{"title":"Evaluation of geological conditions and clogging of tunneling using machine learning","authors":"Xue-Dong Bai, W. Cheng, D. Ong, Ge Li","doi":"10.12989/GAE.2021.25.1.059","DOIUrl":null,"url":null,"abstract":"There frequently exists inadequacy regarding the number of boreholes installed along tunnel alignment. While geophysical imaging techniques are available for pre-tunnelling geological characterization, they aim to detect specific object (e.g., water body and karst cave). There remains great motivation for the industry to develop a real-time identification technology relating complex geological conditions with the existing tunnelling parameters. This study explores the potential for the use of machine learning-based data driven approaches to identify the change in geology during tunnel excavation. Further, the feasibility for machine learning-based anomaly detection approaches to detect the development of clayey clogging is also assessed. The results of an application of the machine learning-based approaches to Xi'an Metro line 4 are presented in this paper where two tunnels buried in the water-rich sandy soils at depths of 12-14 m are excavated using a 6.288 m diameter EPB shield machine. A reasonable agreement with the measurements verifies their applicability towards widening the application horizon of machine learning-based approaches.","PeriodicalId":12602,"journal":{"name":"Geomechanics and Engineering","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geomechanics and Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.12989/GAE.2021.25.1.059","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 23
Abstract
There frequently exists inadequacy regarding the number of boreholes installed along tunnel alignment. While geophysical imaging techniques are available for pre-tunnelling geological characterization, they aim to detect specific object (e.g., water body and karst cave). There remains great motivation for the industry to develop a real-time identification technology relating complex geological conditions with the existing tunnelling parameters. This study explores the potential for the use of machine learning-based data driven approaches to identify the change in geology during tunnel excavation. Further, the feasibility for machine learning-based anomaly detection approaches to detect the development of clayey clogging is also assessed. The results of an application of the machine learning-based approaches to Xi'an Metro line 4 are presented in this paper where two tunnels buried in the water-rich sandy soils at depths of 12-14 m are excavated using a 6.288 m diameter EPB shield machine. A reasonable agreement with the measurements verifies their applicability towards widening the application horizon of machine learning-based approaches.
期刊介绍:
The Geomechanics and Engineering aims at opening an easy access to the valuable source of information and providing an excellent publication channel for the global community of researchers in the geomechanics and its applications.
Typical subjects covered by the journal include:
- Analytical, computational, and experimental multiscale and interaction mechanics-
Computational and Theoretical Geomechnics-
Foundations-
Tunneling-
Earth Structures-
Site Characterization-
Soil-Structure Interactions