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

Author

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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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