Evaluation of geological conditions and clogging of tunneling using machine learning

Xue-Dong Bai , Wen-Chieh Cheng , Dominic E.L. Ong , Ge Li

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.

Key Words

Xi'an Metro; EPB shield; machine learning; clogging; silt soil

Address

Xue-Dong Bai and Ge Li — 1.) School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China 2.) Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering (XAUAT), Xi'an 710055, China
Wen-Chieh Cheng — School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Dominic E.L. Ong — School of Engineering and Built Environment, Griffith University, Queensland 4111, Australia

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