主管:中华人民共和国应急管理部
主办:应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU

消防科学与技术 ›› 2022, Vol. 41 ›› Issue (4): 491-495.

• • 上一篇    下一篇

基于神经网络的公共建筑应急疏散风险评估方法

李嘉锋1,2,胡玉玲1,2,李佳旭1,2   

  1. (1.北京建筑大学 电气与信息工程学院,北京100044;2. 北京建筑大学 建筑大数据智能处理方法研究北京市重点实验室,北京 100044)
  • 出版日期:2022-04-15 发布日期:2022-04-15
  • 作者简介:李嘉锋(1998-),男,北京人,北京建筑大学电气与信息工程学院硕士研究生,主要从事基于深度学习的应急疏散风险评估,复杂系统建模方面的研究,北京市大兴区永源路15号,100044。
  • 基金资助:
    北京建筑大学基本科研业务基金项目(X20109);国家重点研发项目(2018YFC0807806)

Risk assessment method of emergency evacuation in public buildings based on neural network

LI Jia-feng1,2, HU Yu-ling1,2, LI Jia-xu1,2   

  1. (1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; 2. Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Eneineering and Architecture, Beijing 100044, China)
  • Online:2022-04-15 Published:2022-04-15

摘要: 公共建筑空间大、人员密集、水平疏散距离长,在应急情景下的疏散本身存在一定的风险,提出了一种基于深度神经网络(DNN)的应急疏散风险评估方法。给出了DNN预测模型的建立方法,并以某高校体育馆为案例,说明了模型数据获取、模型训练,及模型测试的整个评估过程。结果表明,相较于传统评估方法,该深度学习方法克服了主观性强、对以人为核心的复杂疏散系统风险评估困难等缺点,可以实现对公共建筑应急疏散快速有效的评估。

Abstract: Public buildings have large spaces, densely populated people, and long horizontal evacuation distances. There are certain risks in the evacuation in emergency situations. This paper proposes an emergency evacuation risk assessment method based on deep neural network (DNN). The establishment method of DNN prediction model is given, and a university gymnasium is used as a case to illustrate the whole evaluation process of model data acquisition, model training, and model testing. The results show that compared with traditional evaluation methods, this deep learning method overcomes the shortcomings of subjectivity and difficulty in risk assessment of complex evacuation systems centered on people, and can realize rapid and effective evaluation of emergency evacuation in public buildings.

Key words: emergency evacuation, deep learning, risk assessment, DNN prediction model, AnyLogic platform