Method for concrete surface cracking detection based on rov and digital image technology

被引：0
|
作者：
Xie W.-G. [1 ]
Zhang Y.-X. [1 ]
Liu A.-R. [1 ]
Fu J.-Y. [1 ]
Hu X.-Y. [2 ]
Chen B.-C. [1 ]
Yuan X.-R. [1 ]
机构：
[1] Research Center of Wind Engineering and Engineering Vibration, Guangzhou
[2] Guangzhou Municipal Engineering Design and Research Institute Co. Ltd, Guangzhou
来源：
Gongcheng Lixue/Engineering Mechanics | 2022年 / 39卷
关键词：
Bridge detection; Crack detection; Crack width; Image processing; Underwater vehicle;
D O I：
10.6052/j.issn.1000-4750.2021.05.S010
中图分类号：
学科分类号：
摘要：
Manual detection is the main disease detection way for bridge underwater structures, which has the problems of high risk, low efficiency and low accuracy. In response to the above problems, this paper proposes an underwater robot bridge detection method with high-definition underwater camera. The surface detection of concrete structures was carried out in clean water experiment. The image with cracks was analyzed by image processing, Gaussian filtering and gradient calculation of the weight threshold method, and the crack edge features were extracted; The calculation method of crack width under Euclidean distance based on slope calculation was adopted to realize the analysis and calculation of crack width. The experimental results show that the accuracy of crack detection results in this paper is high, and the calculation accuracy of crack width parameters can meet the requirements of code. Copyright ©2022 Engineering Mechanics. All rights reserved.
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页码：64 / 70
页数：6
相关论文
共 20 条
  • [1] JTG H11−2004, Code for maintenance of highway bridges and culvers, (2004)
  • [2] Zhu Qiankun, Chen Jianbang, Zhang Qiong, Et al., A non-contact recognition for deflection influence line of footbridge based on computer vision, Engineering Mechanics, 38, 8, pp. 145-153, (2021)
  • [3] Yang Na, Zhang Chong, Li Tianhao, Design of crack monitoring system for Chinse ancient wooden buildings base on UAV and CV, Engineering Mechanics, 38, 3, pp. 27-39, (2021)
  • [4] Ellenberg A, Kontsos A, Moon F, Et al., Bridge related damage quantification using unmanned aerial vehicle imagery, Structural Control and Health Monitoring, 23, 9, pp. 1168-1179, (2016)
  • [5] Hallermann N, Morgenthal G., Unmanned aerial vehicles (UAV) for the assessment of existing structures, IABSE Symposium Report, pp. 1-8, (2013)
  • [6] Nishikawa T, Yoshida J, Sugiyama T, Et al., Concrete crack detection by multiple sequential image filtering, Computer-Aided Civil and Infrastructure Engineering, 27, 1, pp. 29-47, (2012)
  • [7] Wang Chao, Jia He, Zhang Sherong, Et al., Image based quantitative and efficient identification method for concrete surface cracks, Journal of Hydroelectric Engineering, 40, 3, pp. 134-144, (2021)
  • [8] Chen Bo, Zhang Hua, Wang Shuang, Et al., Image based quantitative and efficient identification method for concrete surface cracks, Journal of Hydroelectric Engineering, 39, 7, pp. 52-60, (2020)
  • [9] Zhang X, Li X., Study on damage working behaviors of main pier pile foundation of sea crossing bridge, Engineering and Computational Mechanics, 171, 3, pp. 1-30, (2018)
  • [10] Park J Y, Jun B H, Lee P M, Et al., Experiments on vision guided docking of an autonomous underwater vehicle using one camera, Ocean Engineering, 36, 1, pp. 48-61, (2009)
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