Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects

N. Khakzad Rostami, Gabriele Landucci, Genserik Reniers

Research output: Contribution to journalArticleScientificpeer-review

35 Citations (Scopus)
66 Downloads (Pure)

Abstract

The propagation of fire in chemical plants – also known as fire domino effects - largely depends on the performance of add-on passive and active protection systems such as sprinkler systems, water deluge systems, emergency shut down and emergency blow down systems, fireproofing, and emergency response. Although such safety barriers are widely employed to prevent or delay the initiation or escalation of fire domino effects, their inclusion in the modeling and risk assessment of fire domino effects has hardly been taken into account. In the present study, the dynamic evolution of fire protection systems has been investigated qualitatively using event tree analysis. To quantify the temporal changes and their impact on the escalation of fire domino effects, a dynamic Bayesian network methodology has been developed. The application of the methodology has been demonstrated using an illustrative case study, considering a variety of fire scenarios, target installations, and firefighting systems.

Original languageEnglish
Pages (from-to)232-247
Number of pages16
JournalReliability Engineering & System Safety
Volume167
DOIs
Publication statusPublished - 1 Nov 2017

Keywords

  • Dynamic Bayesian network
  • Event tree analysis
  • Fire domino effects
  • Fire protection systems
  • Quantitative risk assessment

Fingerprint Dive into the research topics of 'Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects'. Together they form a unique fingerprint.

Cite this