This paper characterized field rutting performance of asphalt pavement based on Hamburg wheel tracking (HWT) rut depth. The rut depths were collected from 50 field pavement sections, and cores from the same test areas were obtained to conduct volumetric properties measurement and HWT test. The relationship between field measurements and HWT rut depth was evaluated; the ranking of HWT results and field rut depth among mixtures was also compared. An analysis of if the HWT rut depth underpredicted or overpredicted field rut depth, or they were equivalent was summarized. A field rut depth predictive model that consisted of HWT rut depth was developed. Results indicated that the HWT rut depth magnitudes were closer to field rut depth if polymer modification was adopted. The rutting observed in the field was minor compared to what was observed with the laboratory HWT test results for the majority of evaluated pavement sections. Ranking analysis showed that applying HWT results at the end of the test did not provide a strong comparison in contrast to the field rut depth ranking among mixtures. The field rut depth predictive model was developed based on the random forest algorithm, which included four input parameters, namely, HWT rut depth, pavement age, number of high-temperature hours, and annual average daily truck traffic (AADTT). The model was able to accurately predict field rut depth based on the relatively high coefficient of determination (R2=0.79) and low standard error of the esitimate (SEE=0.58). The sensitivity analysis indicated that pavement age has the most significant effect on rut depth, followed by HWT rut depth and AADTT.
|Journal of Transportation Engineering Part B: Pavements
|Published - Mar 1 2021
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering