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.
SHEN Jianhong
,
LIU Shupeng
. Analysis of Causes Related to Construction Accidents
in Metro Tunnel and Station Based on Tree Augmented Naive Bayesian[J]. Tunnel Construction, 2023
, 43(1)
: 27
-35
.
DOI: 10.3973/j.issn.2096-4498.2023.01.003
