Print Email Facebook Twitter An integration of human factors into quantitative risk analysis using Bayesian Belief Networks towards developing a ‘QRA+’ Title An integration of human factors into quantitative risk analysis using Bayesian Belief Networks towards developing a ‘QRA+’ Author Steijn, W.M.P. van Kampen, J.N. van der Beek, D. Groeneweg, J. van Gelder, P.H.A.J.M. Publication year 2020 Abstract Quantitative Risk Analysis (QRA) is a standard tool in some high-risk industries (such as the on- and offshore exploration and production and chemical industry). Presently, existing knowledge concerning human error likelihood and human reliability assessment is insufficiently represented in QRAs. In this paper we attempt to implement the quantification of the human factors in a QRA, which we call QRA+. We analysed a specific incident scenario: the risk of overfilling chemical storage tanks that operate at atmospheric pressure. This scenario was chosen because it is a relevant example of a high-risk scenario in the chemical industry. We identified relevant technological and human parameters within this scenario through on-site visits and interviews with site-experts. The quantitative knowledge concerning the technological parameters was obtained from officially documented SIL statistics, whereas the Standardized Plant Analysis Risk-Human Reliability analysis (SPAR-H) was used to quantify the human factors. Beta distributions were used to model failure probability distributions to account for the uncertainty inherent in dealing with human reliability. For seamless integration of existing qualitative and quantitative knowledge, we made use of a Bayesian Belief Network. The resulting model provides an integrated and more accurate estimation of the failure probabilities for both technological and human factors and the uncertainty surrounding such probability estimates. Furthermore, it gives insight in where these failure probabilities originate and how they interact. This will allow companies to identify those parameters they need to influence to get optimal results concerning their management of risk. © 2019 Elsevier Ltd Subject Atmospheric pressureBayesian networksChemical analysisChemical industryDecision theoryFactor analysisHuman engineeringOffshore oil well productionProbability distributionsReliability analysisRisk assessmentRisk perceptionUncertainty analysisChemical storage tanksHuman reliability analysisHuman reliability assessmentsOffshore explorationQuantitative knowledgeQuantitative risk analysisSeamless integrationTechnological parametersRisk analysisHumanInterviewProbabilityQuantitative analysisReliabilityUncertainty To reference this document use: http://resolver.tudelft.nl/uuid:65b8cb0c-0507-4435-a0ea-0721800b3559 DOI https://doi.org/10.1016/j.ssci.2019.104514 TNO identifier 869710 ISSN 0925-7535 Source Safety Science, 122 Article number 104514 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.