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Assessment of HRA method predictions against operating crew performance: Part I: Study background, design and methodology

Author

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  • Liao, Huafei
  • Forester, John
  • Dang, Vinh N.
  • Bye, Andreas
  • Chang, Yung Hsien J.
  • Lois, Erasmia

Abstract

This is the first in a series of three papers documenting two large-scale human reliability analysis (HRA) empirical studies – the International HRA Empirical Study and the US HRA Empirical Study. The two studies are the first major efforts in recent years to benchmark HRA methods by comparing HRA method predictions against actual operator performance in responding to accidents simulated on nuclear power plant (NPP) full-scale simulators. The studies aimed to gain knowledge and insights concerning the strengths and weaknesses of the studied HRA methods and the factors contributing to inter-analyst (or intra-method) variability. In addition, the studies also compared the results of the same HRA method applied by different analysis teams. This paper provides the background and motivation of the studies, the overall study design, the simulation scenarios and human failure events to be analyzed, and concluding remarks concerning lessons learned on benchmarking HRA methods with crew performance of scenarios on NPP simulators.

Suggested Citation

  • Liao, Huafei & Forester, John & Dang, Vinh N. & Bye, Andreas & Chang, Yung Hsien J. & Lois, Erasmia, 2019. "Assessment of HRA method predictions against operating crew performance: Part I: Study background, design and methodology," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:reensy:v:191:y:2019:i:c:s0951832018303661
    DOI: 10.1016/j.ress.2019.106509
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    Citations

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    Cited by:

    1. Paglioni, Vincent P. & Groth, Katrina M., 2022. "Dependency definitions for quantitative human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Zhao, Yunfei, 2022. "A Bayesian approach to comparing human reliability analysis methods using human performance data," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    3. Kim, Yochan & Choi, Sun Yeong & Park, Jinkyun & Kim, Jaewhan, 2022. "Empirical study on human error probability of procedure-extraneous behaviors," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    4. Podofillini, Luca & Reer, Bernhard & Dang, Vinh N., 2021. "Analysis of recent operational events involving inappropriate actions: influencing factors and root causes," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    5. Greco, Salvatore F. & Podofillini, Luca & Dang, Vinh N., 2021. "A Bayesian model to treat within-category and crew-to-crew variability in simulator data for Human Reliability Analysis," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    6. Garg, Vipul & Vinod, Gopika & Prasad, Mahendra & Chattopadhyay, J. & Smith, Curtis & Kant, Vivek, 2023. "Human reliability analysis studies from simulator experiments using Bayesian inference," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    7. Zheng, Xi & Bolton, Matthew L. & Daly, Christopher & Biltekoff, Elliot, 2020. "The development of a next-generation human reliability analysis: Systems analysis for formal pharmaceutical human reliability (SAFPHâ–ª)," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    8. Kim, Yochan & Park, Jinkyun & Presley, Mary, 2021. "Selecting significant contextual factors and estimating their effects on operator reliability in computer-based control rooms," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    9. Zhou, Jian-Lan & Yu, Ze-Tai & Xiao, Ren-Bin, 2022. "A large-scale group Success Likelihood Index Method to estimate human error probabilities in the railway driving process," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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