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Marine and Offshore Safety Assessment by Incorporative Risk Modeling in a Fuzzy‐Bayesian Network of an Induced Mass Assignment Paradigm

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  • A. G. Eleye‐Datubo
  • A. Wall
  • J. Wang

Abstract

The incorporation of the human element into a probabilistic risk‐based model is one that requires a possibilistic integration of appropriate techniques and/or that of vital inputs of linguistic nature. While fuzzy logic is an excellent tool for such integration, it tends not to cross its boundaries of possibility theory, except via an evidential reasoning supposition. Therefore, a fuzzy‐Bayesian network (FBN) is proposed to enable a bridge to be made into a probabilistic setting of the domain. This bridge is formalized by way of the mass assignment theory. A framework is also proposed for its use in maritime safety assessment. Its implementation has been demonstrated in a maritime human performance case study that utilizes performance‐shaping factors as the input variables of this groundbreaking FBN risk model.

Suggested Citation

  • A. G. Eleye‐Datubo & A. Wall & J. Wang, 2008. "Marine and Offshore Safety Assessment by Incorporative Risk Modeling in a Fuzzy‐Bayesian Network of an Induced Mass Assignment Paradigm," Risk Analysis, John Wiley & Sons, vol. 28(1), pages 95-112, February.
    • Handle: RePEc:wly:riskan:v:28:y:2008:i:1:p:95-112
    DOI: 10.1111/j.1539-6924.2008.01004.x
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    Cited by:

    1. Yang, Zhisen & Wan, Chengpeng & Yang, Zaili & Yu, Qing, 2021. "Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    2. Yang, Zhisen & Yang, Zaili & Yin, Jingbo & Qu, Zhuohua, 2018. "A risk-based game model for rational inspections in port state control," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 477-495.
    3. Marcelo Ramos Martins & Marcos Coelho Maturana, 2010. "Human Error Contribution in Collision and Grounding of Oil Tankers," Risk Analysis, John Wiley & Sons, vol. 30(4), pages 674-698, April.
    4. Liu, Kezhong & Yu, Qing & Yang, Zhisen & Wan, Chengpeng & Yang, Zaili, 2022. "BN-based port state control inspection for Paris MoU: New risk factors and probability training using big data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    5. Baoping Cai & Yonghong Liu & Zengkai Liu & Xiaojie Tian & Yanzhen Zhang & Renjie Ji, 2013. "Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations," Risk Analysis, John Wiley & Sons, vol. 33(7), pages 1293-1311, July.
    6. Limao Zhang & Xianguo Wu & Yawei Qin & Miroslaw J. Skibniewski & Wenli Liu, 2016. "Towards a Fuzzy Bayesian Network Based Approach for Safety Risk Analysis of Tunnel‐Induced Pipeline Damage," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 278-301, February.
    7. Tzu Yang Loh & Mario P. Brito & Neil Bose & Jingjing Xu & Kiril Tenekedjiev, 2019. "A Fuzzy‐Based Risk Assessment Framework for Autonomous Underwater Vehicle Under‐Ice Missions," Risk Analysis, John Wiley & Sons, vol. 39(12), pages 2744-2765, December.

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