Causal Human Reliability Operator-Centered Models (Chromo) For Identifying and Quantifying Errors In Information-Gathering, Decision Making, And Action Execution

Abstract Human reliability analysis (HRA) characterizes the causes and probabilities of human failure events (HFEs) in complex engineering systems. This work, which is the first of three related papers, seeks to advance the state of the art in HRA by introducing models with: a causally robust theoretical basis, a user-friendly and data-driven quantification scheme, and applicability to a wide range of scenarios. We present a method for developing HRA Bayesian network (BN) model structures applicable to both control room and ex-control room scenarios, and a further method for model parameterization using literature and data. These structures build upon the Information-Decision-Action in Crew Context (IDAC) framework, which considers information-gathering, decision-making, and action execution to be the three main functions of operator actions. We apply this method to develop a causal model for information-gathering HFEs: part of the Causal Human Reliability Operator-centered MOdels (CHROMO). The resulting BN models the causal pathways of these information-gathering HFEs and is capable of quantifying the effects of causal factors on failure probabilities. The BN structures, built with comprehensive data, have been validated through expert discussions and an ATHEANA-based case study on the human failure events at Three Mile Island. These models are currently being incorporated into the Phoenix HRA method and are suitable for regulatory use.