@article{ref1,
title="Risk assessment method of emergency evacuation in public buildings based on neural network",
journal="Fire science and technology (Beijing)",
year="2022",
author="Jia-feng, Li and Yu-ling, Hu and Jia-xu, Li",
volume="41",
number="4",
pages="491-495",
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.   http://www.xfkj.com.cn/EN/Y2022/V41/I4/491<p /> <p>Language: zh</p>",
language="zh",
issn="1009-0029",
doi="",
url="http://dx.doi.org/"
}