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

Ping Liu, Mengchu Xie, Jing Bian, Huishan Li and Liangliang Song
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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

IJERPH, 2020, vol. 17, issue 5, 1-24

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.

Keywords: safety risk prediction; metro station construction; design for safety; support vector machine; particle swarm optimization (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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