Risk analysis of dam break accident combining case mining and Bayesian network

To deeply and comprehensively explore the mechanism of the risk caused by dam break accident, this study proposes a calculation method of the risk caused by dam break accident through combining the mining of historical dam break accident cases and Bayesian network. Based on a large number of historical cases of dam break accidents at home and abroad, 24Model is used to identify and extract the causes and chain of dam break accidents. The topology structure caused by dam break accident is constructed, and the probability of dam break is calculated by Bayesian forward causal reasoning, and the mechanism of dam break is analyzed by reverse diagnostic reasoning. Based on the Bayesian sensitivity analysis, the key risk factors affecting dam failure are explored. The results show that in terms of human factors, the proportion of gate control problems is high, while in terms of management factors, construction problems, operation and maintenance management defects, and design problems are important indirect causes of dam break. Flood overtopping and seepage erosion/piping are the main risk factors leading to dam failure.