中国科技核心期刊
RCCSE中国核心学术期刊
JST China收录期刊
中国建筑科学领域高质量科技期刊分级目录

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于计算机视觉的钢结构表面损伤识别与健康监测综述

逯鹏 赵天淞 王剑 赵磊 常好诵 郑云

逯鹏, 赵天淞, 王剑, 赵磊, 常好诵, 郑云. 基于计算机视觉的钢结构表面损伤识别与健康监测综述[J]. 工业建筑, 2022, 52(10): 22-27. doi: 10.13204/j.gyjzG22071401
引用本文: 逯鹏, 赵天淞, 王剑, 赵磊, 常好诵, 郑云. 基于计算机视觉的钢结构表面损伤识别与健康监测综述[J]. 工业建筑, 2022, 52(10): 22-27. doi: 10.13204/j.gyjzG22071401
LU Peng, ZHAO Tiansong, WANG Jian, ZHAO Lei, CHANG Haosong, ZHENG Yun. Review on Damage Identification and Health Monitoring of Steel Structures Based on Computer Vision[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 22-27. doi: 10.13204/j.gyjzG22071401
Citation: LU Peng, ZHAO Tiansong, WANG Jian, ZHAO Lei, CHANG Haosong, ZHENG Yun. Review on Damage Identification and Health Monitoring of Steel Structures Based on Computer Vision[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 22-27. doi: 10.13204/j.gyjzG22071401

基于计算机视觉的钢结构表面损伤识别与健康监测综述

doi: 10.13204/j.gyjzG22071401
基金项目: 

国家自然科学基金面上项目(52078508)。

详细信息
    作者简介:

    逯鹏,男,1985年出生,博士,高级工程师。

    通讯作者:

    王剑,男,1981年出生,博士,副教授,avonlea@163.com。

Review on Damage Identification and Health Monitoring of Steel Structures Based on Computer Vision

