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

LU Peng; ZHAO Tiansong; WANG Jian; ZHAO Lei; CHANG Haosong; ZHENG Yun

doi:10.13204/j.gyjzG22071401

Volume 52 Issue 10

Oct. 2022

Turn off MathJax

Article Contents

Article Navigation > INDUSTRIAL CONSTRUCTION > 2022 > 52(10): 22-27

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

Citation:

PDF( 2444 KB)

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

doi: 10.13204/j.gyjzG22071401

1. Inspection and Certification Co., Ltd., MCC, Beijing 100088, China;
2. Tianjin Chengjian University, Tianjin 300384, China;
3. 96854 Troops of PLA, Shenyang 110033, China;
4. Central Research Institute of Building and Construction Co., Ltd., MCC Group, Beijing 100088, China

Received Date: 2022-07-14
Available Online: 2023-03-22

Abstract

Abstract

Steel structures suffer from surface damage all the time due to their material properties and insufficient daily management. With the development of digital information technology, computer vision techniques have become key means in damage identification and health monitoring of steel structures. This paper presented the advances of computer vision techniques in damage and health monitoring of steel structures and discussed the identification techniques for surface corrosion damage, weld joint damage, and bolt connection damage of steel structures and their health monitoring. Moreover, it also predicted the development trends of damage identification and health monitoring techniques based on computer vision for steel structures.
- steel structure damage,
- damage identification,
- computer vision,
- health monitoring,
- carrying platform

FullText(HTML)

References(36)

References

[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