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A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction

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  • Podofillini, L.
  • Dang, V.N.

Abstract

In human reliability analysis (HRA), models are often used for the prediction of human error probabilities (HEPs), given a set of performance conditions, typically represented by ratings on a set of influencing factors. The relationships underlying these models (yielding HEPs for specific sets of factor ratings) should ideally be built on empirical data. However the derivation of these relationships in practice has to cope with limited availability of data, so that a strong component of expert judgment is always present. Nevertheless, the incorporation of expert judgment in HRA models is typically not done in a formal way, so that that it is often impossible to distinguish source data and judgments. In this context, this paper presents a Bayesian approach to aggregate expert estimates on human error probabilities to determine the relationships of an HRA model. The idea is to build a computable model using information from experts, provided as estimates. A numerical example demonstrates that the approach formally and transparently represents (and distinguishes) the inherent variability of the HEP quantity as well as that of the experts providing their estimates.

Suggested Citation

  • Podofillini, L. & Dang, V.N., 2013. "A Bayesian approach to treat expert-elicited probabilities in human reliability analysis model construction," Reliability Engineering and System Safety, Elsevier, vol. 117(C), pages 52-64.
    • Handle: RePEc:eee:reensy:v:117:y:2013:i:c:p:52-64
    DOI: 10.1016/j.ress.2013.03.015
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    References listed on IDEAS

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    1. Kirwan, Barry & Gibson, W. Huw & Hickling, Brian, 2008. "Human error data collection as a precursor to the development of a human reliability assessment capability in air traffic management," Reliability Engineering and System Safety, Elsevier, vol. 93(2), pages 217-233.
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    Cited by:

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    6. Pandya, Dhruv & Podofillini, Luca & Emert, Frank & Lomax, Antony J. & Dang, Vinh N. & Sansavini, Giovanni, 2020. "Quantification of a human reliability analysis method for radiotherapy applications based on expert judgment aggregation," Reliability Engineering and System Safety, Elsevier, vol. 194(C).
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    9. Musharraf, Mashrura & Bradbury-Squires, David & Khan, Faisal & Veitch, Brian & MacKinnon, Scott & Imtiaz, Syed, 2014. "A virtual experimental technique for data collection for a Bayesian network approach to human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 1-8.
    10. Kim, Yochan & Park, Jinkyun & Jung, Wondea & Jang, Inseok & Hyun Seong, Poong, 2015. "A statistical approach to estimating effects of performance shaping factors on human error probabilities of soft controls," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 378-387.
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    12. Patriarca, Riccardo & Ramos, Marilia & Paltrinieri, Nicola & Massaiu, Salvatore & Costantino, Francesco & Di Gravio, Giulio & Boring, Ronald Laurids, 2020. "Human reliability analysis: Exploring the intellectual structure of a research field," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    13. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2015. "Bayesian belief networks for human reliability analysis: A review of applications and gaps," Reliability Engineering and System Safety, Elsevier, vol. 139(C), pages 1-16.
    14. 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.
    15. Podofillini, Luca & Reer, Bernhard & Dang, Vinh N., 2023. "A traceable process to develop Bayesian networks from scarce data and expert judgment: A human reliability analysis application," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    16. Ning Wang & Cheng-shun Xu & Xiu-li Du & Ming-ju Zhang, 2018. "A risk assessment method of deep excavation based on Bayesian analysis and expert elicitation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(2), pages 452-466, April.
    17. 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).
    18. Su, Xiaoyan & Mahadevan, Sankaran & Xu, Peida & Deng, Yong, 2014. "Inclusion of task dependence in human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 41-55.
    19. Liu, Jianqiao & Zou, Yanhua & Wang, Wei & Zio, Enrico & Yuan, Chengwei & Wang, Taorui & Jiang, Jianjun, 2022. "A Bayesian belief network framework for nuclear power plant human reliability analysis accounting for dependencies among performance shaping factors," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    20. Groth, Katrina M. & Smith, Curtis L. & Swiler, Laura P., 2014. "A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods," Reliability Engineering and System Safety, Elsevier, vol. 128(C), pages 32-40.
    21. Liu, Peng & Qiu, Yongping & Hu, Juntao & Tong, Jiejuan & Zhao, Jun & Li, Zhizhong, 2020. "Expert judgments for performance shaping Factors’ multiplier design in human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 194(C).

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