|本期目录/Table of Contents|

[1]段在鹏,李炯,杨泽鸿,等.改建房结构安全的三阶提升预警模型*[J].中国安全生产科学技术,2024,20(2):52-60.[doi:10.11731/j.issn.1673-193x.2024.02.007]
　DUAN Zaipeng,LI Jiong,YANG Zehong,et al.Three-order improvement early-warning model for structural safety of reconstructed houses[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(2):52-60.[doi:10.11731/j.issn.1673-193x.2024.02.007]
点击复制

改建房结构安全的三阶提升预警模型*
分享到：

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年2期
页码:
52-60
栏目:
学术论著
出版日期:
2024-02-29

文章信息/Info

Title:
Three-order improvement early-warning model for structural safety of reconstructed houses
文章编号:
1673-193X(2024)-02-0052-09
作者:
段在鹏 ; 李炯; 杨泽鸿; 黄豪琪
（1.福州大学 经济与管理学院，福建 福州 350108；
2.福建省应急管理研究中心，福建 福州 350108；
3.福州大学 环境与安全工程学院，福建 福州 350108）
Author(s):
DUAN Zaipeng;  LI Jiong;  YANG Zehong;  HUANG Haoqi
(1.School of Economics and Management,Fuzhou University,Fuzhou Fujian 350108,China;
2.Fujian Emergency Management Research Center,Fuzhou Fujian 350108,China;
3.College of Environment and Safety Engineering,Fuzhou University,Fuzhou Fujian 350108,China)
关键词:
改建房; 深度学习; 集成算法; 生成式对抗网络; 金豺优化算法
Keywords:
reconstructed houses;  deep learning;  integrated algorithm;  generative adversarial networks;  golden jackal optimization algorithm
分类号:
X947；TU18
DOI:
10.11731/j.issn.1673-193x.2024.02.007
文献标志码:
A
摘要:
为研究改建房结构安全问题并提升模型预警精度，采用“文本-图像融合”、“信息再生成”和“智能优化模型参数” 3个方法建立三阶提升预警模型。首先构建基础预警模型：选取VGG16、ResNet50等4种图像识别模型进行迁移学习，将性能最优者作为基础预警模型；之后进行第1次预警精度提升：收集测试集中对应的文本信息，经独热编码等预处理后与图像信息“融合”，优选随机森林等5种算法以提升预警精度；然后进行第2次精度提升：采用过采样-深度卷积生成对抗网络（SMOTE-DCGAN）策略提高模型对隐患改建房的“捕捉”能力；最后，使用金豺优化算法进行第3次提升。研究结果表明：DenseNet121模型更能抓取到隐患改建房图像特征；改建房结构安全预警模型最优的是支持向量机（SVM），准确率为82.5%；使用SMOTE-DCGAN策略后，表现最佳的SVM和XGBoost，其隐患改建房的召回率分别提升10和5个百分点；金豺优化算法下的“SMOTE-DCGAN-SVM”准确率、召回率、精确率和F1值再次提升7.0、7.5、10.5和9.1个百分点。研究结果可为相关部门排查改建房安全隐患提供技术支持。
Abstract:
In order to study the structural safety problem of reconstructed houses and improve the early-warning accuracy of the model,three methods of “text-image fusion”,“information regeneration” and “intelligent optimization model parameters” were used to establish a three-order improvement early-warning model.Firstly,the basic early-warning model was constructed,then four image recognition models such as VGG16 and ResNet50 were selected for transfer learning,and the model with the best performance was used as the basic early-warning model.After that,the first early-warning accuracy improvement was carried out.The text information corresponding to the test set was collected,pre-processed by one-hot coding and “fused” with image information,and five machine learning algorithms such as random forest were selected to improve the early-warning accuracy.Then,the second accuracy improvement was carried out.The “oversampling-deep convolutional generative adversarial network” strategy was used to improve the “capture” ability of the model to the reconstructed houses with potential safety hazard.Finally,the golden jackal optimization algorithm was used for the third improvement.The results show that the DenseNet121 model can better capture the image features of the reconstructed houses with potential safety hazard.The optimal early-warning model for the structural safety of reconstructed houses is SVM,with the accuracy of 82.5%.After using the “oversampling-deep convolutional generative adversarial network” strategy,the SVM and XGBoost with the best performance increase the recall rate of the reconstructed houses with potential safety hazard by 10 and 5 percentage points,respectively.The overall accuracy,recall rate,precision rate and F1 value of “SMOTE-DCGAN-SVM” under the golden jackal optimization algorithm increase by 7.0，7.5，10.5 and 9.1 percentage points.The research results can provide a reference for the relevant departments to infer the reconstructed houses that may have structural safety hazards in advance when conducting the census of dangerous houses.

