Estimating probability of success of escape, evacuation, and rescue (EER) on the offshore platform by integrating Bayesian Network and Fuzzy AHP

Estimating probability of success of escape, evacuation, and rescue (EER) on the offshore... Reliable Escape, Evacuation, and Rescue (EER) could have averted or reduced the catastrophic consequences of marine disasters. This paper presents a model to estimate the probability of successful EER on the offshore platforms. The proposed model consists of two parts. The first part uses fault tree method to analyze factors that affect the success of EER qualitatively, and these influencing factors are treated as nodes of Bayesian network (BN) in the next phase. A quantitative analysis model is constructed using BN in the second part. The BN is mapped from the fault tree model in the first part. Since one of the most important steps in BN analysis is to determine the CPT between nodes, fuzzy analytical hierarchy process (fuzzy AHP) and decomposition method are applied to estimate the CPTs of BN. Probabilities of successful EER are calculated by using BN, and the most influencing factors for the success of EER are determined based on sensitivity analysis (SA). In order to demonstrate the proposed method, a case study is made and results show that the proposed model is capable of finding out potential critical risk factors in EER and carrying out quantitative analysis of EER. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Loss Prevention in the Process Industries Elsevier

Estimating probability of success of escape, evacuation, and rescue (EER) on the offshore platform by integrating Bayesian Network and Fuzzy AHP

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0950-4230
eISSN
1873-3352
D.O.I.
10.1016/j.jlp.2018.02.007
Publisher site
See Article on Publisher Site

Abstract

Reliable Escape, Evacuation, and Rescue (EER) could have averted or reduced the catastrophic consequences of marine disasters. This paper presents a model to estimate the probability of successful EER on the offshore platforms. The proposed model consists of two parts. The first part uses fault tree method to analyze factors that affect the success of EER qualitatively, and these influencing factors are treated as nodes of Bayesian network (BN) in the next phase. A quantitative analysis model is constructed using BN in the second part. The BN is mapped from the fault tree model in the first part. Since one of the most important steps in BN analysis is to determine the CPT between nodes, fuzzy analytical hierarchy process (fuzzy AHP) and decomposition method are applied to estimate the CPTs of BN. Probabilities of successful EER are calculated by using BN, and the most influencing factors for the success of EER are determined based on sensitivity analysis (SA). In order to demonstrate the proposed method, a case study is made and results show that the proposed model is capable of finding out potential critical risk factors in EER and carrying out quantitative analysis of EER.

Journal

Journal of Loss Prevention in the Process IndustriesElsevier

Published: Jul 1, 2018

References

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