Please wait a minute...
 首页  期刊介绍 期刊订阅 联系我们 横山亮次奖 百年刊庆
 
最新录用  |  预出版  |  当期目录  |  过刊浏览  |  阅读排行  |  下载排行  |  引用排行  |  横山亮次奖  |  百年刊庆
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
全文: PDF(1884 KB)  
输出: BibTeX | EndNote (RIS)      
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。
服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
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
[1] WEI Y Y, ZHANG J Y, WANG J. Research on building fire risk fast assessment method based on fuzzy comprehensive evaluation and SVM[J]. Procedia Engineering, 2018, 211:1141-1150.
[2] DONG X M, LI Y, PAN Y L, et al. Study on urban fire station planning based on fire risk assessment and GIS technology[J]. Procedia Engineering, 2018, 211:124-130.
[3] USFA. Information on the risk, hazard and value evaluation[R]. USA:United States Fire Administration, 1999.
[4] FERREIRA T M, VICENTE R, DA SILVA J A R M, et al. Urban fire risk:Evaluation and emergency planning[J]. Journal of Cultural Heritage, 2016, 20:739-745.
[5] XIN J, HUANG C F. Fire risk analysis of residential buildings based on scenario clusters and its application in fire risk management[J]. Fire Safety Journal, 2013, 62:72-78.
[6] LI S Y, TAO G, ZHANG L J. Fire risk assessment of high-rise buildings based on Gray-FAHP mathematical model[J]. Procedia Engineering, 2018, 211:395-402.
[7] SFPE Risk Task Group. SFPE engineering guide to application of risk assessment in fire protection design[R]. Bethesda:Society of Fire Protection Engineers, 2005.
[8] CODE L S. NFPA 101®[J]. National Fire Protection Assn, Quincy, 2009, 2(1):1-34.
[9] NIMLYAT P S, AUDU A U, OLA-ADISA E O, et al. An evaluation of fire safety measures in high-rise buildings in Nigeria[J]. Sustainable Cities and Society, 2017, 35:774-785.
[10] HANSEN N D, STEFFENSEN F B, VALKVIST M, et al. A fire risk assessment model for residential high-rises with a single stairwell[J]. Fire Safety Journal, 2018, 95:160-169.
[11] ZHANG X, LI X, MEHAFFEY J, et al. A probability-based Monte Carlo life-risk analysis model for fire emergencies[J]. Fire Safety Journal, 2017, 89:51-62.
[12] MATELLINI D B, WALL A D, JENKINSON I D, et al. Modelling dwelling fire development and occupancy escape using Bayesian network[J]. Reliability Engineering & System Safety, 2013, 114:75-91.
[13] GIACHETTI B, COUTON D, PLOURDE F. Smoke spreading analyses in a subway fire scale model[J]. Tunnelling and Underground Space Technology, 2017, 70:233-239.
[14] JIN Y L, JANG B S. Probabilistic fire risk analysis and structural safety assessment of FPSO topside module[J]. Ocean Engineering, 2015, 104:725-737.
[15] LIU F, ZHAO S Z, WENG M C, et al. Fire risk assessment for large-scale commercial buildings based on structure entropy weight method[J]. Safety Science, 2017, 94:26-40.
[16] YI G W, QIN H L. Fuzzy comprehensive evaluation of fire risk on high-rise buildings[J]. Procedia Engineering, 2011, 11:620-624.
[17] WANG Y F, QIN T, LI B, et al. Fire probability prediction of offshore platform based on dynamic Bayesian network[J]. Ocean Engineering, 2017, 145:112-123.
[18] 马德仲, 丁文飞, 刘圣楠, 等. 基于贝叶斯网络的地下空间火灾风险评估方法研究[J]. 中国安全科学学报, 2013, 23(11):151-156. MA D Z, DING W F, LIU S N, et al. Risk assessment method for fire in underground space based on Bayesian network[J]. China Safety Science Journal, 2013, 23(11):151-156. (in Chinese)
[19] 方鸿强, 陈潇, 陆守香. 基于贝叶斯网络的城市火灾风险分析研究[C]//第30届全国高校安全科学与工程学术年会暨第12届全国安全工程领域专业学位研究生教育研讨会论文集. 合肥, 中国:中国科学技术大学, 2018. FANG H Q, CHEN X, LU S X. Urban fire risk analysisbased on Bayesian networks[C]//Proceedings of the 30th National Conference on Safety Science and Engineering in Universities. Hefei, China:University of Science and Technology of China, 2018. (in Chinese)
[20] 中华人民共和国建设部. 城市消防远程监控系统技术规范:GB50440-2007[S]. 北京:中国计划出版社, 2008. Ministry of Construction of the People's Republic of China. Technical code for remote-monitoring system of urban fire protection:GB50440-2007[S]. Beijing:China Planning Publishing House, 2008. (in Chinese)
[21] 虞利强, 杨琦, 黄鹏, 等. 基于物联网技术的消防给水监测系统构建[J]. 消防科学与技术, 2017, 36(7):971-973. YU L Q, YANG Q, HUANG P, et al. Construction of fire water supply monitoring system based on Internet of Things technology[J]. Fire Science and Technology, 2017, 36(7):971-973. (in Chinese)
[22] 范维澄, 孙金华, 陆守香, 等. 火灾风险评估方法学[M]. 北京:科学出版社, 2004. FAN W C, SUN J H, LU S X, et al. Methodology of fire risk assessment[M]. Beijing:Science Press, 2004. (in Chinese)
[23] 张连文, 郭海鹏. 贝叶斯网引论[M]. 北京:科学出版社, 2006. ZHANG L W, GUO H P. Introduction to Bayesian networks[M]. Beijing:Science Press, 2006. (in Chinese)
[24] NORSYS. Netica's help system[R/OL].(2012-12-15).[2019-05-01]. https://www.norsys.com/WebHelp/NETICA.htm.
