Pattern identification and risk prediction of domino effect based on data mining methods for accidents occurred in the tank farm

In recent decades, scarce work was done on risk management of the domino effect by establishing the relationship models between influencing factors and consequences of accidents. In this study, the statistical analysis of the 1144 accidents of tank farms including 100 domino accidents and 1044 non-domino accidents occurred in China from 1960 to 2018 was performed. Unlike the existing statistical analysis literature, the causes of the primary events and the secondary events were separately analyzed to determine the common causes of accident escalation. The C5.0 decision tree algorithm was adopted to extract rules that show the most likely sequences of causal factors for triggering domino accidents. Association rule mining was performed on season factors, units of accidents, operation status and causal factors to predict the causal factors under different process conditions and scenarios. The results showed that inadequate training, inadequate procedure and design deficiency were the most important factors in the C5.0 models on the prediction of domino risks. Six decision rules were learned by C5.0 algorithm and twenty effective rules were learned by the association rule mining, which can jointly provide accurate and reliable prevention strategies and decision support for the risk management of domino accidents.