Human reliability: Benchmark and prediction

Abstract

A straightforward, general, and simple methodology is given for evaluating and predicting the human error probability (HEP) in transients for accident risk prediction and reduction purposes. The result is validated against all the available data, producing a completely independent assessment of the uncertainty in HEP for safety and risk analysis, and a new validated prediction method. Previous published work established the technical basis for the existence of learning curves for predicting human performance, system outcomes, and accident rates, which was derived and validated against extensive system outcomes and human learning trial data. This latest second-generation minimum error rate equation (MERE)/universal learning curve (ULC) prediction is based on the well-known and proven learning hypothesis. It is therefore applicable to transient and accident analysis, providing the probability of failure or success for individuals as well as for systems. In this paper, new simulator data and three major existing human reliability analysis (HRA) methods used in safety and risk analysis (e.g. technique of human error rate prediction (THERP), human error and assessment technique (HEART), and human cognitive reliability (HCR)) are benchmarked against the new prediction derived directly from the general ULC. Since these existing methods utilize empirical factors, expert judgement and task analysis, the paper demonstrates a totally new objective approach to benchmark human reliability analysis.