Tags

Type your tag names separated by a space and hit enter

Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts.
Ergonomics. 2019 Feb; 62(2):181-191.E

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. Practitioner

Summary:

A model to predict unsafe acts (HFACS level 1) from their preconditions (HFACS level 2) was developed from the analysis of 523 military aircraft accidents using an artificial NN. The results could correctly predict approximately 74% of errors.

Authors+Show Affiliations

a Mobility and Transport Research Centre , Coventry University , Coventry , UK.b Safety and Accident Investigation Centre , Cranfield University , Cranfield , UK.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29155609

Citation

Harris, Don, and Wen-Chin Li. "Using Neural Networks to Predict HFACS Unsafe Acts From the Pre-conditions of Unsafe Acts." Ergonomics, vol. 62, no. 2, 2019, pp. 181-191.
Harris D, Li WC. Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts. Ergonomics. 2019;62(2):181-191.
Harris, D., & Li, W. C. (2019). Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts. Ergonomics, 62(2), 181-191. https://doi.org/10.1080/00140139.2017.1407441
Harris D, Li WC. Using Neural Networks to Predict HFACS Unsafe Acts From the Pre-conditions of Unsafe Acts. Ergonomics. 2019;62(2):181-191. PubMed PMID: 29155609.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts. AU - Harris,Don, AU - Li,Wen-Chin, Y1 - 2017/12/19/ PY - 2017/11/21/pubmed PY - 2019/12/18/medline PY - 2017/11/21/entrez KW - Human Factors Analysis and Classification System (HFACS) KW - Neural Networks KW - accident analysis KW - human error KW - modelling SP - 181 EP - 191 JF - Ergonomics JO - Ergonomics VL - 62 IS - 2 N2 - 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. Practitioner Summary: A model to predict unsafe acts (HFACS level 1) from their preconditions (HFACS level 2) was developed from the analysis of 523 military aircraft accidents using an artificial NN. The results could correctly predict approximately 74% of errors. SN - 1366-5847 UR - https://www.unboundmedicine.com/medline/citation/29155609/Using_Neural_Networks_to_predict_HFACS_unsafe_acts_from_the_pre_conditions_of_unsafe_acts_ L2 - https://www.tandfonline.com/doi/full/10.1080/00140139.2017.1407441 DB - PRIME DP - Unbound Medicine ER -