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Virtual-reality-generated-data-driven Bayesian networks for risk analysis

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  • Meng, Huixing
  • Zhao, Shijun
  • Song, Wenjuan
  • Hu, Mengqian

Abstract

Risk analysis is crucial to the risk control of major accidents. Therefore, the risk analysis of complex systems has attracted increasing attention from academia and industry. Data-driven Bayesian network (BN) models have proved to be useful for risk analysis in complex systems. Nevertheless, insufficient data remains a challenge for risk analysis. In this paper, we propose a method of virtual reality (VR)-generated data aiming to provide a solution to generate data for risk analysis. To demonstrate the feasibility of VR-generated data applied to data-driven risk analysis, we proposed the following methodology on the example of an emergency response system for deepwater blowout (i.e., a spilled oil collection system). Firstly, a VR model of the spilled oil collection system is established. Secondly, required data is generated from the VR system for the risk analysis of emergency operations. Eventually, the data-driven BN for risk analysis is constructed based on VR-generated data. The results show that VR-generated data can support risk analysis in the presence of limited data.

Suggested Citation

  • Meng, Huixing & Zhao, Shijun & Song, Wenjuan & Hu, Mengqian, 2025. "Virtual-reality-generated-data-driven Bayesian networks for risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025002546
    DOI: 10.1016/j.ress.2025.111053
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    References listed on IDEAS

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    1. Nima Khakzad & Sina Khakzad & Faisal Khan, 2014. "Probabilistic risk assessment of major accidents: application to offshore blowouts in the Gulf of Mexico," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(3), pages 1759-1771, December.
    2. 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).
    3. Wang, Chenyushu & Cai, Baoping & Shao, Xiaoyan & Zhao, Liqian & Sui, Zhongfei & Liu, Keyang & Khan, Javed Akbar & Gao, Lei, 2023. "Dynamic risk assessment methodology of operation process for deepwater oil and gas equipment," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Meng, Huixing & Hu, Mengqian & Kong, Ziyan & Niu, Yiming & Liang, Jiali & Nie, Zhenyu & Xing, Jinduo, 2024. "Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    5. Fan, Shiqi & Yang, Zaili, 2024. "Accident data-driven human fatigue analysis in maritime transport using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Bruce G. Marcot & Anca M. Hanea, 2021. "What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?," Computational Statistics, Springer, vol. 36(3), pages 2009-2031, September.
    7. Wu, Bing & Tang, Yuheng & Yan, Xinping & Guedes Soares, Carlos, 2021. "Bayesian Network modelling for safety management of electric vehicles transported in RoPax ships," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    8. Li, Huanhuan & Ren, Xujie & Yang, Zaili, 2023. "Data-driven Bayesian network for risk analysis of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    9. Zhou, Kaiwen & Xing, Wenbin & Wang, Jingbo & Li, Huanhuan & Yang, Zaili, 2024. "A data-driven risk model for maritime casualty analysis: A global perspective," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    10. Yang, Zhisen & Yang, Zaili & Yin, Jingbo, 2018. "Realising advanced risk-based port state control inspection using data-driven Bayesian networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 38-56.
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