Bayesian integration of simulator data and judgment to develop empirically-based reference values for human reliability (2021)

Supervisors: Prof. Dr. Horst-Michael Prasser (ETH Zürich, Switzerland), Prof. Dr. Marloes H. Maathuis (ETH Zürich, Switzerland), Dr. Luca Podofillini (Paul Scherrer Institute, Switzerland)

Keywords: Human Reliability Analysis, control room simulator, data variability, crew behavior models, Bayesian population variability models

The collection of human performance data from plant simulators is receiving renewed attention by the Human Reliability Analysis (HRA) community to improve the empirical basis of our models. However, gaps remain on how to incorporate the data into HRA models.

More information on Salvo's work:
The present Ph.D. work aims at developing new quantitative models, based on Bayesian statistical methods, traceably integrating simulator data and expert judgment in the production of human failure probability values and bounds.

 References and links :

• Greco SF, Podofillini L, and Dang VN. A Bayesian model to treat within-category and crew-to-crew variability in simulator data for Human Reliability Analysis. Reliab Eng Syst Safe 2021, 206:107309, ISSN 0951-8320. https://www.sciencedirect.com/science/article/pii/S095183202030805X
•  Greco SF, Podofillini L, and Dang VN. Crew performance variability in human error probability quantification: a methodology based on behavioral patterns from simulator data. Proc I Mech E Part O: J Risk and Reliability 2021, doi:10.1177/1748006X20986743. https://journals.sagepub.com/doi/abs/10.1177/1748006X20986743