Clogging Risk Early Warning for Slurry Shield Tunneling in Mixed Mudstone–Gravel Ground: A Real-Time Self-Updating Machine Learning Approach

Junli Zhai, Qiang Wang, Dongyang Yuan, Weikang Zhang, Haozheng Wang, Xiongyao Xie and Isam Shahrour
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Junli Zhai: Zhejiang Scientific Research Institute of Transport, Hangzhou 311305, China
Qiang Wang: Zhejiang Scientific Research Institute of Transport, Hangzhou 311305, China
Dongyang Yuan: Zhejiang Scientific Research Institute of Transport, Hangzhou 311305, China
Weikang Zhang: Zhejiang Scientific Research Institute of Transport, Hangzhou 311305, China
Haozheng Wang: Zhejiang Scientific Research Institute of Transport, Hangzhou 311305, China
Xiongyao Xie: Zhejiang Scientific Research Institute of Transport, Hangzhou 311305, China
Isam Shahrour: Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China

Sustainability, 2022, vol. 14, issue 3, 1-21

Abstract: Clogging constitutes a significant obstacle to shield tunneling in mudstone soils. Previous research has focused on investigating the influence of soils and slurry properties on clogging, although little attention has been paid to the impact of tunneling parameters on clogging, and particularly early clogging warning during tunneling. This paper contributes to developing a real-time clogging early-warning approach, based on a self-updating machine learning method. The clogging judgment criteria are based on the statistical characteristics of whole-ring tunneling parameters. The paper proposes the use of random forest (RF) for a real-time self-updating early warning strategy for clogging. The performance of this approach is illustrated through its application to a slurry-pressure-balanced shield tunneling construction of Nanning metro line 1. Results show that the RF-based approach can predict clogging during a ring construction with only four minutes of tunneling data, with an accuracy of 95%. The RF model provided the best performance compared with the other machine learning methods. Furthermore, the RF model can realize an accurate clogging prediction in one ring, using less tunneling data with the self-updating mechanism.

Keywords: clogging; slurry shield tunneling; mudstone; real-time; early warning; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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