title: Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning.
abstract: Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well-established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents' relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor-based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates.
authors.affiliation: Safety and Risk Engineering Group, Memorial University of Newfoundland, St. John's, NL, Canada, A1B3X5.
authors.forename: Nima
authors.initials: N
authors.lastname: Khakzad
authors.affiliation: Safety and Risk Engineering Group, Memorial University of Newfoundland, St. John's, NL, Canada, A1B3X5.
authors.forename: Faisal
authors.initials: F
authors.lastname: Khan
authors.affiliation: Department of Process Engineering & Applied Science, Dalhousie University, Halifax, NS, Canada, B3J2X4.
authors.forename: Paul
authors.initials: P
authors.lastname: Amyotte
keywords: Accident precursor data; Bayesian network; approximate reasoning; information theory; major accident; risk analysis