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Title: A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods

In the past several years, several international organizations have begun to collect data on human performance in nuclear power plant simulators. The data collected provide a valuable opportunity to improve human reliability analysis (HRA), but these improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this paper, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existing HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.
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Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0951-8320
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Reliability Engineering & System safety; Journal Volume: 128
Research Org:
Idaho National Laboratory (INL)
Sponsoring Org:
Country of Publication:
United States
99 GENERAL AND MISCELLANEOUS Bayesian inference; human performance data; Human reliability analysis (HRA); Nuclear power plant; Simulator data