Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures

A domino effect is a low frequency high consequence chain of accidents where a primary accident (usually fire and explosion) in a unit triggers secondary accidents in adjacent units. High complexity and growing interdependencies of chemical infrastructures make them increasingly vulnerable to domino effects. Domino effects can be considered as time dependent processes. Thus, not only the identification of involved units but also their temporal entailment in the chain of accidents matter. More importantly, in the case of domino-induced fires which can generally last much longer compared to explosions, foreseeing the temporal evolution of domino effects and, in particular, predicting the most probable sequence of accidents (or involved units) in a domino effect can be of significance in the allocation of preventive and protective safety measures. Although many attempts have been made to identify the spatial evolution of domino effects, the temporal evolution of such accidents has been overlooked. We have proposed a methodology based on dynamic Bayesian network to model both the spatial and temporal evolutions of domino effects and also to quantify the most probable sequence of accidents in a potential domino effect. The application of the developed methodology has been demonstrated via a hypothetical fuel storage plant. •A Dynamic Bayesian Network methodology has been developed to model domino effects.•Considering time-dependencies, both spatial and temporal evolutions of domino effects have been modeled.•The concept of most probable sequence of accidents has been proposed instead of the most probable combination of accidents.•Using backward analysis, the most vulnerable units have been identified during a potential domino effect.•The proposed methodology does not need to identify a unique primary unit (accident) for domino effect modeling.