Effects of traffic load amplitude sequence on the cracking performance of asphalt pavement with a semi-rigid base

ABSTRACT

The propagation of cracks in in-service asphalt pavements is closely related to the complicated traffic loading patterns over time. However, typical traffic-related variables capture only the overall traffic level without being able to account for the load-time history. Therefore, this study aims to investigate the effects of traffic load sequence on the cracking performance of asphalt pavement from both field and laboratory perspectives. A load amplitude sequence (LAS) index was developed to characterize the traffic loading sequence in the field. Two machine learning (ML) algorithms, namely artificial neural network (ANN) and random forest regression (RFR), were applied to correlate the LAS index with field pavement cracking performance. The two-block semi-circular bending (SCB) test was developed to characterize the non-linear fatigue damage accumulation of asphalt mixtures. It was found that heavier traffic loads in early stages are detrimental to the long-term pavement cracking performance. The LAS index plays a crucial role in the prediction and development of pavement cracks. The laboratory test results reveal that a loading sequence starting with a higher stress may shorten the fatigue life and vice versa. The outcomes of this study may contribute to a better understanding of the traffic loading characterization of in-service asphalt pavements.

KEYWORDS:

• Load amplitude sequence
• pavement cracking
• traffic loading characterisation
• machine learning
• variable amplitude fatigue test

Acknowledgements

This study was conducted under the support of the Research Institute for Sustainable Urban Development (RISUD) at the Hong Kong Polytechnic University. In addition, the data used in this research were collected from the Pavement Management System in Jiangsu province, China. The engineers and researchers who established the system and collected the data are also acknowledged for their contribution.

Disclosure statement

No potential conflict of interest was reported by the author(s).