A data-driven approach to the evaluation of asphalt pavement structures using falling weight deflectometer

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