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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 agencies have begun to collect data on human performance in nuclear power plant simulators [1]. This data provides a valuable opportunity to improve human reliability analysis (HRA), but there improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used in 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 article, 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.
ORCiD logo [1] ;  [2] ;  [1]
  1. Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
  2. Idaho National Lab. (INL), Idaho Falls, ID (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 0951-8320; PII: S0951832014000581
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Reliability Engineering and System Safety
Additional Journal Information:
Journal Volume: 128; Journal ID: ISSN 0951-8320
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; Human Reliability Analysis (HRA); Bayesian inference; simulator data; nuclear power plant; operator performance data
OSTI Identifier: