Gan Wang , Qian Fang , Jun Wang , Qiming Li , Haoran Song , Jinkun Huang
{"title":"Artificial intelligence prediction of surface settlement induced by twin shields tunnelling","authors":"Gan Wang , Qian Fang , Jun Wang , Qiming Li , Haoran Song , Jinkun Huang","doi":"10.1016/j.tust.2025.106606","DOIUrl":null,"url":null,"abstract":"<div><div>The accurate prediction of the surface settlement trough is essential for the safety assessment of tunnel construction in densely occupied urban areas. In this study, we propose an artificial intelligence model to predict surface settlement troughs induced by twin tunnelling. The proposed model includes a newly proposed formula for describing settlement trough and a new calculation method of loss. The model uses a Graphical Convolutional Neural network (GCN) to extract latent feature information from field monitoring data that shows the state of the surrounding ground before the second shield passage. The proposed model is verified by comparing its results to those of two other models. The analysis shows that the developed calculation method of loss and consideration of the state of surrounding ground significantly improve the prediction accuracy of surface settlement troughs. While adding more monitoring points can offer benefits, the performance gains become weaker as the number of monitoring points increases. Therefore, we recommend using 24 monitoring points for the proposed model as it strikes the optimal balance between performance and computational efficiency.</div></div>","PeriodicalId":49414,"journal":{"name":"Tunnelling and Underground Space Technology","volume":"161 ","pages":"Article 106606"},"PeriodicalIF":6.7000,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tunnelling and Underground Space Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0886779825002445","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
The accurate prediction of the surface settlement trough is essential for the safety assessment of tunnel construction in densely occupied urban areas. In this study, we propose an artificial intelligence model to predict surface settlement troughs induced by twin tunnelling. The proposed model includes a newly proposed formula for describing settlement trough and a new calculation method of loss. The model uses a Graphical Convolutional Neural network (GCN) to extract latent feature information from field monitoring data that shows the state of the surrounding ground before the second shield passage. The proposed model is verified by comparing its results to those of two other models. The analysis shows that the developed calculation method of loss and consideration of the state of surrounding ground significantly improve the prediction accuracy of surface settlement troughs. While adding more monitoring points can offer benefits, the performance gains become weaker as the number of monitoring points increases. Therefore, we recommend using 24 monitoring points for the proposed model as it strikes the optimal balance between performance and computational efficiency.
期刊介绍:
Tunnelling and Underground Space Technology is an international journal which publishes authoritative articles encompassing the development of innovative uses of underground space and the results of high quality research into improved, more cost-effective techniques for the planning, geo-investigation, design, construction, operation and maintenance of underground and earth-sheltered structures. The journal provides an effective vehicle for the improved worldwide exchange of information on developments in underground technology - and the experience gained from its use - and is strongly committed to publishing papers on the interdisciplinary aspects of creating, planning, and regulating underground space.