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ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geohazards and Georisks
Volume 13 - 2025 | doi: 10.3389/feart.2025.1544650
This article is part of the Research Topic Evolution Mechanism and Prevention Technology of Karst Geological Engineering Disasters View all articles
TBM Shield Mud Cake Prediction Model Based on Machine Learning
Provisionally accepted- 1 CCCC Second Harbor Engineering Co., Ltd., Wuhan, China
- 2 CCCC South China Construction and Development Co., Ltd., Shenzhen, China
- 3 Sichuan Tibet Railway Co., Ltd., Chengdu, China
- 4 School of Civil Engineering, Southeast University, Nanjing, Jiangsu Province, China
- 5 School of Civil Engineering, Central South University, Changsha, Hunan Province, China
During tunnel boring machine (TBM) shield tunneling in clayey strata, the excavated soil consolidates on the cutter head or cutting tools , forming mud cakes that significantly impact the efficiency of shield tunneling. To predict mud cakes during shield tunneling, four distinct supervised machine learning models, including logistic regression, support vector machine, random forest, and BP neural network were employed. The optimal predictive model for mud cake formation was determined by assessing the precision, recall, and F1 scores of the models. Further analysis of feature dependencies and shapley additive explanations (SHAP) is conducted to pinpoint the critical risk factors associated with mud cake formation. The results indicate that among the four supervised machine learning models, the random forest model exhibited the best performance in predicting mud cake formation during shield tunneling, with an F1 score as high as 0.9934. Feature dependencies and SHAP information showed that the shield tunneling chamber temperature and average excavation speed had the most significant impact on mud cake formation, serving as crucial factors in determining mud cake formation. The rear earth pressure of the screw conveyor and the cutterhead penetration depth followed, constituting important elements in mud cake formation. The introduction of the interpretable method SHAP for analyzing the relationships between various factors extends beyond simple linear relationships, allowing for the examination of nonlinear patterns among factors.
Keywords: Shield tunnel, Mud cake, tunneling parameter, machine learning, Prediction model
Received: 13 Dec 2024; Accepted: 10 Feb 2025.
Copyright: © 2025 Zhang, Xu, Zhang, Yang, Li, Kong and Yuan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Qi Zhang, CCCC Second Harbor Engineering Co., Ltd., Wuhan, China
Peng Xu, CCCC South China Construction and Development Co., Ltd., Shenzhen, China
Jing Zhang, Sichuan Tibet Railway Co., Ltd., Chengdu, China
Zhao Yang, CCCC Second Harbor Engineering Co., Ltd., Wuhan, China
Xintong Kong, School of Civil Engineering, Southeast University, Nanjing, 210096, Jiangsu Province, China
Xiao Yuan, School of Civil Engineering, Central South University, Changsha, 410083, Hunan Province, China
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