Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks

Research output: Contribution to journalArticle

Abstract

Addressing safety is considered a priority starting from the design stage of any vessel until end-of-life. However, despite all safety measures developed, accidents are still occurring. This is a consequence of the complex nature of shipping accidents where too many factors are involved including human factors. Therefore, there is a need for a practical method, which can identify the importance weightings for each contributing factor involved in accidents. As a result, by identifying the importance weightings for each factor, risk assessments can be informed, and risk control options can be developed and implemented more effectively. To this end, Marine Accident Learning with Fuzzy Cognitive Maps (MALFCM) approach incorporated with Bayesian networks (BNs) is suggested and applied in this study. The MALFCM approach is based on the concept and principles of fuzzy cognitive maps (FCMs) to represent the interrelations amongst accident contributor factors. Thus, MALFCM allows identifying the importance weightings for each factor involved in an accident, which can serve as prior failure probabilities within BNs. Hence, in this study, a specific accident will be investigated with the proposed MALFCM approach.
LanguageEnglish
Number of pages10
JournalSafety in Extreme Environments
Early online date25 Oct 2019
DOIs
Publication statusE-pub ahead of print - 25 Oct 2019

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Bayesian networks
Accidents
Human engineering
Freight transportation
Risk assessment

Keywords

  • maritime accident
  • maritime safety
  • Maritime Accident Learning with Fuzzy Cognitive Maps (MALFCMs)
  • human factors
  • Bayesian networks

Cite this

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title = "Marine accident learning with fuzzy cognitive maps (MALFCMs) and Bayesian networks",
abstract = "Addressing safety is considered a priority starting from the design stage of any vessel until end-of-life. However, despite all safety measures developed, accidents are still occurring. This is a consequence of the complex nature of shipping accidents where too many factors are involved including human factors. Therefore, there is a need for a practical method, which can identify the importance weightings for each contributing factor involved in accidents. As a result, by identifying the importance weightings for each factor, risk assessments can be informed, and risk control options can be developed and implemented more effectively. To this end, Marine Accident Learning with Fuzzy Cognitive Maps (MALFCM) approach incorporated with Bayesian networks (BNs) is suggested and applied in this study. The MALFCM approach is based on the concept and principles of fuzzy cognitive maps (FCMs) to represent the interrelations amongst accident contributor factors. Thus, MALFCM allows identifying the importance weightings for each factor involved in an accident, which can serve as prior failure probabilities within BNs. Hence, in this study, a specific accident will be investigated with the proposed MALFCM approach.",
keywords = "maritime accident, maritime safety, Maritime Accident Learning with Fuzzy Cognitive Maps (MALFCMs), human factors, Bayesian networks",
author = "{Navas de Maya}, Beatriz and Babaleye, {Ahmed O.} and Kurt, {Rafet E.}",
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AU - Kurt, Rafet E.

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KW - human factors

KW - Bayesian networks

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