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Major accidents (gray swans) likelihood modeling using accident precursors and approximate reasoning

Citation

Khakzad, N and Khan, FI and Amyotte, P, Major accidents (gray swans) likelihood modeling using accident precursors and approximate reasoning, Risk Analysis, 35, (7) pp. 1336-1347. ISSN 0272-4332 (2015) [Refereed Article]

Copyright Statement

Copyright 2015 Society for Risk Analysis

DOI: doi:10.1111/risa.12337

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 States.

Item Details

Item Type:Refereed Article
Keywords:accident precursor data, approximate reasoning, Bayesian network, information theory, major accident, risk analysis, approximation theory, Bayesian networks, complex networks, information theory, risk analysis, risk assessment, accident precursors
Research Division:Engineering
Research Group:Interdisciplinary Engineering
Research Field:Risk Engineering (excl. Earthquake Engineering)
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Engineering
Author:Khan, FI (Professor Faisal Khan)
ID Code:120664
Year Published:2015
Web of Science® Times Cited:8
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2017-08-30
Last Modified:2017-11-01
Downloads:0

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