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A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the Xi’an metro, China

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Abstract

Complex geological conditions and/or inappropriate shield tunnel boring machine (TBM) operation can significantly degrade both the excavation and safety of tunnel construction. In recent years, the excavation behaviour of shield TBMs has been a popular topic in the literature given the large volume of data automatically collected by modern shields. These datasets provide an excellent opportunity to apply advanced data analysis techniques to improve predictions of shield tunnelling excavation behaviour. In this study, a framework to develop machine learning (ML)-based regression models for predicting the behaviour of an earth pressure balance (EPB) shield machine using tunnelling parameters is proposed. The feasibility of four ML algorithms, namely Linear Regression (LR), Decision Tree Regression (DTR), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR), to predict EPB shield excavation behaviour is explored through their application to a recent tunnelling case history in sandy soils. The results show that the misestimates were primarily attributed to a reduction of screw conveyor rotational speed (SCRS), induced by a lower injection volume, the artificial manipulation of penetration rate (PR), the local variations of total jacking load, and the use of ‘breakout’ cutterwheel torque (CT). The GBR model provided the best performance, while LR often performs the worst due to its inability to handle highly nonlinear relationships. DTR prevented the overfitting problem by using a lower max depth parameter towards sacrificing its accuracy. The performance of SVR was seriously affected by loss functions. The proposed optimisation scheme that prevents the over-smoothing problem during the STL decomposition elevates further the prediction accuracy.

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Acknowledgements

This work would not have been possible without supports from the Special Fund for Shaanxi innovation ability support scheme (EDS—Education Department of Shaanxi Province) under Grant No. 2020TD-005.

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Authors and Affiliations

  1. School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an, 710055, China

    Xue-Dong Bai, Wen-Chieh Cheng & Ge Li

  2. Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering (XAUAT), Xi’an 710055, China

    Wen-Chieh Cheng

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  1. Xue-Dong Bai
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  2. Wen-Chieh Cheng
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  3. Ge Li
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X-DB Data curation, Formal analysis, Validation, Software, Writing—original draft. W-CC Conceptualisation, Methodology, Writing—review & editing, Supervision, Funding acquisition. GL Writing—review & editing.

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Correspondence to Wen-Chieh Cheng.

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Bai, XD., Cheng, WC. & Li, G. A comparative study of different machine learning algorithms in predicting EPB shield behaviour: a case study at the Xi’an metro, China. Acta Geotech. 16, 4061–4080 (2021). https://doi.org/10.1007/s11440-021-01383-7

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