<p style='text-indent:20px;'>The evaluation of asphalt pavement structures has been a critical challenge in the field due to the practical limitations in methodology. In this paper, we propose a data-driven framework to evaluate structural performance of nineteen widely used asphalt structures in the Research Institute of Highway Ministry of Transport track (RIOHTrack). Specifically, we utilize the unsupervised machine learning method to delineate the similar and disparate performance among tested structures based on four years of falling weight deflectometer (FWD) experiments. Next, the structural performance is investigated on the temporal scale and the dynamic performance variations are captured over the course of the testing. Finally, experimental results are discussed and we provide essential evidence to aid future asphalt pavement design and construction.</p>

The assessment of asphalt pavement structures with diversified designs and constructions has been a critical challenge due to the practical limitations in testing and methodology. Here, we propose a complex network approach to the evaluation of longitudinal asphalt pavement performances in the Research Institute of Highway Ministry of Transport track (RIOHTrack). We add to the growing field in which network approaches are employed and indicate the profound application in understanding pavement structures. Specifically, similar and disparate pavement performances are revealed regardless of their predefined design and construction categories. Evidence shows that short-term pavement performance depends on the strength of the subbase structure, whereas long-term performance relies on the thickness of the asphalt layer. In addition, our results indicate that recycling materials could be an important substitute for reducing the thickness of asphalt concrete and maintaining a healthy service life.