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Using catastrophe theory to analyze subway fire accidents

Author

Listed:
  • Xiaofei Lin

    (Beijing Jiaotong University
    Anhui University of Technology)

  • Shouxin Song

    (Beijing Jiaotong University)

  • Huaiyuan Zhai

    (Beijing Jiaotong University)

  • Pengwei Yuan

    (University of Jinan)

  • Mingli Chen

    (Beijing Jiaotong University)

Abstract

Catastrophe theory can describe a continuous process that is undergoing abrupt changes. A dynamic process can be considered to be a swallowtail catastrophe if it has the following six qualities: bimodality, divergence, sudden transitions, hysteresis, inaccessibility and irreversibility. In this paper, the swallowtail catastrophe model is applied to describe the changing dynamic process of subway fire accidents. This dynamic process is also proved to have the six qualities of a swallowtail catastrophe. By using the swallowtail catastrophe model, we construct a model for the subway fire accidents, and we present analyses of subway fire accidents. On the basis of the model and analyses, the dynamic changes in the subway fire accident evolution process can be described with a novel approach. The causes of fire accidents in subways are also discussed, from the perspective of the fire triangle and four elements of an accident. We hope that this study’s theoretical descriptions and discussion of subway fire accidents will facilitate a profound analysis of subway safety.

Suggested Citation

  • Xiaofei Lin & Shouxin Song & Huaiyuan Zhai & Pengwei Yuan & Mingli Chen, 2020. "Using catastrophe theory to analyze subway fire accidents," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(1), pages 223-235, February.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:1:d:10.1007_s13198-019-00942-2
    DOI: 10.1007/s13198-019-00942-2
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    Cited by:

    1. Wang, Yangpeng & Li, Shuxiang & Lee, Kangkuen & Tam, Hwayaw & Qu, Yuanju & Huang, Jingyin & Chu, Xianghua, 2023. "Accident risk tensor-specific covariant model for railway accident risk assessment and prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    2. Senthil Kumar Jagatheesaperumal & Khan Muhammad & Abdul Khader Jilani Saudagar & Joel J. P. C. Rodrigues, 2023. "Automated Fire Extinguishing System Using a Deep Learning Based Framework," Mathematics, MDPI, vol. 11(3), pages 1-18, January.

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