Pavement defect detection is of profound significance regarding road safety, so it has been a
trending research topic. In the past years, deep learning based methods have turned into a …

Abstract Graph Neural Networks (GNNs) have emerged as a promising solution for
effectively handling non-Euclidean data in construction, including building information …

Road pavement damage affects driving comfort markedly, threatens driving safety, and may
even cause traffic accidents. The traffic management department conventionally captures …

The intelligent detection of pavement distress using deep learning methods has consistently
been a hotspot in pavement maintenance. This paper aims to offer new insights to promote …

Introduction In urban traffic management, the timely detection of road faults plays a crucial
role in improving traffic efficiency and safety. However, conventional methods often fail to …

Road damage presents a significant risk to traffic safety, including pavement distress and
disaster-induced damage. Thanks to their high efficiency, computer vision-based methods …

Abstract Machine learning models have seen widespread application in predicting building
thermal loads. Yet, these existing models generally predict the thermal load for a single …

Ensuring traffic safety is crucial, which necessitates the detection and prevention of road
surface defects. As a result, there has been a growing interest in the literature on the subject …

Rapid post-earthquake structural assessments are essential, as they significantly contribute
to community recovery and enhancing seismic resilience. This research introduces a novel …

Accurate pavement crack extraction is significant for pavement routine maintenance and
potential traffic disaster minimization. Due to unordered data formats, intensity distinctions …