A multidimensional framework for asphalt pavement evaluation based on multilayer network representation learning: A case study in RIOHTrack

Abstract

Pavement evaluation using multiple performance indicators has been a critical challenge in the field due to limitations in relying on pavement engineers to simultaneously assess several performance attributes, which usually leads to subjectivity and variability. In this study, a data-driven multidimensional framework is proposed to compact this issue by utilizing multilayer network representation learning. The key to this framework is to capture not only the performance conditions per se, but also the nonlinear interactions among these attributes. This provides an in-depth higher-order property of pavements’ service condition. Specifically, pavement performance attributes are modeled into multilayer network with each layer representing an aspect of pavement condition. Subsequently, this multilayer pavement condition network is mapped into low-dimensional space through the network representation learning for systematic evaluation. Finally, unsupervised cluster analysis derives groups of pavements which share similar overall condition for future decision-making process. The proposed method is validated with a case study in the Research Institute of Highway Ministry track (RIOHTrack) and experimental results demonstrate the effectiveness in categorizing pavement condition based on multi-attributes.