How to address model uncertainty in the escalation of domino effects?

N. Khakzad Rostami*, Paul Amyotte, Valerio Cozzani, Genserik Reniers, Hans Pasman

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

29 Citations (Scopus)


Modeling potential domino scenarios in process plants includes the prediction of the most probable sequence of events and the calculation of respective probabilities, so-called escalation probabilities, so that appropriate prevention and mitigation safety measures can be devised. Domino effect modeling, however, is very challenging mainly due to uncertainties involved in estimation of escalation probabilities (parameter uncertainty) and prediction of the sequence of events during a domino effect (model uncertainty). In the present study, a methodology based on dynamic Bayesian network is developed for identification of the most likely sequence of events in domino scenarios while accounting for model uncertainty. Verifying the accuracy of the methodology based on a comparison with previous studies, the methodology is applied to model single-primary-event and multiple-primary-event domino scenarios in process plants.

Original languageEnglish
Pages (from-to)49-56
Number of pages8
JournalJournal of Loss Prevention in the Process Industries: the international journal of chemical and process plant safety
Publication statusPublished - 2018


  • Domino effect
  • Dynamic Bayesian network
  • Graph theory
  • Model uncertainty
  • Oil terminal


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