Geomechanics and Engineering

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Evaluation of geological conditions and clogging of tunneling using machine learning

  • Bai, Xue-Dong (School of Civil Engineering, Xi'an University of Architecture and Technology) ;
  • Cheng, Wen-Chieh (School of Civil Engineering, Xi'an University of Architecture and Technology) ;
  • Ong, Dominic E.L. (School of Engineering and Built Environment, Griffith University) ;
  • Li, Ge (School of Civil Engineering, Xi'an University of Architecture and Technology)
  • Received : 2020.10.25
  • Accepted : 2021.03.09
  • Published : 2021.04.10
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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.

Keywords

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