Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICQR.2011.6031742
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dc.titleAn extended quantitative risk analysis model by incorporating human and organizational factors
dc.contributor.authorWang, Y.F.
dc.contributor.authorXie, M.
dc.contributor.authorNg, K.M.
dc.date.accessioned2014-06-19T04:53:09Z
dc.date.available2014-06-19T04:53:09Z
dc.date.issued2011
dc.identifier.citationWang, Y.F.,Xie, M.,Ng, K.M. (2011). An extended quantitative risk analysis model by incorporating human and organizational factors. 2011 IEEE International Conference on Quality and Reliability, ICQR 2011 : 361-365. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICQR.2011.6031742" target="_blank">https://doi.org/10.1109/ICQR.2011.6031742</a>
dc.identifier.isbn9781457706288
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72281
dc.description.abstractIn this paper, a quantitative risk analysis (QRA) model incorporating human and organizational factor is presented by integrating Fault Tree (FT) with Bayesian Network (BN). FT is used to model the factors how to contribute to the final failures. BN extends the causal chain of basic events to potential human and organizational roots and provides a more precise quantitative links between the event nodes. In order to define the conditional probability table of BN, fuzzy Analytical Hierarchy Process (AHP) is integrated with a decomposition method. The fuzzy AHP helps to reduce the subjective biases by avoiding the need to spell out explicit probability values for the variables' states. The decomposition method breaks the complexity by allowing conditioning on each of the parent nodes separately. The new QRA model is demonstrated on an offshore fire case study. By exploiting the advantages of both models, the method of combining FT and BN is normally a more detailed risk model with higher resolution, comparing with traditional QRA. © 2011 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICQR.2011.6031742
dc.sourceScopus
dc.subjectBayesian Network
dc.subjectFault tree
dc.subjectFuzzy Analytical Hierarchy Process
dc.subjectQuantitative Risk Analysis
dc.typeConference Paper
dc.contributor.departmentINDUSTRIAL & SYSTEMS ENGINEERING
dc.description.doi10.1109/ICQR.2011.6031742
dc.description.sourcetitle2011 IEEE International Conference on Quality and Reliability, ICQR 2011
dc.description.page361-365
dc.identifier.isiutNOT_IN_WOS
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