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。
To identify the critical causes of various metro
construction accidents and to support the decisionmaking
of the accidentrelated 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 expectationmaximization 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 riskcausing
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 decisionmaking for risk
prediction under various risk factor combinations. Third, the 10fold crossvalidation confirms the validity
of the model.
