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Deriving causal Bayesian networks from human reliability analysis data: A methodology and example model

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  • Katrina M Groth
  • Ali Mosleh

Abstract

Within the probabilistic risk assessment community, there is a widely acknowledged need to improve the scientific basis of human reliability analysis (HRA). This has resulted in a number of independent research efforts to gather empirical data to validate HRA methods and a number of independent research efforts to improve theoretical models of human performance used in HRA. This paper introduces a methodology for carefully combining multiple sources of empirical data with validated theoretical models to enhance both qualitative and quantitative HRA applications. The methodology uses a comprehensive set of performance influencing factors to combine data from different sources. Further, the paper describes how to use data to gather insights into the relationships among performance influencing factors and to build a quantitative HRA causal model.  To illustrate how the methodology is applied, we introduce the Bayesian network model that resulted from applying the methodology to two sources of human performance data from nuclear power plant operations. The proposed model is introduced to demonstrate how to develop causal insights from HRA data and how to incorporate these insights into a quantitative HRA model. The methodology in this paper provides a path forward for carefully incorporating emerging sources of human performance data into an improved HRA method. The proposed model is a starting point for the next generation of data-informed, theoretically-validated HRA methods.

Suggested Citation

  • Katrina M Groth & Ali Mosleh, 2012. "Deriving causal Bayesian networks from human reliability analysis data: A methodology and example model," Journal of Risk and Reliability, , vol. 226(4), pages 361-379, August.
  • Handle: RePEc:sae:risrel:v:226:y:2012:i:4:p:361-379
    DOI: 10.1177/1748006X11428107
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    Citations

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    Cited by:

    1. Mkrtchyan, L. & Podofillini, L. & Dang, V.N., 2016. "Methods for building Conditional Probability Tables of Bayesian Belief Networks from limited judgment: An evaluation for Human Reliability Application," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 93-112.
    2. 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.
    3. Griffith, Candice D. & Mahadevan, Sankaran, 2015. "Human reliability under sleep deprivation: Derivation of performance shaping factor multipliers from empirical data," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 23-34.
    4. 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.
    5. 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.
    6. 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.
    7. Groth, Katrina M. & Swiler, Laura P., 2013. "Bridging the gap between HRA research and HRA practice: A Bayesian network version of SPAR-H," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 33-42.
    8. Tuqiang Zhou & Junyi Zhang & Dashzeveg Baasansuren, 2018. "A Hybrid HFACS-BN Model for Analysis of Mongolian Aviation Professionals’ Awareness of Human Factors Related to Aviation Safety," Sustainability, MDPI, vol. 10(12), pages 1-20, November.
    9. Groth, Katrina M. & Smith, Reuel & Moradi, Ramin, 2019. "A hybrid algorithm for developing third generation HRA methods using simulator data, causal models, and cognitive science," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    10. Zwirglmaier, Kilian & Straub, Daniel & Groth, Katrina M., 2017. "Capturing cognitive causal paths in human reliability analysis with Bayesian network models," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 117-129.
    11. 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.

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