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A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction

Author

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  • Ping Liu

    (School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Mengchu Xie

    (School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Jing Bian

    (School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China)

  • Huishan Li

    (School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

  • Liangliang Song

    (Institute of Engineering Management, Hohai University, Nanjing 211100, China)

Abstract

Incorporating safety risk into the design process is one of the most effective design sciences to enhance the safety of metro station construction. In such a case, the concept of Design for Safety (DFS) has attracted much attention. However, most of the current research overlooks the risk-prediction process in the application of DFS. Therefore, this paper proposes a hybrid risk-prediction framework to enhance the effectiveness of DFS in practice. Firstly, 12 influencing factors related to the safety risk of metro construction are identified by adopting the literature review method and code of construction safety management analysis. Then, a structured interview is used to collect safety risk cases of metro construction projects. Next, a developed support vector machine (SVM) model based on particle swarm optimization (PSO) is presented to predict the safety risk in metro construction, in which the multi-class SVM prediction model with an improved binary tree is designed. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. The results show that the average accuracy of the test sets is 85.26%, and the PSO–SVM model has a high predictive accuracy for non-linear relationship and small samples. Finally, the proposed framework is applied to a case study of metro station construction. The prediction results show the PSO–SVM model is applicable and reasonable for safety risk prediction. This research also identifies the most important influencing factors to reduce the safety risk of metro station construction, which provides a guideline for the safety risk prediction of metro construction for design process.

Suggested Citation

  • Ping Liu & Mengchu Xie & Jing Bian & Huishan Li & Liangliang Song, 2020. "A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction," IJERPH, MDPI, vol. 17(5), pages 1-24, March.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:5:p:1714-:d:328887
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    References listed on IDEAS

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    Cited by:

    1. Ping Liu & Yu Wang & Tongze Han & Jiaming Xu & Qiangnian Li, 2022. "Safety Evaluation of Subway Tunnel Construction under Extreme Rainfall Weather Conditions Based on Combination Weighting–Set Pair Analysis Model," Sustainability, MDPI, vol. 14(16), pages 1-21, August.

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