中国安全科学学报 ›› 2021, Vol. 31 ›› Issue (1): 165-172.doi: 10.16265/j.cnki.issn 1003-3033.2021.01.024

• 公共安全 • 上一篇    下一篇

城市地下综合管廊电缆舱火灾概率分析方法

米红甫1,2 副教授, 张小梅1,2, 杨文璟1,2, 王文和1,2 教授, 刘亚玲1,2, 焦一飞3   

  1. 1 重庆科技学院 安全工程学院,重庆 401331;
    2 重庆市安全生产科学研究院,重庆 401331;
    3 国网四川省电力公司经济技术研究院,四川 成都 610041
  • 收稿日期:2020-10-10 修回日期:2020-12-15 出版日期:2021-01-28 发布日期:2021-07-28
  • 作者简介:米红甫 (1986—),男,四川南部人,博士,副教授,硕士生导师,主要从事地下空间火灾和油气火灾爆炸安全等方面的研究。E-mail:mimihh5@163.com。
  • 基金资助:
    国家自然科学基金青年基金资助(51704054);重庆市科学技术自然科学基金资助(cstc2019jcyj-msxmX0462);重庆市教委科技研究计划项目(KJQN201801531,KJQN201801519);重庆科技学院研究生科技创新计划项目(YKJCX1920716)。

Fire probability analysis method of cable cabin in urban utility tunnel

MI Hongfu1,2, ZHANG Xiaomei1,2, YANG Wenjing1,2, WANG Wenhe1,2, LIU Yaling1,2, JIAO Yifei3   

  1. 1 College of Safety Engineering, Chongqing University of Science and Technology, Chongqing 401331, China;
    2 Chongqing Academy of Safety Science and Technology, Chongqing 401331, China;
    3 State Grid Sichuan Economic Research Institute, Chengdu Sichuan 610041, China
  • Received:2020-10-10 Revised:2020-12-15 Online:2021-01-28 Published:2021-07-28

摘要: 为提高管廊电缆舱火灾风险评估的准确性,提出基于贝叶斯网络(BN)的电缆舱火灾概率预测分析方法。首先,采用蝴蝶结分析法(BTA),分析电缆舱起火原因,建立潜在的火灾事故场景;其次,考虑火灾事故场景中不确定性因素的影响,将BN应用到电缆舱火灾概率预测分析中,并结合电缆舱火灾发生发展实际优化模型;最后,以某管廊为例,结合文献及统计数据验证该模型逻辑可行。结果表明:通过该模型和方法,能够预测分析综合管廊电缆舱火灾发生发展概率,并且能探究火灾事故致因链条,为综合管廊火灾风险分析和事故防控提供参考。

关键词: 综合管廊, 电缆舱, 火灾概率, 贝叶斯网络(BN), 蝴蝶结分析法(BTA), 最大致因链

Abstract: In order to improve evaluation accuracy of fire risk in cable cabin, a BN-based model for fire probability prediction and analysis was proposed. Firstly, BTA method was used to analyze causes of fire in cable cabin, and potential fire accident scene was established. Then, with influence of uncertain factors in accident scene taken into consideration, BN was applied to fire probability prediction and analysis, and the model was optimized based on actual development of cabin fire. Finally, with a utility tunnel as an example, the model's logic was verified on the ground of literature and statistical data. The results show that this model and method can predict and analyze occurrence and development probability of fire in cable cabin, and explore cause chain of these accidents, which provides a reference for fire risk analysis and accident prevention and control in utility tunnel.

Key words: utility tunnel, cable cabin, fire probability, bayesian networks (BN), bow-tie analysis (BTA), maximum cause chain

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