A classification scheme of erroneous behaviors for human error probability estimations based on simulator data

다국어 초록 (Multilingual Abstract)

<P><B>Abstract</B></P> <P>Because it has been indicated that empirical data supporting the estimates used in human reliability analysis (HRA) is insufficient, several databases have been constructed recently. To generate quantitative estimates from human reliability data, it is important to appropriately sort the erroneous behaviors found in the reliability data. Therefore, this paper proposes a scheme to classify the erroneous behaviors identified by the HuREX (Human Reliability data Extraction) framework through a review of the relevant literature. A case study of the human error probability (HEP) calculations is conducted to verify that the proposed scheme can be successfully implemented for the categorization of the erroneous behaviors and to assess whether the scheme is useful for the HEP quantification purposes. Although continuously accumulating and analyzing simulator data is desirable to secure more reliable HEPs, the resulting HEPs were insightful in several important ways with regard to human reliability in off-normal conditions. From the findings of the literature review and the case study, the potential and limitations of the proposed method are discussed.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A taxonomy of erroneous behaviors is proposed to estimate HEPs from a database. </LI> <LI> The cognitive models, procedures, HRA methods, and HRA databases were reviewed. </LI> <LI> HEPs for several types of erroneous behaviors are calculated as a case study. </LI> </UL> </P>