基于神经网络的公共建筑应急疏散风险评估方法

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

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。

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.