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Developing hybrid XGBoost model integrated with entropy weight and Bayesian optimization for predicting tunnel squeezing intensity

Author

Listed:
  • Xiaojie Geng

    (Kunming University of Science and Technology
    Ministry of Natural Resources of the People’s Republic of China)

  • Shunchuan Wu

    (Kunming University of Science and Technology
    Ministry of Natural Resources of the People’s Republic of China
    University of Science and Technology Beijing)

  • Yanjie Zhang

    (Yunnan Dianzhong Water Diversion Engineering Co., Ltd.)

  • Junlong Sun

    (Kunming University of Science and Technology)

  • Haiyong Cheng

    (Kunming University of Science and Technology
    Ministry of Natural Resources of the People’s Republic of China)

  • Zhongxin Zhang

    (Kunming University of Science and Technology)

  • Shijiang Pu

    (Kunming University of Science and Technology)

Abstract

Tunnel squeezing, a significant deformation issue intimately tied to creep, poses a substantial threat to the safety and efficiency of tunnel construction. In this study, we employ a combination of the Bayesian Optimization (BO) algorithm and the Entropy Weight Method (EWM) to enhance the Extreme Gradient Boosting (XGBoost) model. This optimized model aims to predict tunnel squeezing intensity accurately, utilizing a dataset derived from 139 tunnel case histories. To extract the information encapsulated in the prediction indices, the EWM is initially used to pre-process the sample data, mitigating the impact of large differences in the input parameters’ values across various dimensions. Concurrently, the BO algorithm is applied to optimize the crucial hyperparameters of the XGBoost model, thereby effectively enhancing its performance. As part of this study, the Strength–Stress Ratio (SSR), Rock Mass Quality Index in the BQ system ([BQ]), Tunnel Diameter (D), and Support Stiffness (K) are selected as inputs for the tunnel squeezing estimation model. Within the study’s context, the prediction accuracy (Acc) and kappa coefficient (k) of the EWM-BO-XGBoost, XGBoost, BO-XGBoost, Evidence Theory (ET), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) models are computed and compared. The results demonstrate that the Acc (91.7%) and k (0.89) of the EWM-BO-XGBoost model are the highest, attesting to its reliability and superiority over other alternatives. Furthermore, the analysis of the prediction indices’ feature importance reveals that the SSR contributes the most to the squeezing intensity, followed by the D and [BQ], while the K has the least effect on the squeezing intensity. This study can serve as a reference for predicting tunnel squeezing deformation and provide a research foundation for intelligent tunneling operations.

Suggested Citation

  • Xiaojie Geng & Shunchuan Wu & Yanjie Zhang & Junlong Sun & Haiyong Cheng & Zhongxin Zhang & Shijiang Pu, 2023. "Developing hybrid XGBoost model integrated with entropy weight and Bayesian optimization for predicting tunnel squeezing intensity," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 751-771, October.
    • Handle: RePEc:spr:nathaz:v:119:y:2023:i:1:d:10.1007_s11069-023-06137-0
    DOI: 10.1007/s11069-023-06137-0
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    References listed on IDEAS

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