A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction
Bibliographic Data
| ID | 15465657 |
|---|---|
| Authors | Ping Liu (0000-0002-8713-1999, Lanzhou University of Technology), Mengchu Xie (Lanzhou University of Technology), Jing Bian (0000-0002-2846-6529, Chongqing University, corresponding author), Huishan Li (0000-0002-2832-8951, Lanzhou University of Technology), Liangliang Song (0000-0002-4536-0478, Hohai University) |
| Year | 2020 |
| Volume | 17 |
| Issue | 5 |
| Pages | 1714-1714 |
| Publication date | 2020-03-05 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17051714 |
| PMID | 32150993 |
| OpenAlex | W3009169587 |
| Language | EN |
| References cited | 71 |
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
Business · Data mining · Machine learning · Particle swarm optimization · Process (computing · Risk analysis (engineering · Risk model · Support vector machine · Transport engineering · Computer Science · Engineering · Evaluation and Optimization Models · Occupational Health and Safety Research · Risk and Safety Analysis
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| Highly cited | No |
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