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Research on scenario extrapolation and emergency decision-making for fire and explosion accidents at university laboratories based on BN-CBR

Author

Listed:
  • Liu, Jie
  • Cai, Fei
  • Wang, Wanqing
  • Zhu, Haoyuan
  • Teng, Liangyun
  • Luo, Xuehua
  • Chen, Yi
  • Hao, Chenwei

Abstract

To solve the problems of suddenness, uncertainty and untimely emergency decision-making related to fire and explosion accidents in university laboratories, a combined method of BN and CBR is introduced to analyze laboratory accidents. By summarizing the characteristics of 72 accident cases worldwide, four scenario elements with key roles are extracted by combining the public safety triangle theoretical model; a BN is established from the macro perspective, which is based on the construction of dynamic scenarios; the evolution path is analyzed via BN theory; and the probability of occurrence of accidents is quantified from the microscopic perspective, with a focus on the analysis of the accidental evolution process. A case similarity calculation is carried out via CBR, and the construction of a BN-CBR-assisted decision-making model is completed, verified and corrected in an case study. The results show that the BN-CBR model can quickly determine the accident evolution path and the most similar historical cases, and its quantitative probability calculation enables one to comprehensively grasp the real-time state of the whole accident and the emergency response in a timely manner, which provides a new way to approach emergency decision-making of accidents.

Suggested Citation

  • Liu, Jie & Cai, Fei & Wang, Wanqing & Zhu, Haoyuan & Teng, Liangyun & Luo, Xuehua & Chen, Yi & Hao, Chenwei, 2025. "Research on scenario extrapolation and emergency decision-making for fire and explosion accidents at university laboratories based on BN-CBR," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024006501
    DOI: 10.1016/j.ress.2024.110579
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    References listed on IDEAS

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    2. Ma, Fei & Zhang, Yujie & Liu, Qing & Guo, Yaru & Yang, Zhijie & Zhang, Jiaju, 2025. "Modeling urban transportation safety resilience under extreme rainstorms: A catastrophe theory approach," Reliability Engineering and System Safety, Elsevier, vol. 263(C).
    3. Li, Yuanzhen & She, Yunlei & Shi, Ying & Ding, Rijia, 2025. "Modeling and analysis of open-pit coal mine accident causation based on directed weighted network," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    4. Selivanova, Anna & KrejÄ Ã­, Igor & Sedlářová-Nehézová, Tereza & Hůlka, Jiří & ÄŒeÅ¡pírová, Irena & KuÄ a, Petr, 2025. "Creation of a System Dynamics model of recovery of affected areas after radioactive contamination," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
    5. Huang, Jing & Wang, Ziqing & Sun, Dianchen & Wang, Huimin, 2026. "Scenario deduction for urban rainstorm-induced waterlogging disaster chain based on dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 267(PB).
    6. Lingmei Fu & Jinmei Wang & Qing Yang, 2025. "Exploring Crisis and Conflict Management Through a Scenario Study of a Waste Incineration Project in Hangzhou, China," Sustainability, MDPI, vol. 17(17), pages 1-24, August.
    7. Liu, Jie & Yang, Xiaolin & Yang, Yi & Wang, Wanqing & Chen, Ziyu & Ding, Fanshu & Zhu, Haoyuan, 2025. "Research on fire risk quantification for extralong highway tunnels based on Wuli–Shili–Renli theory, dempster–shafer theory, and bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 264(PB).

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