[25] 范维澄, 王清安, 张人杰, 等. 火灾科学导论[M]. 武汉:湖北科学技术出版社, 1993. FAN W C, WANG Q A, ZHANG R J, et al. Introduction to fire science[M]. Wuhan:Hubei Science and Technology Press, 1993. (in Chinese)
[26] MATHESON J E. Using influence diagrams to value information and control[M]//OLIVER R M, SMITH J Q. Influence Diagrams, Belief Nets and Decision Analysis. New York:John Wiley & Sons, 1990:25-63.
[1] 范晓亮, 彭朝鹏, 郑传潘, 王程. 面向大规模交通网络的时空关联挖掘方法[J]. 清华大学学报(自然科学版), 2023, 63(9): 1317-1325.
[2] 刘康, 刘昭伟, 陈永灿, 马芳平, 王皓冉, 黄会宝, 谢辉. 引水隧洞结构安全风险评价的动态Bayes网络模型[J]. 清华大学学报(自然科学版), 2023, 63(7): 1041-1049.
[3] 杜雨霁, 付明, 端木维可, 侯龙飞, 李静. 基于FAHP-ICV的燃气管网风险评估方法[J]. 清华大学学报(自然科学版), 2023, 63(6): 941-950.
[4] 胡俊, 疏学明, 解学才, 颜峻, 张雷. 基于定量风险评估的建筑火灾保险费率[J]. 清华大学学报(自然科学版), 2023, 63(5): 775-782.
[5] 王佳, 王维曦, 王李韬, 申世飞. 犯罪侦查决策支持模型[J]. 清华大学学报(自然科学版), 2023, 63(10): 1598-1607.
[6] 丁光耀, 陈启航, 徐辰, 钱卫宁, 周傲英. 大数据处理系统中面向GPU加速DNN推理的模型共享[J]. 清华大学学报(自然科学版), 2022, 62(9): 1435-1441.
[7] 魏泽洋, 刘毅, 王春艳, 张佳, 边江, 姚琳洁, 林斯杰, EWEKaijie. 环境计算:概念、发展与挑战[J]. 清华大学学报(自然科学版), 2022, 62(12): 1839-1850.
[8] 巴锐, 张宇栋, 刘奕, 张辉. 城市复杂灾害"三层四域"情景分析方法及应用[J]. 清华大学学报(自然科学版), 2022, 62(10): 1579-1590.
[9] 王飞, 刘金飞, 尹习双, 谭尧升, 周天刚, 杨支跃, 冯博, 杨小龙. 高拱坝智能进度仿真理论与关键技术[J]. 清华大学学报(自然科学版), 2021, 61(7): 756-767.
[10] 姜波, 张超, 陈涛, 袁宏永, 范维澄. 基于Bayes网络的暴雨情景构建和演化方法[J]. 清华大学学报(自然科学版), 2021, 61(6): 509-517.
[11] 郑孟蕾, 田凌. 基于时序数据库的产品数字孪生模型海量动态数据建模方法[J]. 清华大学学报(自然科学版), 2021, 61(11): 1281-1288.
[12] 贾楠, 郭旦怀, 陈永强, 刘奕. 面向社区风险防范的大数据平台理论架构设计[J]. 清华大学学报(自然科学版), 2019, 59(2): 122-128.
[13] 李子浩, 田向亮, 黎忠文, 周炜, 周志杰, 钟茂华. 基于客流规律的地铁车站客流风险分析[J]. 清华大学学报(自然科学版), 2019, 59(10): 854-860.
[14] 陈宇, 王娜, 王晋东. 利用三角模糊数的语言变量项集减项算法[J]. 清华大学学报(自然科学版), 2017, 57(8): 892-896.
[15] 徐远超, 杨璐. 面向高通量应用的众核处理器任务调度[J]. 清华大学学报(自然科学版), 2017, 57(3): 244-249.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 《清华大学学报(自然科学版)》编辑部
本系统由北京玛格泰克科技发展有限公司设计开发 技术支持:support@magtech.com.cn