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Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network

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  • Weiliang Qiao
  • Yu Liu
  • Xiaoxue Ma
  • Yang Liu

Abstract

Human factors are widely regarded to be highly contributing factors to maritime accident prevention system failures. The conventional methods for human factor assessment, especially quantitative techniques, such as fault trees and bow‐ties, are static and cannot deal with models with uncertainty, which limits their application to human factors risk analysis. To alleviate these drawbacks, in the present study, a new human factor analysis framework called multidimensional analysis model of accident causes (MAMAC) is introduced. MAMAC combines the human factors analysis and classification system and business process management. In addition, intuitionistic fuzzy set theory and Bayesian Network are integrated into MAMAC to form a comprehensive dynamic human factors analysis model characterized by flexibility and uncertainty handling. The proposed model is tested on maritime accident scenarios from a sand carrier accident database in China to investigate the human factors involved, and the top 10 most highly contributing primary events associated with the human factors leading to sand carrier accidents are identified. According to the results of this study, direct human factors, classified as unsafe acts, are not a focus for maritime investigators and scholars. Meanwhile, unsafe preconditions and unsafe supervision are listed as the top two considerations for human factors analysis, especially for supervision failures of shipping companies and ship owners. Moreover, potential safety countermeasures for the most highly contributing human factors are proposed in this article. Finally, an application of the proposed model verifies its advantages in calculating the failure probability of accidents induced by human factors.

Suggested Citation

  • Weiliang Qiao & Yu Liu & Xiaoxue Ma & Yang Liu, 2020. "Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network," Risk Analysis, John Wiley & Sons, vol. 40(5), pages 957-980, May.
    • Handle: RePEc:wly:riskan:v:40:y:2020:i:5:p:957-980
    DOI: 10.1111/risa.13444
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    2. Laihao Ma & Xiaoxue Ma & Jingwen Zhang & Qing Yang & Kai Wei, 2021. "Identifying the Weaker Function Links in the Hazardous Chemicals Road Transportation System in China," IJERPH, MDPI, vol. 18(13), pages 1-17, July.
    3. Wenjun Zhang & Yingjun Zhang & Weiliang Qiao, 2022. "Risk Scenario Evaluation for Intelligent Ships by Mapping Hierarchical Holographic Modeling into Risk Filtering, Ranking and Management," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    4. Yang Liu & Xiaoxue Ma & Weiliang Qiao & Huiwen Luo & Peilong He, 2021. "Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network," IJERPH, MDPI, vol. 19(1), pages 1-31, December.
    5. Min Zeng & Chuanzhou Dian & Yaoyao Wei, 2022. "Risk Assessment of Insider Threats Based on IHFACS-BN," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    6. Pan, Xing & Zuo, Dujun & Zhang, Wenjin & Hu, Lunhu & Wang, Huixiong & Jiang, Jing, 2021. "Research on Human Error Risk Evaluation Using Extended Bayesian Networks with Hybrid Data," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    7. Weiliang Qiao & Yang Liu & Xiaoxue Ma & He Lan, 2021. "Cognitive Gap and Correlation of Safety-I and Safety-II: A Case of Maritime Shipping Safety Management," Sustainability, MDPI, vol. 13(10), pages 1-24, May.
    8. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Deng, Wanyi, 2023. "Determining the critical risk factors for predicting the severity of ship collision accidents using a data-driven approach," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao, 2022. "On the causation of seafarers’ unsafe acts using grounded theory and association rule," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    10. Ung, S.T., 2021. "Navigation Risk estimation using a modified Bayesian Network modeling-a case study in Taiwan," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    11. Haiying Cheng & Zhun Luo & Nd Seliverstov, 2022. "A Study of Foam Bitumen Preparation for Effective Recycling of Pavement Layers," Sustainability, MDPI, vol. 14(15), pages 1-22, July.
    12. Wenjun Zhang & Xiangkun Meng & Xue Yang & Hongguang Lyu & Xiang-Yu Zhou & Qingwu Wang, 2022. "A Practical Risk-Based Model for Early Warning of Seafarer Errors Using Integrated Bayesian Network and SPAR-H," IJERPH, MDPI, vol. 19(16), pages 1-14, August.
    13. Weiliang Qiao & Enze Huang & Hongtongyang Guo & Yang Liu & Xiaoxue Ma, 2022. "Barriers Involved in the Safety Management Systems: A Systematic Review of Literature," IJERPH, MDPI, vol. 19(15), pages 1-35, August.

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