Application of Bayesian approach to the assessment of mine gas explosion

Application of Bayesian approach to the assessment of mine gas explosion Frequent mine gas explosion accidents in recent years have caused catastrophic casualties and economic loss in China. In this paper, based on expert knowledge with treatment by the Delphi method to determine conditional probabilities, a Bayesian network (BN) has been developed to investigate the factors influencing mine gas explosion accidents. Based on case analysis of typical mine gas explosion accidents and further evaluation by experts, twenty BN nodes are proposed to represent mine gas explosion process from occurrence causes to explosion impacts, and final consequences. The results of case studies and Sensitivity Analysis (SA) with the proposed Bayesian model indicate that the integration of Bayesian network and Delphi method is an effective framework for dynamically assessing mine gas explosion accident, which could provide a more realistic assessment for emergency decision-making on mine gas explosion disaster response and loss prevention. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Loss Prevention in the Process Industries Elsevier

Application of Bayesian approach to the assessment of mine gas explosion

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0950-4230
eISSN
1873-3352
D.O.I.
10.1016/j.jlp.2018.04.003
Publisher site
See Article on Publisher Site

Abstract

Frequent mine gas explosion accidents in recent years have caused catastrophic casualties and economic loss in China. In this paper, based on expert knowledge with treatment by the Delphi method to determine conditional probabilities, a Bayesian network (BN) has been developed to investigate the factors influencing mine gas explosion accidents. Based on case analysis of typical mine gas explosion accidents and further evaluation by experts, twenty BN nodes are proposed to represent mine gas explosion process from occurrence causes to explosion impacts, and final consequences. The results of case studies and Sensitivity Analysis (SA) with the proposed Bayesian model indicate that the integration of Bayesian network and Delphi method is an effective framework for dynamically assessing mine gas explosion accident, which could provide a more realistic assessment for emergency decision-making on mine gas explosion disaster response and loss prevention.

Journal

Journal of Loss Prevention in the Process IndustriesElsevier

Published: Jul 1, 2018

References

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