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Assessment of HRA method predictions against operating crew performance: Part III: Conclusions and achievements

Author

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

Abstract

This is the last 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 goal of the two studies was to develop an empirically-based understanding of the performance, strengths, and weaknesses of HRA methods by comparing HRA method predictions against actual operator performance in simulated accident scenarios on nuclear power plant (NPP) simulators. This paper first addresses areas where there is convergence between the two studies and where differences lie. Then it summarizes the combined insights and conclusions, including key findings on HRA in general through lessons learned about the HRA methods assessed in the studies and specific recommendations for improving guidance, practice and methods. Then it discusses the relevance and usefulness of simulator data for HRA in general. Finally, it presents the key achievements and overall conclusions of the two studies taken together.

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 III: Conclusions and achievements," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:reensy:v:191:y:2019:i:c:s0951832018303685
    DOI: 10.1016/j.ress.2019.106511
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    References listed on IDEAS

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    1. Liao, Huafei & Groth, Katrina & Stevens-Adams, Susan, 2015. "Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 159-169.
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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).

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