School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China.
Int J Environ Res Public Health. 2020 Mar 5;17(5):1714. doi: 10.3390/ijerph17051714.
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.
将安全风险纳入设计过程是提高地铁车站建设安全性的最有效设计科学之一。在这种情况下，安全设计（DFS）的概念引起了广泛关注。然而，当前大多数研究都忽略了 DFS 应用中的风险预测过程。因此，本文提出了一种混合风险预测框架，以提高 DFS 在实践中的有效性。首先，通过采用文献综述方法和施工安全管理分析规范，确定了与地铁施工安全风险相关的 12 个影响因素。然后，采用结构化访谈的方法收集地铁施工项目的安全风险案例。接下来，提出了一种基于粒子群优化（PSO）的开发支持向量机（SVM）模型，用于预测地铁施工中的安全风险，其中设计了改进的二叉树的多类 SVM 预测模型。结果表明，测试集的平均准确率为 85.26%，PSO-SVM 模型对非线性关系和小样本具有较高的预测精度。最后，将所提出的框架应用于地铁车站施工的案例研究。预测结果表明，PSO-SVM 模型适用于安全风险预测，且具有合理性。本研究还确定了最重要的影响因素，以降低地铁车站建设的安全风险，为地铁施工的安全风险预测提供了设计过程的指导。
