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Prediction of shield jamming risk for double-shield TBM tunnels based on numerical samples and random forest classifier

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Abstract

Currently, various machine learning (ML) techniques have been developed to solve geotechnical engineering problems. However, the lack of representative field samples limits the application of ML models. A shield jamming risk prediction method based on numerical samples and random forest (RF) classifier is proposed. The database with samples of different shield jamming risk levels is established by numerical simulation of the TBM construction process. By setting different values and combinations, seven influencing parameters, i.e., advance rate, overcut, elastic modulus, tensile strength, in situ stress, maximum thrust and friction coefficient are considered. The shield jamming risk level is determined according to the ratio of the total friction force to TBM residual thrust. Feature importance analysis indicates that elastic modulus, overcut and in-situ stress are the major influencing factors of shield jamming. Based on the labeled database, the RF model integrating multiple decision trees is established to capture the complex relationship between shield jamming risk and different influencing factors. The trained model has shown a good prediction performance on test set, and the prediction results of six field instances in DXL tunnel are in good agreement with the actual shield jamming situation. Compared with other conventional classifiers, i.e., support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT) and logistic regression (LR), the proposed FR classifier has stronger prediction accuracy and generalization ability. Additionally, the influence of sample numbers and sample imbalance is discussed.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China with Grant No. 52179105 and the State Key Laboratory of Hydroscience and Engineering with Grant No. 2019-KY-03.

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Authors and Affiliations

  1. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China

    Shaokang Hou, Yaoru Liu, Wenyu Zhuang, Kai Zhang, Rujiu Zhang & Qiang Yang

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  1. Shaokang Hou
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  2. Yaoru Liu
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Correspondence to Yaoru Liu.

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Hou, S., Liu, Y., Zhuang, W. et al. Prediction of shield jamming risk for double-shield TBM tunnels based on numerical samples and random forest classifier. Acta Geotech. 18, 495–517 (2023). https://doi.org/10.1007/s11440-022-01567-9

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