Maritime Accident Analysis Using Modified HFACS-MA
Principal Investigator(s): View help for Principal Investigator(s) Eslam A. Youssef, AASTMT
Version: View help for Version V2
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Project Citation:
A. Youssef, Eslam. Maritime Accident Analysis Using Modified HFACS-MA. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-10-02. https://doi.org/10.3886/E194061V2
Project Description
Summary:
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Purpose: In a
previous endeavor, the authors discussed Human Reliability Assessment (HRA),
Human Error Identification (HEI), and accident analysis as the most well-known
accident investigation methods. A general overview of the various analytical
models and methodologies was given, outlining the fundamental concepts behind
each category, and choosing the Maritime Human Factors Analysis and
Classification System (HAFCS-MA) to serve as a starting point for any research
in this field. This particular model is a qualitative analytic model that
examines both active and latent failures revealed in maritime accident reports.
In this paper, a comparative study is
presented among three versions of the HFACS-MA model; a benchmarking was made
with a US government DoD-HFACS version in order to compare the codes inside
each version to ensure the integration of all safety standards. Accordingly, the
most suitable version was singled out.
Design/Methodology/Approach: The modified Maritime Human Factors
Analysis and Classification System (HFACS-MA) was carefully examined as a
qualitative analytic model to evaluate active and latent failures stated in the
contact accident report, identifying human error as the primary cause. This was
done as an example of how the modified (HFACS-MA) may be applied to maritime
accident analysis.
Findings: The HFACS-MA version developed by Kim et
al. (2011) and modified by Kang (2017b) was further modified in the current
work by adding one extra code to it, that is Equipment Design and
Specification, code (i.9).
Scope of Project
Subject Terms:
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Accident investigation;
Human errors;
Maritime accident analysis;
Maritime Human Factors Analysis and Classification System model
Geographic Coverage:
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USA
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