参考文献/References:

［1］新华社.湖南长沙“4·29”特别重大居民自建房倒塌事故调查报告公布［EB/OL］.(2023-05-21)［2023-06-16］.https://www.mem.gov.cn/xw/bndt/202305/t20230521_451393.shtml.
［2］朱杰,陈洋,蔡乐刚.基于既有房屋信息系统的安全管理预警技术研究与应用［J］.安全与环境工程,2018,25(3):166-171. ZHU Jie,CHEN Yang,CAI Legang.Research andapplication of pre-warning technology for existing houses safety management based on information system［J］.Safety and Environmental Engineering,2018,25(3):166-171.
［3］赵睿.房屋安全管理动态信息化研究［D］.镇江:江苏大学,2018.
［4］赵挺生,任玲玲,周炜,等.基于熵权法-CIM模型的高速公路施工临近房屋安全风险评价［J］.中国安全生产科学技术,2017,13(3):174-179. ZHAO Tingsheng,REN Lingling,ZHOU Wei,et al.Safety risk assessment on buildings adjacent to construction site of expressway based on entropy-weight method and CIM model［J］.Journal of Safety Science and Technology,2017,13(3):174-179.
［5］段在鹏,李帆,邱少辉,等.地铁沿线老旧房屋结构安全预警模型［J］.中国安全生产科学技术,2022,18(3):162-167. DUAN Zaipeng,LI Fan,QIU Shaohui,et al.Early warning model for structural safety of old buildings along metro lines［J］.Journal of Safety Science and Technology,2022,18(3):162-167.
［6］BENDAK S,ALHAMMADI A A.A multi-criteria decision-making approach to minimising fire risk in detached house designs［J］.Journal of Engineering,Design and Technology,2019,17(6):1146-1160.
［7］中华人民共和国住房和城乡建设部.危险房屋鉴定标准：JGJ 125—2016 ［S］.北京：中国建筑工业出版社，2016.
［8］YAO G,LEI T,ZHONG J.A review of convolutional-neural-network-based action recognition［J］.Pattern Recognition Letters,2019,118:14-22.
［9］PINTO G,WANG Z,ROY A,et al.Transfer learning for smart buildings:A critical review of algorithms,applications,and future perspectives［J］.Advances in Applied Energy,2022,5:100084.
［10］TOO E C,LIY J,NJUKI S,et al.A comparative study of fine-tuning deep learning models for plant disease identification［J］.Computers and Electronics in Agriculture,2019,161:272-279.
［11］DRITSAS E,TRIGKA M.Stroke risk prediction with machine learning techniques［J］.Sensors,2022,22(13):4670.
［12］YANG L,AGHAABBASI M,ALI M,et al.Comparative analysis of the optimized KNN,SVM,and ensemble DT models using Bayesian optimization for predicting pedestrian fatalities:an advance towards realizing the sustainable safety of pedestrians［J］.Sustainability,2022,14(17):10467.
［13］BHARTI J P,MISHRA P,MOORTHY U,et al.Slope stability analysis using Rf,Gbm,Cart,Bt and Xgboost［J］.Geotechnical and Geological Engineering,2021,39:3741-3752.
［14］FERNANDEZ A,GARCIA S,HERRERA F,et al.SMOTE for learning from imbalanced data:progress and challenges,marking the 15-year anniversary［J］.Journal of artificial intelligence research,2018,61:863-905.
［15］VIOLA J,CHEN Y Q,WANG J.FaultFace:Deep convolutional generative adversarial network (DCGAN) based ball-bearing failure detection method［J］.Information Sciences,2021,542:195-211.
［16］CHOPRA N,ANSARI M M.Golden jackal optimization:A novel nature-inspired optimizer for engineering applications［J］.Expert Systems with Applications,2022,198:116924.
［17］FAWCETT T.An introduction to ROC analysis［J］.Pattern recognition letters,2006,27(8):861-874.

