In risk analysis of rare events, there is a need to adopt data from different sources with varying levels of detail (e.g., local, regional, categorical data). Therefore, it is very important to identify, understand, and incorporate the uncertainty that accompanies the data. Hierarchical Bayesian analysis (HBA) addresses uncertainty among the aggregated data for each event through generating an informative prior distribution for the event's parameter of interest. The Bayesian network (BN) approach is used to model accident causation. BN enables both inductive and abductive reasoning, which helps to better understand and minimize model uncertainty. In this work, the methodology is proposed to integrate BN with HBA to model rare events, considering both data and model uncertainty. HBA considers data uncertainty, while BN uses an adaptive model to better represent and manage model uncertainty. Application of the proposed methodology is demonstrated using three types of offshore accidents. The proposed methodology provides a way to develop a dynamic risk analysis approach to rare events.
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June 2017
Research-Article
Rare Event Analysis Considering Data and Model Uncertainty
Malak El-Gheriani,
Malak El-Gheriani
Centre for Risk, Integrity and Safety Engineering
(C-RISE),
Faculty of Engineering and Applied Science,
Memorial University,
St John's, NL A1B 3X5, Canada
e-mail: maeg40@mun.ca
(C-RISE),
Faculty of Engineering and Applied Science,
Memorial University,
St John's, NL A1B 3X5, Canada
e-mail: maeg40@mun.ca
Search for other works by this author on:
Faisal Khan,
Faisal Khan
Centre for Risk, Integrity and Safety Engineering
(C-RISE),
Faculty of Engineering and Applied Science,
Memorial University,
St John's, NL A1B 3X5, Canada
e-mail: fikhan@mun.ca
(C-RISE),
Faculty of Engineering and Applied Science,
Memorial University,
St John's, NL A1B 3X5, Canada
e-mail: fikhan@mun.ca
Search for other works by this author on:
Ming J. Zuo
Ming J. Zuo
Department of Mechanical Engineering,
Faculty of Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada
e-mail: ming.zuo@ualberta.ca
Faculty of Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada
e-mail: ming.zuo@ualberta.ca
Search for other works by this author on:
Malak El-Gheriani
Centre for Risk, Integrity and Safety Engineering
(C-RISE),
Faculty of Engineering and Applied Science,
Memorial University,
St John's, NL A1B 3X5, Canada
e-mail: maeg40@mun.ca
(C-RISE),
Faculty of Engineering and Applied Science,
Memorial University,
St John's, NL A1B 3X5, Canada
e-mail: maeg40@mun.ca
Faisal Khan
Centre for Risk, Integrity and Safety Engineering
(C-RISE),
Faculty of Engineering and Applied Science,
Memorial University,
St John's, NL A1B 3X5, Canada
e-mail: fikhan@mun.ca
(C-RISE),
Faculty of Engineering and Applied Science,
Memorial University,
St John's, NL A1B 3X5, Canada
e-mail: fikhan@mun.ca
Ming J. Zuo
Department of Mechanical Engineering,
Faculty of Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada
e-mail: ming.zuo@ualberta.ca
Faculty of Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada
e-mail: ming.zuo@ualberta.ca
1Corresponding author.
Manuscript received September 19, 2016; final manuscript received March 2, 2017; published online March 31, 2017. Assoc. Editor: Konstantin Zuev.
ASME J. Risk Uncertainty Part B. Jun 2017, 3(2): 021008 (15 pages)
Published Online: March 31, 2017
Article history
Received:
September 19, 2016
Revised:
March 2, 2017
Citation
El-Gheriani, M., Khan, F., and Zuo, M. J. (March 31, 2017). "Rare Event Analysis Considering Data and Model Uncertainty." ASME. ASME J. Risk Uncertainty Part B. June 2017; 3(2): 021008. https://doi.org/10.1115/1.4036155
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