direct correlation, it was found that the field rut depth in general decreased with the increase of the rutting resistance index (RRI). However, HWT test results alone do not have a strong relationship with the field rut depth, and many other factors, such as climate and pavement structure, have to be considered. Further, statistical-based methods in conjunction with engineering interpretation were applied to identify critical influencing factors and develop a prediction model for field rut depth. The developed rutting predictive model indicated that (a) mixture property (rutting resistance index, a parameter developed based on the HWT test), pavement age (month), average annual daily truck traffic (AADTT), and pavement structure (total HMA thickness and overlay thickness) are critical influencing factors for field rut depth; (b) RRI, along with pavement age and traffic data, has the most significant effect on rut depth among the identified five key predictor variables; (c) no significant differences are observed between prediction results of HMA and WMA mixtures, and thus the prediction model can be applied for both; and (d) using the developed predictive model, the effect of the HWT RRI can be considered comprehensively with other factors including climate, traffic, and pavement structure to determine the suitability of a designed asphalt mixture for pavement construction. (C) 2017 American Society of Civil Engineers.