相似文献/References:

[1]涂思羽,彭平安,蒋元建.基于深度学习的井下环境异常工况智能识别技术研究[J].中国安全生产科学技术,2018,14(11):58.[doi:10.11731/j.issn.1673-193x.2018.11.009]
　TU Siyu,PENG Pingan,JIANG Yuanjian.Research on intelligent recognition technology of abnormal operating conditions in underground environment based on deep learning method[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2018,14(2):58.[doi:10.11731/j.issn.1673-193x.2018.11.009]
[2]刘欣宜,张宝峰,符烨,等.基于深度学习的污染场地作业人员着装规范性检测[J].中国安全生产科学技术,2020,16(7):169.[doi:10.11731/j.issn.1673-193x.2020.07.027]
　LIU Xinyi,ZHANG Baofeng,FU Ye,et al.Detection on normalization of operating personnel dressing at contaminated sites based on deep learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2020,16(2):169.[doi:10.11731/j.issn.1673-193x.2020.07.027]
[3]毕东月.基于深度学习的输煤皮带故障视觉检测方法研究[J].中国安全生产科学技术,2021,17(8):84.[doi:10.11731/j.issn.1673-193x.2021.08.013]
　BI Dongyue.Research on visual detection method for fault of coal conveyor belt based on deep learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(2):84.[doi:10.11731/j.issn.1673-193x.2021.08.013]
[4]崔铁军,王凌霄.YOLOv4目标检测算法在煤矿工人口罩佩戴监测工作中的应用研究*[J].中国安全生产科学技术,2021,17(10):66.[doi:10.11731/j.issn.1673-193x.2021.10.010]
　CUI Tiejun,WANG Lingxiao.Research on application of YOLOv4 object detection algorithm in monitoring on masks wearing of coal miners[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2021,17(2):66.[doi:10.11731/j.issn.1673-193x.2021.10.010]
[5]刘超,雷晨,李树刚,等.基于CNN-GRU的瓦斯浓度预测模型及应用*[J].中国安全生产科学技术,2022,18(9):62.[doi:10.11731/j.issn.1673-193x.2022.09.009]
　LIU Chao,LEI Chen,LI Shugang,et al.Prediction model of gas concentration based on CNN-GRU and its application[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(2):62.[doi:10.11731/j.issn.1673-193x.2022.09.009]
[6]曹亚利,李振雷,刘旭东,等.基于卷积神经网络的冲击地压预警方法研究*[J].中国安全生产科学技术,2022,18(10):101.[doi:10.11731/j.issn.1673-193x.2022.10.015]
　CAO Yali,LI Zhenlei,LIU Xudong,et al.Research on early-warning method of rockburst based on convolutional neural network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(2):101.[doi:10.11731/j.issn.1673-193x.2022.10.015]
[7]李子奇,蒋柱虎,王力,等.基于深度学习的工程结构损伤识别研究进展[J].中国安全生产科学技术,2022,18(12):43.[doi:10.11731/j.issn.1673-193x.2022.12.006]
　LI Ziqi,JIANG Zhuhu,WANG Li,et al.Research progress in damage identification of engineering structure based on deep learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(2):43.[doi:10.11731/j.issn.1673-193x.2022.12.006]
[8]唐豪,奉鑫鑫,高曙,等.基于视频分析的空管员违规行为识别方法*[J].中国安全生产科学技术,2023,19(1):196.[doi:10.11731/j.issn.1673-193x.2023.01.029]
　TANG Hao,FENG Xinxin,GAO Shu,et al.Violations recognition method of air traffic controllers based on video analysis[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(2):196.[doi:10.11731/j.issn.1673-193x.2023.01.029]
[9]黄鹏,房志明,朱曼,等.基于SSD网络的电梯内电动自行车检测研究*[J].中国安全生产科学技术,2023,19(2):167.[doi:10.11731/j.issn.1673-193x.2023.02.023]
　HUANG Peng,FANG Zhiming,ZHU Man,et al.Research on detection of electric bicycles in elevator based on SSD network[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(2):167.[doi:10.11731/j.issn.1673-193x.2023.02.023]
[10]夏正洪,何琥,吴建军,等.基于深度学习的航空铆钉分类及异常情况检测*[J].中国安全生产科学技术,2023,19(6):199.[doi:10.11731/j.issn.1673-193x.2023.06.028]
　XIA Zhenghong,HE Hu,WU Jianjun,et al.Aviation rivet classification and abnormal situation detection based on deep learning[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2023,19(2):199.[doi:10.11731/j.issn.1673-193x.2023.06.028]

备注/Memo

备注/Memo:
收稿日期： 2023-09-09
* 基金项目： 国家社会科学基金项目（23BGL290）
作者简介： 段在鹏，博士，副教授，主要研究方向为智慧安全预警、安全复杂系统分析等。
更新日期/Last Update: 2024-03-11