Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Optimal Bayesian MCMC based fire brigade non-suppression probability model considering uncertainty of parameters

  • Kim, Sunghyun (Department of Disaster Prevention Engineering, Chungbuk National University) ;
  • Lee, Sungsu (School of Civil Engineering, Chungbuk National University)
  • Received : 2022.01.02
  • Accepted : 2022.03.12
  • Published : 2022.08.25
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Abstract

The fire brigade non-suppression probability model is a major factor that should be considered in evaluating fire-induced risk through fire probabilistic risk assessment (PRA), and also uncertainty is a critical consideration in support of risk-informed performance-based (RIPB) fire protection decision-making. This study developed an optimal integrated probabilistic fire brigade non-suppression model considering uncertainty of parameters based on the Bayesian Markov Chain Monte Carlo (MCMC) approach on electrical fire which is one of the most risk significant contributors. The result shows that the log-normal probability model with a location parameter (µ) of 2.063 and a scale parameter (σ) of 1.879 is best fitting to the actual fire experience data. It gives optimal model adequacy performance with Bayesian information criterion (BIC) of -1601.766, residual sum of squares (RSS) of 2.51E-04, and mean squared error (MSE) of 2.08E-06. This optimal log-normal model shows the better performance of the model adequacy than the exponential probability model suggested in the current fire PRA methodology, with a decrease of 17.3% in BIC, 85.3% in RSS, and 85.3% in MSE. The outcomes of this study are expected to contribute to the improvement and securement of fire PRA realism in the support of decision-making for RIPB fire protection programs.

Keywords

Acknowledgement

This research was supported by a grant(2020-MOIS35-002) from the Development of Preparedness and Mitigation Technologies Linked to the Countermeasures on Natural Disasters funded by the Ministry of Interior and Safety (MOIS, Korea).

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