Considering Spatial Structure of the Road Network in Pavement Deterioration Modeling
Pavement deterioration modeling is important in providing information regarding the future state of the road network and in determining the needs of preventive maintenance or rehabilitation treatments. This research incorporated spatial dependence of road network into pavement deterioration modeling through a graph neural network (GNN). The key motivation of using a GNN for pavement performance modeling is the ability to easily and directly exploit the rich structural information in the network. This paper explored if considering spatial structure of the road network will improve the prediction performance of the deterioration models. The data used in this research comprises a large pavement condition data set with more than a half million observations taken from the Pavement Management Information System (PMIS) maintained by the Texas Department of Transportation. The promising comparison results indicates that pavement deterioration prediction models perform better when spatial relationship is considered.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph Neural NetworkSimilar Papers 제목 키워드 기반
Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks
Pavement infrastructure monitoring is challenged by complex spatial dependencies, changing environmental conditions, and non-linear deterioration across road networks. Traditional Pavement Management Systems (PMS) remain…
Reinforcement LearningGraph Neural NetworkDeep learning-based pavement performance modeling using multiple distress indicators and road work history
The deterioration of pavement is a complex and dynamic process determined by different factors including material, environment, design, and some other unobserved variables. Accurate predictions of pavement condition can …
ST-ResGAT: Explainable Spatio-Temporal Graph Neural Network for Road Condition Prediction and Priority-Driven Maintenance
Climate-vulnerable road networks require a paradigm shift from reactive, fix-on-failure repairs to predictive, decision-ready maintenance. This paper introduces ST-ResGAT, a novel Spatio-Temporal Residual Graph Attention…
Graph Neural NetworkA Decision Making Framework for Recommended Maintenance of Road Segments
Due to limited budgets allocated for road maintenance projects in various countries, road management departments face difficulties in making scientific maintenance decisions. This paper aims to provide road management de…
Decision MakingDeep Reinforcement LearningManagementConvolutional neural networks for crack detection on flexible road pavements
Flexible road pavements deteriorate primarily due to traffic and adverse environmental conditions. Cracking is the most common deterioration mechanism; the surveying thereof is typically conducted manually using internat…