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Prediction and Feature Importance of Earth Pressure in Shields Using Machine Learning Algorithms

  • Tunnel Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

To reduce subjectivity and uncertainty when maintaining suitable earth pressure in earth pressure shields that can prevent heave or collapse, many prediction models using machine learning algorithms were proposed, but little research into the effects of other parameters on earth pressure has been undertaken, and soil conditioning parameters are always ignored. To establish a model with thorough parameters and probe into influences of other parameters, multiple machine learning algorithms were attempted. Given the accuracy, diversity and functions, random forest (RF), LightGBM and Attention-back-propagation neural network (Attention-BPNN) were further analyzed. Then, two RF models were compared in this research, one with soil conditioning parameters and the other without. Meanwhile, a case study was utilized to verify the reliability of the model. Finally, the feature importance of three models was compared and the variation rules of the most four important features were discussed by controlling variates. The results showed that soil conditioning parameters delivered a significant reduction in the prediction error. The case study demonstrated that the proposed model can satisfy engineering requirements. More earth pressure should give priority to increasing propulsion pressure, advance rate, and reducing foam air flow, rotational speed of screw conveyor, and vice versa.

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Acknowledgments

This study was supported by the National Natural Science Foundation of China (Grants 52179121 & 52079150), the Light Natural Science Foundation of Shaanxi Province (Grant 2021JLM-50), and IWHR Research & Development Support Program of China (Grant GE0145B012021).

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Authors and Affiliations

  1. Institute of Geotechnical Engineering, Zhejiang University, Zhejiang Hangzhou, 310058, China

    Hongyu Huang

  2. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100048, China

    Lipeng Liu & Ruilang Cao

  3. China Power Railway Construction Investment Group Co., Ltd, Beijing, 100070, China

    Yuxin Cao

Authors
  1. Hongyu Huang
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  2. Lipeng Liu
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  3. Ruilang Cao
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  4. Yuxin Cao
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Correspondence to Lipeng Liu.

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Huang, H., Liu, L., Cao, R. et al. Prediction and Feature Importance of Earth Pressure in Shields Using Machine Learning Algorithms. KSCE J Civ Eng 27, 862–877 (2023). https://doi.org/10.1007/s12205-022-1241-8

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  • DOI: https://doi.org/10.1007/s12205-022-1241-8

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