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Domino Effect Analysis Using Bayesian Networks
Author(s) -
Khakzad Nima,
Khan Faisal,
Amyotte Paul,
Cozzani Valerio
Publication year - 2013
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.2012.01854.x
Subject(s) - domino effect , domino , bayesian network , bayesian probability , computer science , probabilistic logic , machine learning , bayesian inference , artificial intelligence , path analysis (statistics) , data mining , dynamic bayesian network , path (computing) , bayesian statistics , biochemistry , chemistry , physics , nuclear physics , programming language , catalysis
A new methodology is introduced based on Bayesian network both to model domino effect propagation patterns and to estimate the domino effect probability at different levels. The flexible structure and the unique modeling techniques offered by Bayesian network make it possible to analyze domino effects through a probabilistic framework, considering synergistic effects, noisy probabilities, and common cause failures. Further, the uncertainties and the complex interactions among the domino effect components are captured using Bayesian network. The probabilities of events are updated in the light of new information, and the most probable path of the domino effect is determined on the basis of the new data gathered. This study shows how probability updating helps to update the domino effect model either qualitatively or quantitatively. The methodology is applied to a hypothetical example and also to an earlier‐studied case study. These examples accentuate the effectiveness of Bayesian network in modeling domino effects in processing facility.