|本期目录/Table of Contents|

[1]赵平,熊倩,张鑫,等.基于YOLO-BP神经网络的古建筑修缮阶段火灾监测方法*[J].中国安全生产科学技术,2020,16(12):122-128.[doi:10.11731/j.issn.1673-193x.2020.12.020]
　ZHAO Ping,XIONG Qian,ZHANG Xin,et al.Monitoring method of fire in repair stage of ancient buildings based on YOLO-BP neural network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2020,16(12):122-128.[doi:10.11731/j.issn.1673-193x.2020.12.020]
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基于YOLO-BP神经网络的古建筑修缮阶段火灾监测方法*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
16
期数:
2020年12期
页码:
122-128
栏目:
职业安全卫生管理与技术
出版日期:
2020-12-31

文章信息/Info

Title:
Monitoring method of fire in repair stage of ancient buildings based on YOLO-BP neural network
文章编号:
1673-193X(2020)-12-0122-07
作者:
赵平; 熊倩; 张鑫; 刘广川
（1.西安建筑科技大学 土木工程学院，陕西 西安 710055；
2.西安建筑科技大学 国家级土木工程虚拟仿真实验教学中心，陕西 西安 710055）
Author(s):
ZHAO Ping; XIONG Qian; ZHANG Xin;  LIU Guangchuan
(1.School of Civil Engineering,Xi’an University of Architecture and Technology,Xi’an Shaanxi 710055,China;
2.National Experimental Teaching Center for Civil Engineering Virtual Simulation (XAUAT),Xi’an University of Architecture and Technology,Xi’an Shaanxi 710055,China)
关键词:
建筑安全; 古建筑; 火灾监测; YOLO算法; 反向传播(BP)神经网络
Keywords:
construction safety;  ancient building;  fire monitoring;  YOLO algorithm;  back propagation (BP) neural network
分类号:
X947
DOI:
10.11731/j.issn.1673-193x.2020.12.020
文献标志码:
A
摘要:
为提高古建筑修缮阶段火灾监测水平，有效预防火灾事故，提出1种基于YOLO-BP神经网络算法的古建筑修缮阶段火灾监测模型。针对修缮作业对古建筑产生的消防隐患，在施工场地设置监测点，并利用YOLO算法以火源、可燃物为目标进行检测与定位，计算火源与可燃物距离；以火源温度、含氧量、火源与可燃物距离3项指标作为BP神经网络输入层，以火灾概率为输出层，从而进行预测；基于古建筑修缮阶段焊接、切割动火作业阶段的样本数据进行仿真模拟。结果表明:利用模型监测古建筑修缮阶段火灾的准确率达93.9%，验证模型的可靠性；当火源温度在150~2 000 ℃范围内，含氧量不小于19.5%时，动火作业安全距离为10.1 m。
Abstract:
In order to improve the level of fire monitoring in the repair stage of ancient buildings and effectively prevent the fire accidents,a fire monitoring model for the repair stage of ancient buildings based on the YOLO-BP neural network algorithm was proposed.In view of the hidden fire hazards of ancient buildings due to repair operation,the monitoring points were set in the construction site,and the YOLO algorithm was used to detect and locate the fire source and combustibles,then the distance between the fire source and combustibles was calculated.Taking three indexes of fire source temperature,oxygen content and distance between fire source and combustibles as the input layer of BP neural network,and the fire probability as the output of output layer,the prediction was conducted.Finally,the sample data of the welding and cutting hot work stages in the repair stage of an ancient building was applied for simulation.The results showed that the accuracy of the model to monitor the fire in the repair stage of ancient building reached 93.9%,which verified the reliability of the model.When the temperature of fire source was in the range of 150~2 000 ℃ and the oxygen content was not less than 19.5%,the safe distance of hot work was 10.1 m.

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备注/Memo

备注/Memo:
收稿日期： 2020-06-07
* 基金项目： 陕西省重点研发计划项目 (2018SF-385）
作者简介： 赵平,博士,教授，主要研究方向为工程项目决策与控制、土木工程施工管理。
通信作者： 熊倩，硕士研究生，主要研究方向为土木工程施工与管理。
更新日期/Last Update: 2021-01-08