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Evaluation of the Bending Behavior of RC beam by Using Color-based Image Processing Method

색상에 기반한 영상분석기법을 이용한 콘크리트 거더의 휨 거동 분석

  • 우태련 (부산대학교 사회환경시스템공학과) ;
  • 정치영 ((주)제이원산업 기술연구소) ;
  • 김인태 (부산대학교 사회환경시스템공학과) ;
  • 이종한 (인하대학교 사회인프라공학과) ;
  • 정진환 (부산대학교 사회환경시스템공학과)
  • Received : 2020.06.26
  • Accepted : 2020.07.14
  • Published : 2020.08.30

Abstract

Cracks in reinforced concrete structures are the most common type of damage and are used as important analytical data to understand the fracture behavior characteristics of structures. Currently, there is a problem that most of the crack investigation relies on visual inspection, therefore many researchers have proposed image analysis techniques to improve the problem. In this study, we proposed a crack evaluation method to be applied at an indoor experimental level using image analysis method. The image analysis technique using color is for distinguishing a boundary surface between objects existing in an image, and is a method for separating similar colors into one region based on a predefined color. In this study, to improve the accuracy of image analysis, blue paint was applied to the concrete surface and bending experiments were performed. The image analysis method was able to measure the crack width with superior accuracy compared to the crack diameter, and at the same time, it was also possible to analyze the deflection of the beam. Both the crack and deformation were able to confirm the accuracy similar to the existing measurement method, and it was found that the image analysis method was very excellent in terms of applicability.

철근콘크리트 구조물에서 균열은 가장 대표적인 손상 유형으로써 구조물의 파괴거동특성 파악을 위한 중요한 분석자료로 활용되고 있다. 현재 균열조사는 대부분 육안조사에 의존하고 있으며, 이에 대한 개선을 위해서 많은 연구자들이 영상분석기법을 제안하고 있다. 본 연구에서는 영상분석기법을 활용하여 실내 실험수준에서 적용하기 위한 균열평가 방법을 제안하였다. 색상을 이용한 영상분석 기법은 영상내 존재하는 객체들 간의 경계면을 구분하기 위한 것으로 사전에 정의된 색상을 기준으로 비슷한 색상들을 하나의 영역으로 분리하기 위한 방법이다. 본 연구에서는 영상분석의 정확도를 향상시키기 위해서 콘크리트 표면에 파랑색 페인트를 도포하고 휨 실험을 수행하였다. 영상분석결과 균열확대경 대비 우수한 정확도의 균열폭 측정이 가능하였고, 동시에 보의 처짐 역시 분석이 가능하였다. 균열과 처짐 모두 기존 계측방법과 유사한 정확도를 확인할 수 있었으며, 계측 용이성 측면에서 영상분석기법이 매우 우수함을 알 수 있었다.

Keywords

References

  1. Lee, J. H., Jung, C. Y., Woo, T. R., and Cheung, J. H. (2019) Post-yielding Tension Stiffening of Reinforced Concrete Members Using an Image Analysis Method with a Consideration of Steel Ratios, Advances in Concrete Construction, 7(2), 117-126 https://doi.org/10.12989/ACC.2019.7.2.117
  2. Kim, J., Shin, Y. S., and Min, K. W. (2018), Line Laser Image Processing for Automated Crack Detection of Concrete Structures, Journal of Computational Structural Engineering Institute of Korea, 31(3), 147-153 (in Korean). https://doi.org/10.7734/COSEIK.2018.31.3.147
  3. J. H. Cheung, J. H. Lee, T. R. Woo, and C. Y. Jung (2017), Evaluation on Strain and Necking Region of the Rebar by Using Image Processing Method, Journal of the Korea Concrete Institute, KCI, 29(1), 33-42 https://doi.org/10.4334/JKCI.2017.29.1.033
  4. Kim, H. J., Ahn, E. J., Cho, S. J., Shin, M. S., and Sim, S. H. (2017), Comparative Analysis of Image Binarization Methods for Crack Identification in Concrete Structures, Cement and Concrete Research, 99, 53-61 https://doi.org/10.1016/j.cemconres.2017.04.018
  5. Cheung, J. H., Lee, J. H., Woo, T. R., and Jung, C. Y. (2017) Evaluation on Strain and Necking Region of the Rebar by Using Image Processing Method, Journal of the Korea Concrete Institute, KCI, 29(1), 33-42 (in Korean) https://doi.org/10.4334/JKCI.2017.29.1.033
  6. Kim, S. D., Jung, C. Y., Woo, T. R., and Cheung, J. H. (2016), Application of Image Processing Method to Evaluate Ultimate Strain of Rebar, Journal of the Korea Institute for Structural Maintenance and Inspection, KSMI, 20(3), 111-121 (in Korean) https://doi.org/10.11112/jksmi.2016.20.3.111
  7. Talab, A., Huang, Z., Xi, F., and Haiming, L. (2016), Detection Crack in Image Using Otsu Method and Multiple Filtering in Image Processing Techniques, Optik, 127(3), 1030-1033 https://doi.org/10.1016/j.ijleo.2015.09.147
  8. Kaur A. and Kranti B.V. Comparison between YCbCr Color Space and CIE Lab Color Space for Skin Color Segmentation. International Journal of Applied Information System, 2012; pp. 30-36
  9. Tsai, Y. C., Kaul V., and Mersereau, R. M. (2010), Critical assessment of pavement distress segmentation methods, Journal of Transportation, 136(1), 11-19
  10. Jung, C. H., Oh, A. S., and Kim, K. B. (2007), Extraction and Analysis of Concrete Surface Cracks, Proceedings of the Korea Multimedia Society Conference, 46-52 (in Korean)
  11. Sinha, S. K. and Fieguth, P. W. (2006), Segmentation of buried concrete pipe images, Automation in Construction, 15(1), 47-57 https://doi.org/10.1016/j.autcon.2005.02.007
  12. Her, J. Y., Kim, K. R., Lim, E. K., Ahn, S. H., and Kim, K. B. (2006), A Length and Width Extraction of Concrete Surface Cracks using Image Processing Techniques, Proceedings of the Korea Institute of Maritime Information & Communication Sciences, 346-351 (in Korean)