Crack Monitoring of RC Columns Under Cyclic Loading Based on Computer Vision

SONG Chenghao; CHEN Shucheng; HU Xiaobin; YUAN Huanxin

doi:10.13204/j.gyjzG22070112

Volume 52 Issue 10

Oct. 2022

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SONG Chenghao, CHEN Shucheng, HU Xiaobin, YUAN Huanxin. Crack Monitoring of RC Columns Under Cyclic Loading Based on Computer Vision[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 53-60. doi: 10.13204/j.gyjzG22070112

Citation:

SONG Chenghao, CHEN Shucheng, HU Xiaobin, YUAN Huanxin. Crack Monitoring of RC Columns Under Cyclic Loading Based on Computer Vision[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 53-60. doi: 10.13204/j.gyjzG22070112

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Crack Monitoring of RC Columns Under Cyclic Loading Based on Computer Vision

doi: 10.13204/j.gyjzG22070112

School of Civil Engineering, Wuhan University, Wuhan 430072, China

Received Date: 2022-07-01
Available Online: 2023-03-22

Abstract

Abstract

Based on computer vision, the procedure of recognizing wide-range cracks on the surface of concrete members was presented by obtaining the wide-range crack images through image stitching, and identifying cracks by the threshold segmentation. Adopting the procedure, crack monitoring was conducted on a reinforced concrete (RC) column under cyclic loading to study crack development on the surface during loading and unloading processes. The results showed that wide-range concrete surfaces with cracks could be photographed in a sub-regional way with consumer-grade mobile phones. By further stitching the photos together, the cracks were finely recognized. The proposed procedure could be well used to recognize wide-range cracks on the surface of RC columns with relatively higher identification accuracy regarding crack width, length and inclined angles. In addition, with the increase of displacement amplifude, the width, length and areas of cracks on the surface of the RC column increased, the inclined angles decreased, and then the damage increased.
- computer vision,
- wide-range crack recognition,
- image stitching,
- cyclic loading,
- crack monitoring

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References(16)

References

[1]	AMMOUCHE A, BREYSSE D, HORNAIN H, et al. A new image analysis technique for the quantitative assessment of micro cracks in cement-based materials[J]. Cement and Concrete Research, 2000, 30(1):25-35.
[2]	YAMAGUCHI T, NAKAMURA S, SAEGUSA R, et al. Image-based crack detection for real concrete surfaces[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2008, 3(1):128-135.
[3]	刘鹏. 基于图像处理的混凝土预制构件裂缝检测系统研究[D].西安:西安建筑科技大学,2017.
[4]	沈俊凯.基于计算机视觉的混凝土裂缝检测算法研究[D].哈尔滨:中国地震局力学工程研究所,2019.
[5]	周颖,刘彤.基于计算机视觉的混凝土裂缝识别[J]. 同济大学学报(自然科学版), 2019(9):1277-1286.
[6]	SHAN B, ZHENG S, OU J. A stereovision-based crack width detection approach for concrete surface assessment[J].KSCE Journal of Civil Engineering, 2016, 20(2):803-812.
[7]	韩晓健,赵志成.基于计算机视觉技术的结构表面裂缝检测方法研究[J].建筑结构学报,2018(增刊1):418-427.
[8]	BROWN M, LOWE D G. Automatic Panoramic Image Stitching using Invariant Features[J]. International Journal of Computer Vision, 2007, 74(1):59-73.
[9]	HARRIS C, STEPHENS M. A combined corner and edge detector[C]//Proceedings of the 4th alvey vision conference, 1988:189-192.
[10]	FISCHLER M A, BOLLES R C. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6):381-395.
[11]	章毓晋. 计算机视觉教程[M]. 北京:人民邮电出版社, 2017.
[12]	YANOWITZ S D, BRUCKSTEIN A M. A new method for image segmentation[J]. Computer Vision Graphics and Image Processing, 1989, 46(1):82-95.
[13]	雷斯达,曹鸿猷,康俊涛.基于深度学习的复杂场景下混凝土表面裂缝识别研究[J].公路交通科技,2020(12):80-88.
[14]	HARALICK R M, SHAPIRO L G. Computer and robot vision[M]. Reading:Addison-Wesley, 1992.
[15]	贾云得. 机器视觉[M]. 北京:科学出版社, 2003.
[16]	The MathWorks, Inc.Image processing toolbox[M/OL]. https://www.mathworks.com/help/images. 2209000020801010陈振明.fbd