Retrospective Bayesian Analysis of Tunnel Construction Accidents: An Exploratory Study Using Theory-Constrained Association Rules

Tunnel construction accidents arise from factors operating across organisational and operational levels. Using investigation reports from 88 tunnel construction accidents in China between 2010 and 2025, this study developed an exploratory framework for retrospective probabilistic inference. The reports and the 24Model informed a hierarchy linking safety culture, safety management, unsafe acts and conditions, and accident occurrence. The Apriori algorithm screened associations between adjacent layers using prespecified criteria. The retained associations defined the network structure, which was parameterised using smoothed conditional probability tables estimated from accident data and expert-elicited Noisy-MAX parameters. In five-fold internal cross-validation, the fixed-structure network and the networks reconstructed within training folds had higher overall probabilistic losses than the training-fold prevalence baseline when reconstructing masked risk factor states from the remaining observed factors. At the node level, UA3, UC2 and UC3 showed relatively good discrimination between states. Sensitivity and maximum J-divergence analyses characterised local parameter responses and direct probabilistic dependencies, respectively. The Manme Tunnel case, which both experts knew before parameter elicitation, illustrated how partial accident evidence updated the probabilities of unobserved risk factors. The framework provides a basis for retrospective probabilistic analysis of tunnel construction accidents, with its added value for state reconstruction and its diagnostic validity requiring independent evaluation.