영상 분할기법을 활용한 콘크리트의 공극률 평가
Estimation of Concrete Porosity Using Image Segmentation Method
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초록
이 연구에서는 콘크리트 표면 이미지를 활용하여 표면공극률을 평가할 수 있는 영상 분할모델을 도출하였다. 물-시멘트비가 다른3종류의 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 분할모델을 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.
Abstract
In this study, an image segmentation model that can evaluate surface porosity based on concrete surface images was derived. Three typesof concrete specimens with different water-cement ratios (w/c = 54, 35, and 30%) were prepared, and 2,729 surface images were obtained using anoptical microscope. Benchmarking tests, parameter optimization, and final model derivation were performed using the surface images, and an imagesegmentation model with 97% verification accuracy was obtained. The model was verified by comparing the porosity obtained from the model andX-Ray Microscope (XRM). The model provided similar porosity to that of XRM for the specimens with a high water-cement ratio, but tended to givelower porosity for specimens with a low water-cement ratio.
참고문헌 (22)
- "Guidelines for safety and maintenance of infrastructures" (2022)[URL]
- Chang, C. F., "The experimental investigation of concrete carbonation depth", Cement and Concrete Research, 36(9), pp.1760~1767 (2006)
- "KS F 2385, Permeable asphalt mixtures" (2018)[URL]
- Lu, S., "X-ray microtomographic studies of pore structure and permeability in Portland cement concrete", Materials and Structures, 39(6), pp.611~620 (2006)
- Torres-Luque, M., "Non-destructive methods for measuring chloride ingress into concrete: State-of-the-art and future challenges", Construction and Building Materials, 68, pp.68~81 (2014)
- Hofmarcher, M., "Visual scene understanding for autonomous driving using semantic segmentation, Explainable AI:Interpreting", Explaining and Visualizing Deep Learning, pp.285~296 (2019)
- Wang, G., "Interactive medical image segmentation using deep learning with image-specific fine tuning", IEEE Transactions on Medical Imaging, 37(7), pp.1562~1573 (2018)
- Dogan, G., "Concrete compressive strength detection using image processing based new test method", Measurement, 109, pp.137~148 (2017)
- Jang, Y., "Estimating compressive strength of concrete using deep convolutional neural networks with digital microscope images", Journal of Computing in Civil Engineering, 33(3), pp.04019018~ (2019)
- Yang, H., "Multilabel Image Classification Based Fresh Concrete Mix Proportion Monitoring Using Improved Convolutional Neural Network", Sensors, 20(16), pp.4638~ (2020)
- Dung, C. V., "Autonomous concrete crack detection using deep fully convolutional neural network", Automation in Construction, 99, pp.52~58 (2019)
- Otsu, N., "A threshold selection method from gray-level histograms", IEEE Transactions on Systems, Man, and Cybernetics, 9(1), pp.62~66 (1979)
- Dhanachandra, N., "Image segmentation using K-means clustering algorithm and subtractive clustering algorithm", Procedia Computer Science, 54, pp.764~771 (2015)
- Long, J., "Fully convolutional networks for semantic segmentation", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.3431~3440 (2015)
- Ronneberger, O., "U-net:Convolutional networks for biomedical image segmentation", International Conference on Medical image computing and computer-assisted intervention, pp.234~241 (2015), Springer
- Chen, L. C., "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs", IEEE transactions on pattern analysis and machine intelligence, 40(4), pp.834~848 (2017)
- Howard, A. G., "Mobilenets: Efficient convolutional neural networks for mobile vision applications", ~ (2017)
- 안재훈, "X-Ray 및 수중중량을 이용한 투수성 콘크리트의 간극률 산정", 한국방재학회논문집, 13(4), pp.77~81 (2013), 한국방재학회
- Kumar, R., "Porosity, pore size distribution and in situ strength of concrete", Cement and Concrete Research, 33(1), pp.155~164 (2003)
- Nitto, "Concrete tester and surveyor(User manual)", Nitto, ~ (2009)
- "KS F 2405, Test method for compressive strength of concrete" (2022)[URL]
- Liashchynskyi, P., "Grid search, random search, genetic algorithm: A big comparison for NAS", ~ (2019)