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  • 0248-1

    Final published version, 757 KB, PDF-document

DOI

Success Likelihood Index Model (SLIM) is one of the widely-used methods in human reliability assessment especially when data is insufficient. However, this method suffers from uncertainty as it heavily relies on expert judgment for determining the model parameters such as the rates and weights of the performance shaping factors. The present study is aimed at using Bayesian Network (BN) for improving the performance of SLIM in handling the uncertainty arising from experts opinion and lack of data. To this end, SLIM is combined with BN to form the so-called BN-SLIM technique. We applied both SLIM and BN-SLIM models to a hypothetical example and compared the results. It is shown that BN-SLIM is able to provide a better estimation of human error probability by considering dependencies. The probability updating feature of BN-SLIM in particular makes it possible to use new information to update the prior beliefs about the rates of the performance shaping factors, thus updating the resultant human error probabilities.
Original languageEnglish
Title of host publication29th European Safety and Reliability Conference
EditorsMichael Beer, Enrico Zio
Place of PublicationSingapore
PublisherResearch Publishing
Pages309-315
Number of pages7
ISBN (Print)978-981-11-2724-3
DOIs
Publication statusPublished - 2019
Event29th European Safety and Reliability Conference - Hannover, Germany
Duration: 22 Sep 201926 Sep 2019
https://esrel2019.org/#/

Conference

Conference29th European Safety and Reliability Conference
Abbreviated titleESREL 2019
CountryGermany
CityHannover
Period22/09/1926/09/19
Internet address

    Research areas

  • Human error probability, Uncertainty, Bayesian network, Success likelihood index model

ID: 62168967