基于TAN网络的地铁区间与车站施工事故致因分析

为解决不同类型的地铁施工事故关键致因识别，以便于支持事故相关方在风险分析、预防和控制进行决策的问题。在收集国内2011—2021年间发生的202起事故报告数据的基础上，采用树增强朴素贝叶斯（tree augmented naive, TAN）和EM算法，从事故经过、直接原因、间接原因3个角度分别对事故报告进行统计处理、风险指标提取及合并、风险指标筛选、模型图形结构构建、模型参数确定，并采用GENIE软件训练数据建立最终分析模型。贝叶斯模型分析结果表明： 1）通过正向推理明确不同类型事故的关键致险因素，并对各风险因素引发事故的总体影响程度进行重要度排序； 2）通过反向诊断说明所建模型在不同风险因素组合情境下对风险预测的决策支持作用； 3）10折交叉验证证实了模型的有效性。

Analysis of Causes Related to Construction Accidents in Metro Tunnel and Station Based on Tree Augmented Naive Bayesian

To identify the critical causes of various metro construction accidents and to support the decision making of the accident related parties in risk analysis, prevention, and control, the reported data from 202 metro accidents from 2011 to 2021 in China is collected and statistically processed. The risk indicators are extracted, integrated, and screened. A graphical model structure is constructed and model parameters are determined by tree augmented naive Bayesian and expectation maximization algorithms, respectively, while considering the perspectives of accident occurrence, direct causes, and indirect causes. The final analysis model is established by training with GENIE software. The analytical results of the Bayesian model reveal the following. First, the essential risk causing factors for various types of accidents are clarified through forward reasoning, and the overall degree of impact for each risk factor triggering accidents is ranked in importance. Second, the reverse diagnosis illustrates the role of the proposed model in supporting decision making for risk prediction under various risk factor combinations. Third, the 10 fold cross validation confirms the validity of the model.