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清华大学学报(自然科学版)  2020, Vol. 60 Issue (4): 321-327    DOI: 10.16511/j.cnki.qhdxxb.2019.26.036
  物理与物理工程 本期目录 | 过刊浏览 | 高级检索 |
基于Bayes网络的建筑火灾风险评估模型
疏学明1,3, 颜峻2, 胡俊1, 吴津津1, 邓博誉1
1. 清华大学 工程物理系, 公共安全研究院, 北京 100084;
2. 安全工程学院, 中国劳动关系学院, 北京 100048;
3. 城市综合应急科学北京市重点实验室, 北京 100084
Risk assessment model for building fires based on a Bayesian network
SHU Xueming1,3, YAN Jun2, HU Jun1, WU Jinjin1, DENG Boyu1
1. Department of Engineering Physics, Institute of Public Safety Research, Tsinghua University, Beijing 100084, China;
2. China Institute of Industrial Relations, Institute of Safety Engineering, Beijing 100048, China;
3. Beijing Key Laboratory of City Integrated Emergency Response Science, Beijing 100084, China
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摘要 为评估建筑火灾动态风险,从防火工程的角度将火灾发展过程划分为"火情-火警-火险-火灾"4个阶段,分别研究了不同阶段的主要风险评估参数。采用Bayes网络方法构建了动态风险评估模型,确定了网络结构与参数。采用敏感度分析法研究了评估参数对火灾风险的影响程度。以2座典型建筑为例,分别计算得到每个阶段风险和综合风险。研究结果表明:建筑火灾风险是一个动态变化的过程,各阶段风险、评估参数均存在差异;评估节点和依赖关系构成了因果网;评估模型可以有效地将消防监测终端采集的消防大数据与人工智能分析技术相结合,有助于提升建筑消防安全管理的智能化水平。
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疏学明
颜峻
胡俊
吴津津
邓博誉
关键词 安全工程建筑火灾风险评估大数据Bayes网络    
Abstract:The development of building fires was divided into four stages for risk assessment as fire initiation, fire alarm, fire behavior, and fire spreading based on fire engineering theory with analyses of the main risk assessment parameters of each stage. The dynamic risk assessment model was based on a Bayesian network. A sensitivity analysis was then used to evaluate the influences of key parameters on the fire risk. Two typical buildings were then used as examples to evaluate the risk at each fire stage and the overall risk. The results illustrate how the building fire risk is a dynamic process with different risk and impact parameters in each stage. The model nodes and dependencies constitute a causal network. The evaluation model can effectively combine large amounts of fire data collected by a building fire monitoring terminal using artificial intelligence analyses. This research can effectively improve building fire safety management.
Key wordssafety engineering    building fire    risk assessment    big data    Bayesian network
收稿日期: 2019-04-10      出版日期: 2020-04-03
基金资助:国家重点研发计划项目(2017YFC0806600);国家自然科学基金资助项目(71774094,71790613);中国劳动关系研究生教育教学改革项目(YJG1702)
通讯作者: 颜峻,副教授,E-mail:yanjunn@sina.com     E-mail: yanjunn@sina.com
引用本文:   
疏学明, 颜峻, 胡俊, 吴津津, 邓博誉. 基于Bayes网络的建筑火灾风险评估模型[J]. 清华大学学报(自然科学版), 2020, 60(4): 321-327.
SHU Xueming, YAN Jun, HU Jun, WU Jinjin, DENG Boyu. Risk assessment model for building fires based on a Bayesian network. Journal of Tsinghua University(Science and Technology), 2020, 60(4): 321-327.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2019.26.036  或          http://jst.tsinghuajournals.com/CN/Y2020/V60/I4/321
  图1 火灾发展阶段划分图
  表1 火灾阶段影响参数
  图2 建筑火灾动态风险评估 B a y e s网络结构(单位: %)
  表2 “火情”子节点条件概率
  表3 “火灾(f i r es p r e a d)”节点敏感度分析结果
  表4 甲、 乙建筑特征
  图3 建筑火灾阶段风险整合
  图4 建筑火灾各阶段风险雷达图
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