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Volume 45 Issue 4
Apr.  2023
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ZHANG Lingling, REN Panpan, XU Ao, ZHANG Jiran, DING Libin, AN Chaofeng, WU Song. On-Site Detection of Airtightness of Building Windows Based on Infrared Image Processing[J]. Infrared Technology , 2023, 45(4): 410-416.
Citation: ZHANG Lingling, REN Panpan, XU Ao, ZHANG Jiran, DING Libin, AN Chaofeng, WU Song. On-Site Detection of Airtightness of Building Windows Based on Infrared Image Processing[J]. Infrared Technology , 2023, 45(4): 410-416.
shu

On-Site Detection of Airtightness of Building Windows Based on Infrared Image Processing

  • 1.

    School of Architecture, Yantai University, Yantai 264005, China

  • 2.

    China Railway Construction Engineering Second Construction Co., Ltd., Qingdao 266112, China

  • 3.

    Yantai Dongfang Energy Technology Co., Ltd., Yantai 264005, China

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  • Received Date: May 19, 2022
  • Revised Date: July 10, 2022
    Graphical Abstract
    • Abstract
  • Current methods of on-site detection of airtightness of building windows cannot ensure that the airtightness grades of all windows satisfy the standard. Moreover, there is a lack of efficient and convenient detection methods. Thus, we proposed an on-site method to detect the airtightness performance level of windows. In this study, an infrared image of the windows is collected using a thermal imager, the abnormal area in the image is detected and the defect area is calculated, then an infrared detection model for window defects is established. Based on the experimentally measured indoor–outdoor temperature difference, the defect area of the window and air infiltration, a calculation model for the air infiltration of windows is built. The model is combined with the infrared detection model of exterior windows defects to obtain the air infiltration of the window, and the on-site detection of the windows airtightness performance is realized and then preliminary determine of whether the window meets the corresponding airtightness performance level, which improves the efficiency of the on-site inspection of the airtightness performance of windows and provides a new method for the on-site determination of the airtightness performance level of windows.
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    • References (28)
  • [1]
    中国建筑节能协会. 中国建筑能耗研究报告2020[J]. 建筑节能, 2021, 49(2): 1-6. https://www.cnki.com.cn/Article/CJFDTOTAL-FCYY202102001.htm

    China Association of Building Energy Efficiency. China building energy consumption annual report 2020[J]. Building Energy Efficiency, 2021, 49(2): 1-6. https://www.cnki.com.cn/Article/CJFDTOTAL-FCYY202102001.htm
    [2]
    郭兴忠, 杨闯, 张超, 等. 节能门窗热工性能对建筑能耗影响的模拟研究[J]. 建筑材料学报, 2014, 17(2): 261-265, 297. DOI: 10.3969/j.issn.1007-9629.2014.02.014

    GUO Xingzhong, YANG Chuang, ZHNAG Chao, et al. Simulation on thermal performance of energy-saving windows and door and its influence on building energy consumption[J]. Journal of Building Materials, 2014, 17(2): 261-265, 297. DOI: 10.3969/j.issn.1007-9629.2014.02.014
    [3]
    中国建筑科学研究院. 建筑幕墙、门窗通用技术条件: GB/T 31433-2015[S]. 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会.

    China Academy of Building Research. General specification for building curtain wall, windows and doors: GB/T 31433-2015[S]. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China; Standardization Administration of the People's Republic of China.
    [4]
    中国建筑科学研究院有限公司. 建筑外门窗气密、水密、抗风压性能检测方法: GB/T 7106-2019[S]. 国家市场监督管理总局, 国家标准化管理委员会.

