Using neural networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts

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dc.contributor.authorHarris, Don
dc.contributor.authorLi, Wen-Chin
dc.date.accessioned2018-01-05T14:59:59Z
dc.date.available2018-01-05T14:59:59Z
dc.date.issued2017-12-19
dc.description.abstractHuman Factors Analysis and Classification System (HFACS) is based upon Reason’s organizational model of human error which suggests that there is a ‘one to many’ mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens (HFACS level 1 error and violations). Using accident data derived from 523 military aircraft accidents, the relationship between HFACS level 2 preconditions and level 1 unsafe acts was modelled using an artificial neural network (NN). This allowed an empirical model to be developed congruent with the underlying theory of HFACS. The NN solution produced an average overall classification rate of ca. 74% for all unsafe acts from information derived from their level 2 preconditions. However, the correct classification rate was superior for decision- and skill-based errors, than for perceptual errors and violations.en_UK
dc.identifier.citationHarris D, Li W-C. Using neural networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts. Ergonomics, Volume 62, Issue 2, 2019, pp. 181-191en_UK
dc.identifier.issn0014-0139
dc.identifier.urihttp://dx.doi.org/10.1080/00140139.2017.1407441
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/12853
dc.language.isoenen_UK
dc.publisherTaylor & Francisen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectHuman Factors Analysis & Classification System (HFACS)en_UK
dc.subjectHuman erroren_UK
dc.subjectNeural networksen_UK
dc.subjectModellingen_UK
dc.subjectAccident analysisen_UK
dc.titleUsing neural networks to predict HFACS unsafe acts from the pre-conditions of unsafe actsen_UK
dc.typeArticleen_UK

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