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Research Papers

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 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

Ming J. Zuo

Department of Mechanical Engineering,
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 3(2), 021008 (Mar 31, 2017) (15 pages) Paper No: RISK-16-1125; doi: 10.1115/1.4036155 History: Received September 19, 2016; Revised March 02, 2017

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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References

Figures

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Fig. 1

Predictive posterior distribution for the probability of failure

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Fig. 2

Simple example of BN with four nodes [30]

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Proposed methodology framework

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Fig. 4

FT for ship-iceberg collision

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BN for ship-iceberg collision

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Posterior predictive distributions for the basic events

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Fig. 7

BN for ship-iceberg collision

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Fig. 8

Abductive reasoning for ship-iceberg collision

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Fig. 10

BN for platform grounding

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Fig. 9

ET for grounding during a tow mission

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Posterior predictive distributions for the initial event and safety barriers

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Fig. 12

BN for platform grounding

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Fig. 19

Abductive reasoning for platform fire and explosion

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Fig. 18

BN for platform fire and explosion

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Posterior predictive distribution for the safety barriers occurrence probabilities

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Fig. 16

Posterior predictive distributions for the basic events

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Fig. 15

BN for platform fire and explosion

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Fig. 14

Bowtie modeling for platform fire and explosion

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Fig. 13

Abductive reasoning for platform grounding

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