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

Abstract

<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>

Variational inequalities of multilayer viscoelastic systems with interlayer Tresca friction: Existence and uniqueness of solution and convergence of numerical solution

“…In Figure 1, a three-dimensional physical model of a circular asphalt pavement is shown, which was built by the Research Institute of Highway Ministry of Transport track in Beijing. A total of 19 pavement structures are included in the circular asphalt pavement, and the specific information of each pavement structure can be found in [3,13]. The purpose of the pavement experiment is to study the relationship between stress and displacement of different pavement structures.…”