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Geomechanics and Engineering Volume 25, Number 1, April10 2021, pages 59-73 DOI: https://doi.org/10.12989/gae.2021.25.1.059 |
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Evaluation of geological conditions and clogging of tunneling using machine learning |
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Xue-Dong Bai, Wen-Chieh Cheng, Dominic E.L. Ong and Ge Li
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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. | ||
| 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 | ||