  • 摘要: 由于钢结构材料特性及日常管理不足等原因,其表面损伤情况时有发生。随着数字信息技术的发展,计算机视觉技术已经成为钢结构损伤识别与健康监测的重要手段。本文介绍了计算机视觉技术在钢结构损伤与健康监测方面的相关研究进展,围绕钢结构表面锈蚀损伤、焊缝损伤、螺栓连接损伤的识别技术及钢结构健康监测技术展开讨论,并展望了基于计算机视觉的钢结构损伤识别与健康监测技术的发展方向。
  • [1] 仇智, 罗亚冉. 钢结构建筑的发展现状及前景分析[J]. 产业与科技论坛, 2011, 10(2):55-56.
    [2] 张兴斌, 杨昕光, 潘蓉,等. 土木工程智能化监测评估系统的理论研究及应用[J]. 工业建筑, 2021, 51(12):102-106.
    [3] WANG N, ZHAO Q, LI S, et al. Damage classification for masonry historic structures using convolutional neural networks based on still images[J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(12):1073-1089.
    [4] 周奎, 王琦, 刘卫东, 等. 土木工程结构健康监测的研究进展综述[J]. 工业建筑, 2009, 39(3):96-102.
    [5] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553):436-44.
    [6] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proc IEEE, 1998,86(11):2278-324.
    [7] WU S, ZHONG S, LIU Y. Deep residual learning for image steganalysis[J]. Multimedia Tools and Applications, 2017, 77(9):10437-10453.
    [8] ATHA D J, JAHANSHAHI M R. Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection[J]. Structural Health Monitoring, 2017, 17(5):1110-1128.
    [9] AHUJA S K, SHUKLA M K. A survey of computer vision based corrosion detection approaches, information and communication technology for intelligent systems (ICTIS 2017):Volume 2[M]. 2018:55-63.
    [10] CHOI K Y, KIM S S. Morphological analysis and classification of types of surface corrosion damage by digital image processing[J]. Corrosion Science, 2005, 47(1):1-15.
    [11] JAHANSHAHI M R, MASRI S F. Parametric performance evaluation of wavelet-based corrosion detection algorithms for condition assessment of civil infrastructure systems[J]. Journal of Computing in Civil Engineering, 2013, 27(4):345-357.
    [12] CHEN P H, SHEN H K, LEI C Y, et al. Fourier-transform-based method for automated steel bridge coating defect recognition[J]. Procedia Engineering, 2011, 14:470-476.
    [13] CHEN P H, SHEN H K, LEI C Y, et al. Support-vector-machine-based method for automated steel bridge rust assessment[J]. Automation in Construction, 2012, 23:9-19.
    [14] XU J, GUI C, HAN Q. Recognition of rust grade and rust ratio of steel structures based on ensembled convolutional neural network[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(10):1160-1174.
    [15] DONG C, LI L, YAN J, et al. Pixel-level fatigue crack segmentation in large-scale images of steel structures using an encoder-decoder network[J/OL]. Sensors (Basel), 2021, 21(12).https://doi.org/10.3390/s21124135.
    [16] QIAO W, MA B, LIU Q, et al. Computer vision-based bridge damage detection using deep convolutional networks with expectation maximum attention module[J/OL]. Sensors (Basel), 2021, 21(3).https://doi.org/10.3390/s21030824.
    [17] XU Y, BAO Y, CHEN J, et al. Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images[J]. Structural Health Monitoring, 2018, 18(3):653-674.
    [18] XU Y, LI S, ZHANG D, et al. Identification framework for cracks on a steel structure surface by a restricted Boltzmann machines algorithm based on consumer-grade camera images[J/OL]. Structural Control and Health Monitoring, 2018, 25(2).https://doi.org/10.1002/stc.2075.
    [19] DUNG C V, SEKIYA H, HIRANO S, et al. A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks[J]. Automation in Construction, 2019, 102:217-229.
    [20] RAMANA L, CHOI W, CHA Y J. Fully automated vision-based loosened bolt detection using the Viola-Jones algorithm[J].Structural Health Monitoring, 2018, 18(2):422-434.
    [21] RAMANA L, CHOI W, CHA Y J. Fully automated vision-based loosened bolt detection using the Viola-Jones algorithm[J]. Structural Health Monitoring, 2018, 18(2):422-434.
    [22] ZHOU J, HUO L, SONG G, et al. Deep learning-based visual inspection for the delayed brittle fracture of high-strength bolts in long-span steel bridges[C]//2019 International Conference on Image and Video Processing, and Artificial Intelligence, 2019.
    [23] ZHOU J, HUO L, WANG H. Computer vision-based detection for delayed fracture of bolts in steel bridges[J]. Journal of Sensors, 2021, 2021:1-12.
    [24] 雷素素, 刘宇飞, 段先军, 等. 复杂大跨空间钢结构施工过程综合监测技术研究[J]. 工程力学, 2018, 35(12):203-211.
    [25] TENG S, CHEN G, LIU G, et al. Modal strain energy-based structural damage detection using convolutional neural networks[J]. Applied Sciences, 2019, 9(16):366-371.
    [26] 刘宇飞, 辛克贵, 樊健生, 等. 环境激励下结构模态参数识别方法综述[J]. 工程力学, 2014, 31(4):46-53.
    [27] 卓德兵, 曹晖. 基于小波时频图与轻量级卷积神经网络的螺栓连接损伤识别[J]. 工程力学, 2021, 38(9):228-238.
    [28] PAL J, SIKDAR S, BANERJEE S. A deep-learning approach for health monitoring of a steel frame structure with bolted connections[J/OL]. Structural Control and Health Monitoring, 2021, 29(2).https://doi.org/10.1002/stc.2873.
    [29] PEREZ-PEREZ Y, GOLPARVAR-FARD M, EL-RAYES K. Semantic and geometric labeling for enhanced 3D point cloud segmentation[C]//Proceedings of the 2016 Construction Research Congress, U.S.:2016:2542-2552.
    [30] GUO M, SUN M, PAN D, et al. High-precision detection method for large and complex steel structures based on global registration algorithm and automatic point cloud generation[J/OL]. Measurement, 2021, 172.https://doi.org/10. 1016/j.measurement.2020.108765.
    [31] 魏思航, 刘宇飞, 刘家豪. 基于无人机与数字图像法的混凝土结构表面裂缝检测应用研究[J]. 特种结构, 2020, 37(5):107-111.
    [32] CHEN Q, WEN X, WU F, et al. Defect detection and health monitoring of steel structure based on UAV integrated with image processing system[J/OL]. Journal of Physics:Conference Series, 2019, 1176.https://doi.org/10.1088/1742-6596/1176/5/052074.
    [33] HAN Q, ZHAO N, XU J. Recognition and location of steel structure surface corrosion based on unmanned aerial vehicle images[J]. Journal of Civil Structural Health Monitoring, 2021, 11(5):1375-1392.
    [34] HAN Q, LIU X, XU J. Detection and location of steel structure surface cracks based on unmanned aerial vehicle images[J/OL]. Journal of Building Engineering, 2022, 50.https://doi.org/10.1016/j.jobe.2022.104098.
    [35] 金明辉.公路勘测设计[M]. 北京:人民交通出版社, 2014.
    [36] HUSSAIN M, CHEN D, CHENG A, et al. Change detection from remotely sensed images:From pixel-based to object-based approaches[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 80:91-106. 2209000020800606林拥军.fbd
  • 加载中
计量
  • 文章访问数:  195
  • HTML全文浏览量:  15
  • PDF下载量:  7
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-07-14
  • 网络出版日期:  2023-03-22

目录

    /

    返回文章
    返回