    China Academy of Building Research. Test methods of air permeability, watertightness, wind load resistance performance for building external windows and doors: GB/T 7106-2019[S]. State Administration for Market Regulation; Standardization Administration.
    [5]
    陈航. 建筑门窗三性检测方法及常见问题分析[J]. 建材发展导向, 2021, 19(20): 41-42. https://www.cnki.com.cn/Article/CJFDTOTAL-YNJC202120019.htm

    CHEN Hang. Building doors and windows following detection method and the analysis of common problems [J]. Development Guide to Building Materials, 2021, 19(20): 41-42. https://www.cnki.com.cn/Article/CJFDTOTAL-YNJC202120019.htm
    [6]
    史国权. 建筑门窗工程检测方法及结果分析[J]. 建材与装饰, 2018, 40: 48-49. https://www.cnki.com.cn/Article/CJFDTOTAL-JCYS201840033.htm

    SHI Guoquan. Testing methods and result analysis of building doors and windows engineering[J]. Construction Materials & Decoration, 2018, 40: 48-49. https://www.cnki.com.cn/Article/CJFDTOTAL-JCYS201840033.htm
    [7]
    Figuli L, Papan D, Z Papánová, et al. Experimental mechanical analysis of traditional in-service glass windows subjected to dynamic tests and hard body impact[J]. Smart Structures and Systems, 2021, 27(2): 365-378.
    [8]
    Mohan A, Poobal S. Crack detection using image processing: a critical review and analysis[J]. Alexandria Engineering Journal, 2017, 57(2): 787-798.
    [9]
    Gehri N, J Mata-Falcón, Kaufmann W. Automated crack detection and measurement based on digital image correlation[J]. Construction and Building Materials, 2020, 256: 119383. DOI: 10.1016/j.conbuildmat.2020.119383
    [10]
    Freitas S S D, Freitas V P D, Barreira E. Detection of fade plaster detachments using infrared thermography – a nondestructive technique[J]. Construction & Building Materials, 2014, 70: 80-87.
    [11]
    Ibarra-Castanedo C, Sfarra S, Klein M, et al. Solar loading thermography: time-lapsed thermographic survey and advanced thermographic signal processing for the inspection of civil engineering and cultural heritage structures[J]. Infrared Physics & Technology, 2017, 82: 56-74.
    [12]
    Thusyanthan I, Blower T, Cleverly W. Innovative uses of thermal imaging in civil engineering[C]//Proceedings of the Institution of Civil Engineers, 2017, 170(CE2): 81-87.
    [13]
    Ostańska Anna. Thermal imaging for detection of defects in envelopes of buildings in use: qualitative and quantitative analysis of building energy performance[J]. Periodica Polytechnica Civil Engineering, 2018, 62(4): 939-946.
    [14]
    Pan N H, Tsai C H, Chen K Y, et al. Enhancement of external wall decoration material for the building in safety inspection method[J]. Journal of Civil Engineering and Management, 2020, 26: 216-226.
    [15]
    Tiberio A J, Branchi P. A study of air leakage in residential buildings[C]//2013 International Conference on New Concepts in Smart Cities: Fostering Public and Private Alliances (SmartMILE) of IEEE, 2013: 1-4.
    [16]
    LI X, ZHOU W, LIN D. Research on air infiltration predictive models for residential building at different pressure[J]. Building Simulation, 2020: 1-12.
    [17]
    魏林滨, 李震, 李迪, 等. 被动式房屋气密性测试方法分析与实践应用[J]. 建设科技, 2015, 302(23): 24-28. https://www.cnki.com.cn/Article/CJFDTOTAL-KJJS201523017.htm

    WEI Linbin, LI Zhen, LI Di, et al. Passive building air tightness test method analysis and practice application[J]. Construction Science and Technology, 2015, 302(23): 24-28. https://www.cnki.com.cn/Article/CJFDTOTAL-KJJS201523017.htm
    [18]
    Ziou D, Tabbone S. Edge detection techniques-an overview[J]. Pattern Recognition and Image Analysis C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii, 1998, 8: 537-559.
    [19]
    任浩, 谢磊, 陈惠芳. 基于动态边缘检测的图像锐化算法[J]. 杭州电子科技大学学报, 2012, 32(4): 21-24. https://www.cnki.com.cn/Article/CJFDTOTAL-HXDY201204007.htm

