A fuzzy set-based approach for modeling dependence among human errors

The assessment of dependence among human errors is an important aspect of human reliability analysis. When dependence between two tasks exists, the probability of the operators' failure on one task is higher if they have failed on the preceding task, compared to when they have succeeded. In current practice, the task of assessing dependence among successive operator actions relies to a great extent to expert judgment, often with lack of traceability and repeatability. To overcome these limitations, this work presents a systematic framework for the elicitation of expert knowledge on the factors influencing the dependence between two successive tasks. The framework is based on a fuzzy expert system in which a set of transparent fuzzy logic rules is used to represent the relationship between the input factors and the conditional human error probability. The proposed modeling approach is applied to two tasks required in response to an accident scenario at a nuclear power plant. Given the methodological scope of the work, the fuzzy expert system is tested directly on a working model of dependence, whereas no actual exercise of expert judgment elicitation is carried out.