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A Practical Risk-Based Model for Early Warning of Seafarer Errors Using Integrated Bayesian Network and SPAR-H

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  • Wenjun Zhang

    (Navigation College, Dalian Maritime University, No. 1, Linghai Road, Dalian 116026, China)

  • Xiangkun Meng

    (Navigation College, Dalian Maritime University, No. 1, Linghai Road, Dalian 116026, China)

  • Xue Yang

    (Navigation College, Dalian Maritime University, No. 1, Linghai Road, Dalian 116026, China)

  • Hongguang Lyu

    (Navigation College, Dalian Maritime University, No. 1, Linghai Road, Dalian 116026, China)

  • Xiang-Yu Zhou

    (Navigation College, Dalian Maritime University, No. 1, Linghai Road, Dalian 116026, China)

  • Qingwu Wang

    (Navigation College, Dalian Maritime University, No. 1, Linghai Road, Dalian 116026, China)

Abstract

Unsafe crew acts (UCAs) related to human errors are the main contributors to maritime accidents. The prediction of unsafe crew acts will provide an early warning for maritime accidents, which is significant to shipping companies. However, there exist gaps between the prediction models developed by researchers and those adopted by practitioners in human risk analysis (HRA) of the maritime industry. In addition, most research regarding human factors of maritime safety has concentrated on hazard identification or accident analysis, but not on early warning of UCAs. This paper proposes a Bayesian network (BN) version of the Standardized Plant Analysis Risk–Human Reliability Analysis (SPAR-H) method to predict the probability of seafarers’ unsafe acts. After the identification of performance-shaping factors (PSFs) that influence seafarers’ unsafe acts during navigation, the developed prediction model, which integrates the practicability of SPAR-H and the forward and backward inference functions of BN, is adopted to evaluate the probabilistic risk of unsafe acts and PSFs. The model can also be used when the available information is insufficient. Case studies demonstrate the practicability of the model in quantitatively predicting unsafe crew acts. The method allows evaluating whether a seafarer is capable of fulfilling their responsibility and providing an early warning for decision-makers, thereby avoiding human errors and sequentially preventing maritime accidents. The method can also be considered as a starting point for applying the efforts of HRA researchers to the real world for practitioners.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:16:p:10271-:d:891463
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    References listed on IDEAS

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    1. Bevilacqua, Maurizio & Ciarapica, Filippo Emanuele, 2018. "Human factor risk management in the process industry: A case study," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 149-159.
    2. Utne, Ingrid Bouwer & Rokseth, Børge & Sørensen, Asgeir J. & Vinnem, Jan Erik, 2020. "Towards supervisory risk control of autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    3. Goerlandt, Floris & Montewka, Jakub, 2015. "Maritime transportation risk analysis: Review and analysis in light of some foundational issues," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 115-134.
    4. 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.
    5. Groth, Katrina M. & Swiler, Laura P., 2013. "Bridging the gap between HRA research and HRA practice: A Bayesian network version of SPAR-H," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 33-42.
    6. Meng, Xiangkun & Li, Xinhong & Wang, Weigang & Song, Guozheng & Chen, Guoming & Zhu, Jingyu, 2021. "A novel methodology to analyze accident path in deepwater drilling operation considering uncertain information," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    7. Griffith, Candice D. & Mahadevan, Sankaran, 2015. "Human reliability under sleep deprivation: Derivation of performance shaping factor multipliers from empirical data," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 23-34.
    8. Sotiralis, P. & Ventikos, N.P. & Hamann, R. & Golyshev, P. & Teixeira, A.P., 2016. "Incorporation of human factors into ship collision risk models focusing on human centred design aspects," Reliability Engineering and System Safety, Elsevier, vol. 156(C), pages 210-227.
    9. Pan, Yue & Ou, Shenwei & Zhang, Limao & Zhang, Wenjing & Wu, Xianguo & Li, Heng, 2019. "Modeling risks in dependent systems: A Copula-Bayesian approach," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 416-431.
    10. Wróbel, Krzysztof & Montewka, Jakub & Kujala, Pentti, 2017. "Towards the assessment of potential impact of unmanned vessels on maritime transportation safety," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 155-169.
    11. Fan, Shiqi & Blanco-Davis, Eduardo & Yang, Zaili & Zhang, Jinfen & Yan, Xinping, 2020. "Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
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