基于BP神经网络及烟尘沉积特征的单隔间内起火点预测
Fire origin prediction in single compartment based on BP neural network and soot deposition characteristics
牛甜辉 1耿佃桥 1苑轶 2董辉2
作者信息
- 1. 东北大学 材料电磁过程研究教育部重点实验室,辽宁 沈阳 110819;东北大学 冶金学院,辽宁 沈阳 110819
- 2. 东北大学 冶金学院,辽宁 沈阳 110819
- 折叠
摘要
为帮助火灾调查人员更准确、高效地判定起火点,提出了一种基于BP神经网络的起火点预测模型.通过对单隔间火灾烟尘沉积进行数值模拟,构建了59种不同起火点场景下壁面烟尘沉积数据库,并分析了典型起火点场景下的壁面烟尘沉积特征,发现起火点位置与壁面沉积总量及最大浓度平均值之间具有强关联性.选取上述两个参数作为输入,起火点位置作为输出进行神经网络训练,并利用新数据进行预测.结果表明起火点位置预测值的最大绝对误差为0.65 m,最小绝对误差为0.03 m,平均绝对误差为0.37 m,说明本文提出的模型能以较高精度预测起火点位置,是一种较好的火灾调查替代方法.
Abstract
In order to help fire investigators to determine the fire origin more accurately and efficiently,a BP neural network-based fire point prediction model is proposed in this paper.The soot de-position database of wall soot deposition under 59 different fire ori-gin scenarios is constructed by numerical simulation of single com-partment fire,and the wall soot deposition characteristics under representative fire origin scenarios are analyzed,which indicates a strong correlation of the fire origin location with the mass of wall deposition and the average value of the maximum concentration.The above two parameters are selected as input,and the fire ori-gin location is used as output for network training.And the new data is used for prediction.The results show that the maximum absolute error of the predicted value is 0.65 m,the minimum ab-solute error is 0.03 m,and the average absolute error is 0.37 m,indicating that the proposed model can achieve the prediction of fire source location with relatively high accuracy and is a good al-ternative method for fire investigation.
关键词
BP神经网络/烟尘沉积/数值模拟/单隔间/起火点Key words
BP neural network/soot deposition/numerical simu-lation/single compartment/fire origin引用本文复制引用
基金项目
沈阳市科技计划(21-108-9-16)
应急管理部消防救援局科技项目重点项目(2021XFZD13)
出版年
2024
NETL
NSTL
维普
万方数据