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research-article

Rare Event Analysis Considering Data and Model Uncertainty

[+] Author and Article Information
Malak El-Gheriani

Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering & Applied Science , Memorial University, St John's, NL, A1B 3X5, Canada
maeg40@mun.ca

Faisal Khan

Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering & Applied Science , Memorial University, St John's, NL, A1B 3X5, Canada
fikhan@mun.ca

Ming J Zuo

Department of Mechanical Engineering, Faculty of Engineering, University of Alberta, Edmonton, Alberta, T6G 1H9, Canada
mzuo@ualberta.ca

1Corresponding author.

ASME doi:10.1115/1.4036155 History: Received September 19, 2016; Revised March 02, 2017

Abstract

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 with 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 along with HBA to model rare events considering both data and model uncertainty. HBA considers data uncertainty, while BN uses 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.

Copyright (c) 2017 by ASME
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