A hybrid model for human factor analysis in process accidents: FBN-HFACS

Esmaeil Zarei , Mohammad Yazdi, Rouzbeh Abbassi, Faisal Khan

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Human factors are the largest contributing factors to unsafe operation of the chemical process system. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.
LanguageEnglish
Pages142-155
Number of pages14
JournalJournal of Loss Prevention in the Process Industries
Volume57
Early online date27 Nov 2018
DOIs
Publication statusPublished - Jan 2019

Fingerprint

Factor analysis
accidents
Human engineering
Statistical Factor Analysis
Accidents
Chemical Phenomena
Natural Gas
occupational health and safety
Natural gas pipelines
natural gas
model uncertainty
Fuzzy set theory
risk management
Risk Management
Bayesian networks
Occupational Health
Risk management
Failure modes
Uncertainty
factor analysis

Keywords

  • Process industries
  • HFACS
  • Human reliability assessment
  • Fuzzy AHP
  • Bayesian network

Cite this

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abstract = "Human factors are the largest contributing factors to unsafe operation of the chemical process system. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.",
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A hybrid model for human factor analysis in process accidents : FBN-HFACS. / Zarei , Esmaeil ; Yazdi, Mohammad; Abbassi, Rouzbeh; Khan , Faisal .

In: Journal of Loss Prevention in the Process Industries, Vol. 57, 01.2019, p. 142-155.

Research output: Contribution to journalArticleResearchpeer-review

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