Creating formative HRA dependency models using the HRA dependency idioms and SACADA data, Part II: Model quantification

Abstract

Human reliability analysis (HRA) identifies the causal factors impacting the occurrence of human failure events and quantifies the human failure event probabilities based on those causal factors, and requires understanding the dependency structures that exist between failure events and causal factors. Many HRA methods incorporate the dependency framework established in the Technique for Human Error Rate Prediction (THERP), which uses simple multipliers on human error probabilities (HEPs) resulting from considering only a few factors. Accordingly, those HRA methods have a limited ability and accuracy when characterizing dependency. This paper presents a methodology for using HRA data to quantify Bayesian networks built from the HRA dependency idioms. The result is an objective and traceable human reliability model that enforces formative, rather than summative, dependency. The results show that baseline HEPs in this model are reasonable, and the data-informed dependency is a significant improvement for the field.