Data scarcity has always been a significant challenge in the domain of human reliability analysis (HRA). The advancement of simulation technologies provides opportunities to collect human performance data that can facilitate both the development and validation paradigms of HRA. The potential of simulator data to improve HRA can be tapped through the use of advanced machine learning tools like Bayesian methods. Except for Bayesian networks, Bayesian methods have not been widely used in the HRA community. This paper uses a Bayesian method to enhance human error probability (HEP) assessment in offshore emergency situations using data generated in a simulator. Assessment begins by using constrained non-informative priors to define the HEPs in emergency situations. An experiment is then conducted in a simulator to collect human performance data in a set of emergency scenarios. Data collected during the experiment is used to update the priors and obtain informed posteriors. Use of the informed posteriors enable better understanding of the performance, and a more reliable and objective assessment of human reliability, compared to traditional assessment using expert judgment.
- Ocean, Offshore and Arctic Engineering Division
Using Simulator Data to Facilitate Human Reliability Analysis in Offshore Emergency Situations
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Musharraf, M, Moyle, A, Khan, F, & Veitch, B. "Using Simulator Data to Facilitate Human Reliability Analysis in Offshore Emergency Situations." Proceedings of the ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. Volume 11A: Honoring Symposium for Professor Carlos Guedes Soares on Marine Technology and Ocean Engineering. Madrid, Spain. June 17–22, 2018. V11AT12A027. ASME. https://doi.org/10.1115/OMAE2018-78420
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