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Robust data-driven human reliability analysis using credal networks

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  • Morais, Caroline
  • Estrada-Lugo, Hector Diego
  • Tolo, Silvia
  • Jacques, Tiago
  • Moura, Raphael
  • Beer, Michael
  • Patelli, Edoardo

Abstract

Despite increasing collection efforts of empirical human reliability data, the available databases are still insufficient for understanding the relationships between human errors and their influencing factors. Currently, probabilistic tools such as Bayesian network are used to model data uncertainty requiring the estimation of conditional probability tables from data that is often not available. The most common solution relies on the adoption of assumptions and expert elicitation to fill the gaps. This gives an unjustified sense of confidence on the analysis.

Suggested Citation

  • Morais, Caroline & Estrada-Lugo, Hector Diego & Tolo, Silvia & Jacques, Tiago & Moura, Raphael & Beer, Michael & Patelli, Edoardo, 2022. "Robust data-driven human reliability analysis using credal networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    • Handle: RePEc:eee:reensy:v:218:y:2022:i:pa:s0951832021005007
    DOI: 10.1016/j.ress.2021.107990
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    References listed on IDEAS

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    Cited by:

    1. Liu, Hu-Chen & Wang, Jing-Hui & Zhang, Ling & Zhang, Qi-Zhen, 2022. "New success likelihood index model for large group human reliability analysis considering noncooperative behaviors and social network," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    2. Kim, Yochan & Kim, Jaewhan & Park, Jinkyun, 2023. "A data-informed dependency assessment of human reliability," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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