    REN Hao, XIE Lei, CHEN Huifang. Image sharpening algorithm based on dynamic edge detection[J]. Journal of Hangzhou Dianzi University, 2012, 32(4): 21-24. https://www.cnki.com.cn/Article/CJFDTOTAL-HXDY201204007.htm
    [20]
    JIANG M. Edge enhancement and noise suppression for infrared image based on feature analysis[J]. Infrared Physics & Technology, 2018, 91: 142-152.
    [21]
    Kim C, Choi J S, Jang H, et al. Automatic detection of linear thermal bridges from infrared thermal images using neural network[J]. Applied Sciences, 2021, 11(3): 931.
    [22]
    张玲玲, 许廒, 张继冉, 等. 基于红外图像处理技术的建筑外窗缺陷面积计算研究[J]. 红外技术, 2022, 44(12): 1358-1366. http://hwjs.nvir.cn/article/id/f13166bf-7f5c-4baa-b984-061eea11215c

    ZHANG Lingling, XU Ao, ZHANG Jiran, et al. Research on calculation of defect area of building exterior windows based on infrared image processing technology[J]. Infrared Technology, 2022, 44(12): 1358-1366. http://hwjs.nvir.cn/article/id/f13166bf-7f5c-4baa-b984-061eea11215c
    [23]
    潘振, 刘伟, 陈刚, 等. 建筑外窗自然状态渗透能耗测试设备研制[J]. 新型建筑材料, 2019, 46(12): 141-144. https://www.cnki.com.cn/Article/CJFDTOTAL-XXJZ201912036.htm

    PAN ZHEN, LIU Wei, CHEN Gang, et al. Development of testing equipment for permeation energy consumption building outer window in natural state[J]. New Building Materials, 2019, 46(12): 141-144. https://www.cnki.com.cn/Article/CJFDTOTAL-XXJZ201912036.htm
    [24]
    季永明, 端木琳. 某近零能耗建筑空气渗透量数值分析[J]. 建筑科学, 2019, 35(10): 29-35. https://www.cnki.com.cn/Article/CJFDTOTAL-JZKX201910005.htm

    JI Yongming, DUAN Mulin. Air infiltration rate simulation of typical zero energy building[J]. Building Science, 2019, 35(10): 29-35. https://www.cnki.com.cn/Article/CJFDTOTAL-JZKX201910005.htm
    [25]
    Mijwel M M, Esen A, Shamil A. Overview of neural networks[J]. Computer Engineering Techniques Department, 2019(1): 1-2.
    [26]
    张龙, 董峰, 傅雨田. 基于神经网络的红外图像非均匀性校正[J]. 红外技术, 2018, 40(2): 164-169. http://hwjs.nvir.cn/article/id/hwjs201802011

    ZHANG Long, DONG Feng, FU Yutian. Non-uniformity correction for infrared image using neural [J]. Infrared Technology, 2018, 40(2): 164-169. http://hwjs.nvir.cn/article/id/hwjs201802011
    [27]
    王晓辉, 刘静蕾, 边会娟, 等. 基于改进BP神经网络的室内环境热舒适度预测与分析[J]. 控制工程, 2021, 28(7): 1437-1445. https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF202107023.htm

    WANG Xiaohui, LIU Jinglei, BIAN Huijuan, et al. Prediction and analysis of indoor environment thermal comfort based on improved BP neural network[J]. Control Engineering of China, 2021, 28(7): 1437-1445. https://www.cnki.com.cn/Article/CJFDTOTAL-JZDF202107023.htm
    [28]
    ZHANG X Y, YIN F, ZHANG Y M, et al. Drawing and recognizing Chinese characters with recurrent neural network[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 849-862.
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