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

Date

2017-12-19

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Journal Title

Journal ISSN

Volume Title

Publisher

Taylor & Francis

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Type

Article

ISSN

0014-0139

Format

Citation

Harris 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. 181-191

Abstract

Human 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.

Description

Software Description

Software Language

Github

Keywords

Human Factors Analysis & Classification System (HFACS), Human error, Neural networks, Modelling, Accident analysis

DOI

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Attribution-NonCommercial 4.0 International

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