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Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)

Michael G. Stamatelatos
Michael G. Stamatelatos
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Harold S. Blackman
Harold S. Blackman
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ASME Press
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Worker fatigue is one of many factors that may contribute to human errors in nuclear power plant operation. However, documentation of human errors in licensee event or U.S. Nuclear Regulatory Commission (NRC) inspection reports generally does not identify fatigue as one of these contributing factors. As the impact of fatigue on cognitive functioning is not measured by licensees, worker fatigue is likely under-reported as an event causal factor. Taking this into consideration, the methodology presented in this paper aimed to evaluate the effect of changes in nuclear worker fatigue on the frequency of human performance events without the use of specific historical event data.

Human performance data was extrapolated to replicate work schedule conditions common to power plant operation. The calculated human performance improvement as a result of altered work schedules was assumed to be directly correlated to a change in the human error rate. The human performance change was incorporated into Standardized Plant Analysis Risk (SPAR) models to determine the potential change in a representative plant's calculated core damage frequency (CDF). This change in frequency could then be used to quantify the benefits associated with a reduction in nuclear plant worker fatigue.

The authors implemented this methodology as a supplement to a regulatory analysis for the U.S. Nuclear Regulatory Commission (NRC). It presents a procedure for quantifying fatigue-related human performance and the effects it may have on the nuclear industry and risk to public health. As a result, the methodology advances the ability to address human performance in both Probabilistic Risk Assessment (PRA) and regulatory cost-benefit analyses.

Human Performance Data and Modeling
Integration of Human Performance in SPAR Models
Translation of Human Performance and Plant Risk Metrics into Monetary Benefits
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