한국구조물진단유지관리공학회 논문집 (Journal of the Korea institute for structural maintenance and inspection)

  • 제27권1호
  • /
  • Pages.30-36
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  • 2023
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  • 2234-6937(pISSN)
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  • 2287-6979(eISSN)
한국구조물진단유지관리공학회 (Korea Institute for Structural Maintenance Inspection)
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영상 분할기법을 활용한 콘크리트의 공극률 평가

Estimation of Concrete Porosity Using Image Segmentation Method

  • 정현준 (서울시립대학교, 건축공학과 스마트시티융합전공) ;
  • 정호성 (서울시립대학교, 건축공학과 스마트시티융합전공) ;
  • 김재현 (서울시립대학교, 건축공학과) ;
  • 김강수 (서울시립대학교, 건축공학과 스마트시티융합전공)
  • 투고 : 2022.11.03
  • 심사 : 2023.01.09
  • 발행 : 2023.02.28
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초록

이 연구에서는 콘크리트 표면 이미지를 활용하여 표면공극률을 평가할 수 있는 영상 분할모델을 도출하였다. 물-시멘트비가 다른 3종류의 콘크리트 실험체 (w/c = 54, 35, 및 30%) 가 제작되었으며, 광학현미경을 활용하여 2,729장의 표면 이미지를 취득하였다. 공극이 마스킹 된 표면 이미지 를 활용하여 벤치마킹 테스트, 매개변수 최적화, 최종모델 도출이 실시되었으며, 97%의 검증정확도를 나타내는 영상 분할 모델을 도출할 수 있었다. 영상 분할모델 및 X-Ray Microscope (XRM)을 통해 얻은 공극률을 비교하여 모델을 검증하였으며, 물시멘트비가 높은 시편에 대해선 모델과 XRM이 평가한 공극률이 유사하였고, 물시멘트비가 낮은 시편에 대해서는 모델이 XRM보다 공극률을 낮게 평가하는 경향을 나타내었다.

In this study, an image segmentation model that can evaluate surface porosity based on concrete surface images was derived. Three types of concrete specimens with different water-cement ratios (w/c = 54, 35, and 30%) were prepared, and 2,729 surface images were obtained using an optical microscope. Benchmarking tests, parameter optimization, and final model derivation were performed using the surface images, and an image segmentation model with 97% verification accuracy was obtained. The model was verified by comparing the porosity obtained from the model and X-Ray Microscope (XRM). The model provided similar porosity to that of XRM for the specimens with a high water-cement ratio, but tended to give lower porosity for specimens with a low water-cement ratio.

키워드

과제정보

이 논문은 2022년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2019R1A2C2086388).

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