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| Application of machine learning to intelligent shield tunnelling: review and prospects |
| PAN Qiujing1, LI Xiaozhou1, HUANG Shan2, WANG Lai1, WANG Shuying1, FANG Guoguang3
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1. College of Civil Engineering, Central South University, Changsha 410075, Hunan, China;
2. China Railway No.5 Engineering Group Electric City Communication Co., Ltd., Changsha 410117, Hunan, China;
3. Singapore University of Technology and Design, Singapore 487372, Singapore |
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Abstract This paper made a comprehensive literature review on the topic of applying machine learning methods to shield tunnelling parameter predictions, stratum predictions ahead of a tunnel face, surface settlements prediction, shield attitude deviation and tool wear predictions. The selections of machine learning methods and the associated input and output parameters were analyzed, and the shortcomings and challenges of existing research were summarized. Some prospects were given, including the model generalization, multi-source heterogeneous data compression and assimilation, data-physics-based intelligent shield tunnelling, big data in shield tunnelling, in order to provide reference and guidance for the future research and engineering practice.
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Received: 19 October 2021
Published: 20 September 2022
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