基于机器学习的建筑火灾蔓延快速预测

doi:10.16265/j.cnki.issn1003-3033.2023.11.2219

摘要/Abstract

摘要：

为了迅速预演或重现火场,实时调整火场救援策略,并为建筑设计提供有利于消防工程的空间构成方案,利用火灾动力学软件(FDS)和机器学习技术,研究建筑火灾的关键影响因素。以单室火灾烟气溢出为案例,利用11种空间构成参数和7 776组火灾工况,采用5类机器学习算法训练火场模拟结果数据,并完成算法效率评估。结果表明:机器学习算法适用于建筑空间这类离散型的参数学习与评估预测,它能够直观地给出各参数的权重,挖掘火灾动力学系统中的关键信息,实现火场数据可视化;其中,随机森林(RF)算法具有最高的预测效率,其最佳预测准确率可达91.82%。

关键词: 建筑火灾, 快速预测, 机器学习, 权重占比, 空间构成

Abstract:

In order to quickly rehearse or reproduce the fire scene, the key influencing factors of building fire were studied by using fire dynamics software(FDS) and machine learning technology, so as to adjust the fire rescue strategy in real-time, and finally provide a space composition scheme conducive to fire engineering for architectural design. In this paper, a single-room fire smoke overflow was taken as a case, and 5 kinds of algorithm models were used to perform machine learning training and efficiency evaluation on 11 spatial composition parameters and fire conditions, a total of 7 776 sets of fire simulation result data. The experimental results show that the machine learning algorithm is suitable for parameter learning and evaluation prediction of discrete types such as building space. It can intuitively give the weight of each parameter, mine the key information in the fire dynamics system, and realize the visualization of fire data. The Random Forests(RF) algorithm has the highest prediction efficiency, and its best prediction accuracy can reach 91.82%.

Key words: building fires, rapid fire prediction, machine learning, weight analysis, spatial composition

郭震, 贾笑岩, 李富民, 胡妍, 闫秋艳. 基于机器学习的建筑火灾蔓延快速预测[J]. 中国安全科学学报, 2023, 33(11): 117-125.

GUO Zhen, JIA Xiaoyan, LI Fumin, HU Yan, YAN Qiuyan. Fast prediction for building fire spread based on machine learning[J]. China Safety Science Journal, 2023, 33(11): 117-125.

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序号		开间	进深	门所在位置	门垂壁高度	窗户相对于门所在位置	窗户高度	窗垂壁高度	火源位置 -x	火源位置 -y
1	空间参数	3 000 mm	3 600 mm	开间方向	400 mm	对向	1 500 mm	200 mm	1
1	数据表征	3 000	3 600	1	400	1	1 500	200	-100	120
2	空间参数	3 300 mm	4 200 mm	进深方向	600 mm	左向	1 800 mm	400 mm	5
2	数据表征	3 300	4 200	10	600	10	1 800	400	0	0