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Quantitative risk assessment using hybrid causal logic model

  • Yan Fu Wang
  • , Min Xie
  • , Shahrzad Faghih Roohi

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

This paper presents a hybrid causal logic model, which integrates the traditional Quantitative Risk Assessment (QRA) models with Bayesian Network (BN) incorporating human and organizational factors. The multi-phase model allows different risk assessment methods to be applied to different parts. In the first phase, Event Tree (ET) defines the base scenarios for the source of risk issues. In the second phase, Fault Tree (FT) is used to model the factors how to contributing to the final failures. BN comprise the third phase, which extends the causal chain of basic events to potential human and organizational roots and provide a more precise quantitative links between the event nodes. The new model integrates the power of typical QRA for modeling deterministic causal paths with the flexibility of BN for modeling non-deterministic cause-effect relationships. The integration algorithm is demonstrated on an offshore fire case study. It clearly shows the new model is more flexible and useful than traditional QRA models.
Original languageEnglish
Title of host publicationInternational Topical Meeting on Probabilistic Safety Assessment and Analysis 2011, PSA 2011
Pages121-133
Volume1
Publication statusPublished - 2011
Externally publishedYes
EventInternational Topical Meeting on Probabilistic Safety Assessment and Analysis 2011, PSA 2011 - Wilmington, NC, United States
Duration: 13 Mar 201117 Mar 2011

Publication series

Name
Volume1

Conference

ConferenceInternational Topical Meeting on Probabilistic Safety Assessment and Analysis 2011, PSA 2011
PlaceUnited States
CityWilmington, NC
Period13/03/1117/03/11

Research Keywords

  • Bayesian network
  • Fault tree
  • Human and organizational factors
  • Hybrid causal logic
  • Quantitative risk assessment

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