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A data-informed dependency assessment of human reliability

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  • Kim, Yochan
  • Kim, Jaewhan
  • Park, Jinkyun

Abstract

Dependency assessment is an aspect of human reliability analysis that identifies the causal relationship between two human events and quantifies the conditional probability of the successor event when two or more events exist in an accident sequence. Despite broad recognition of the impact of dependency on the overall system risk, many experts have been concerned that most current methods are rooted in the THERP method without a sufficient theoretical and empirical basis for dependency models. In this study, we propose a method that calculates the conditional failure probability of a successor event based on quantitative evidence of the dependency between two human events. Quantitative assessment is performed by evaluating six features and integrating the failure probabilities due to the features into the assessment based on an arithmetic equation. The estimates obtained from this empirical data analysis, a statistical function for time insufficiency, and a sequence alignment algorithm were employed to support the basis of the calculation with several assumptions. Two case studies are presented to show the feasibility of the study and the result differences between the proposed and existing methods. Since this study presents a new approach to dependency assessment, additional issues to be tackled regarding the assumptions and technical bases used are discussed with further research directions.

Suggested Citation

  • Kim, Yochan & Kim, Jaewhan & Park, Jinkyun, 2023. "A data-informed dependency assessment of human reliability," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:reensy:v:239:y:2023:i:c:s0951832023004532
    DOI: 10.1016/j.ress.2023.109539
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    References listed on IDEAS

    as
    1. 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).
    2. De Ambroggi, Massimiliano & Trucco, Paolo, 2011. "Modelling and assessment of dependent performance shaping factors through Analytic Network Process," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 849-860.
    3. Park, Jooyoung & Boring, Ronald L. & Ulrich, Thomas A. & Lew, Roger & Lee, Sungheon & Park, Bumjun & Kim, Jonghyun, 2022. "A framework to collect human reliability analysis data for nuclear power plants using a simplified simulator and student operators," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    4. 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).
    5. Luca Podofillini & Vinh Dang & Enrico Zio & Piero Baraldi & Massimo Librizzi, 2010. "Using Expert Models in Human Reliability Analysis—A Dependence Assessment Method Based on Fuzzy Logic," Risk Analysis, John Wiley & Sons, vol. 30(8), pages 1277-1297, August.
    6. Wang, Lijing & Wang, Yanlong & Chen, Yingchun & Pan, Xing & Zhang, Wenjin, 2020. "Performance shaping factors dependence assessment through moderating and mediating effect analysis," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    7. Paglioni, Vincent P. & Groth, Katrina M., 2022. "Dependency definitions for quantitative human reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    8. Arigi, Awwal Mohammed & Park, Gayoung & Kim, Jonghyun, 2020. "Dependency analysis method for human failure events in multi-unit probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    Full references (including those not matched with items on IDEAS)

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