Zhan Yang , Rongshan Yang , Xiaolong Liang , Shiqiang Liang , Melese Tibebu Tegegne , Qiang Zhang , Yong Liu
{"title":"Automatic prediction of railway ballast layer thickness using graph neural network based on GPR reflection signals","authors":"Zhan Yang , Rongshan Yang , Xiaolong Liang , Shiqiang Liang , Melese Tibebu Tegegne , Qiang Zhang , Yong Liu","doi":"10.1016/j.autcon.2025.106318","DOIUrl":null,"url":null,"abstract":"<div><div>Ground-Penetrating Radar (GPR) is widely employed for detecting the thickness of railway ballast layer. However, the complexity of the GPR data often requires manual interpretation by experts, which limits the efficiency of large-scale inspections. To address this challenge, this paper proposes a graph neural network-based method for automatic ballast layer thickness prediction. This method leverages Temporal Convolutional Networks (TCNs) to extract temporal patterns from the GPR A-scans and employs Graph Convolutional Networks (GCNs) with a self-adaptive adjacency matrix to dynamically learn and refine the spatial correlations across multiple A-scans. The proposed method was validated using a combined dataset of simulated and field data, and further tested through on-site applications. Experimental results show that the method outperforms four baseline models in prediction accuracy while maintaining high inference efficiency. In on-site tests, the average absolute prediction errors of Two-Way Travel Time (TWTT) and thickness were 0.25 % and 3.06 %, respectively. These findings demonstrate the effectiveness, efficiency, and potential scalability of the proposed method for railway ballast layer thickness detection.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106318"},"PeriodicalIF":9.6000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580525003589","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Ground-Penetrating Radar (GPR) is widely employed for detecting the thickness of railway ballast layer. However, the complexity of the GPR data often requires manual interpretation by experts, which limits the efficiency of large-scale inspections. To address this challenge, this paper proposes a graph neural network-based method for automatic ballast layer thickness prediction. This method leverages Temporal Convolutional Networks (TCNs) to extract temporal patterns from the GPR A-scans and employs Graph Convolutional Networks (GCNs) with a self-adaptive adjacency matrix to dynamically learn and refine the spatial correlations across multiple A-scans. The proposed method was validated using a combined dataset of simulated and field data, and further tested through on-site applications. Experimental results show that the method outperforms four baseline models in prediction accuracy while maintaining high inference efficiency. In on-site tests, the average absolute prediction errors of Two-Way Travel Time (TWTT) and thickness were 0.25 % and 3.06 %, respectively. These findings demonstrate the effectiveness, efficiency, and potential scalability of the proposed method for railway ballast layer thickness detection.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.