Acknowledgement
본 연구는 한국연구재단의 지원(NRF_2022R1A4A1033925)의 지원을 받아 수행한 연구 과제입니다.
References
- Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X. (2015) Tensorflow: Large-scale Machine Learning on Heterogeneous Distributed Systems, Software available from tensorflow.org.
- Bai, C., Shao, L., Da Silva, A.J., Zhao, Z. (2003) A Generalized Model for the Conversion from CT Numbers to Linear Attenuation Coefficients, IEEE Transactions on Nuclear Science, 50(5), pp.1510~1515.
- Bai, G., Zhu, C., Liu, C., Liu, B. (2020) An Evaluation of the Recycled Aggregate Characteristics and the Recycled Aggregate Concrete Mechanical Properties, Constr. Build. Mater., 240, p.117978.
- Bangaru, S.S., Wang, C., Zhou, X., Hassan, M. (2022) Scanning Electron Microscopy (SEM) Image Segmentation for Microstructure Analysis of Concrete using U-net Convolutional Neural Network. Autom. Constr., 144, p.104602.
- Cha, Y.J., Choi, W., Buyukozturk, O. (2017) Deep Learning- based Crack Damage Detection using Convolutional Neural Networks, Comput-Aided Civ Inf, 32(5), pp.361~378.
- Chen, Z., Ting, D., Newbury, R., Chen, C. (2021) Semantic Segmentation for Partially Occluded Apple Trees based on Deep Learning, Comput. & Electron. Agric., 181, p.105952.
- Chicco, D., Jurman, G. (2020) The Advantages of the Matthews Correlation Coefficient (MCC) over F1 Score and Accuracy in Binary Classification Evaluation, BMC Genom., 21, pp.1~13.
- Chung, S.Y., Kim, J.S., Kamm, P.H., Stephan, D., Han, T.S., Abd Elrahman, M. (2021) Pore and Solid Characterizations of Interfacial Transition Zone of Mortar using Microcomputed Tomography Images, J. Mater. Civ. Eng., 33(12), p.04021348.
- Goutte, C., Gaussier, E. (2005) A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for valuation, In: Advances in Information Retrieval: 27th European Conference on IR Research, ECIR 2005, pp.345~359.
- Han, T.S., Eum, D., Kim, S.Y., Kim, J.S., Lim, J.H., Park, K., Stephan, D. (2023) Multi-scale Analysis Framework for Predicting Tensile Strength of Cement Paste by Combining Experiments and Simulations, Cem. Concr. Compos., 139, p.105006.
- He, K., Zhang, X., Ren, S., Sun, J. (2016) Deep Residual Learning for Image Recognition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.770~778.
- Hu, X., Fang, H., Yang, J., Fan, L., Lin, W., Li, J. (2022) Online Measurement and Segmentation Algorithm of Coarse Aggregate based on Deep Learning and Experimental Comparison, Constr. Build. Mater., 327, 127033.
- Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q. (2017) Densely Connected Convolutional Networks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.4700~4708.
- Kim, J.S., Chung, S.Y., Han, T.S., Stephan, D., Abd Elrahman, M. (2020) Correlation between Microstructural Characteristics from Micro-CT of Foamed Concrete and Mechanical Behaviors Evaluated by Experiments and Simulations, Cem. Concr. Compos., 112, p.103657.
- Kim, J.S., Kim, J.H., Han, T.S. (2019a) Microstructure Characterization of Cement Paste from Micro-CT and Correlations with Mechanical Properties Evaluated from Virtual and Real Experiments, Mater. Charact., 155, p.109807.
- Kim, J.S., Suh, J., Pae, J., Moon, J., Han, T.S. (2022) Gradientbased Phase Segmentation Method for Characterization of Hydrating Cement Paste Microstructures Obtained from X-ray Micro-CT, J. Build. Eng., 46, p.103721.
- Kim, S.Y., Kim, J.S., Kang, J.W., Han, T.S. (2019b) Construction of Virtual Interfacial Transition Zone (ITZ) Samples of Hydrated Cement Paste using Extended Stochastic Optimization, Cem. Concr. Compos., 102, pp.84~93. https://doi.org/10.1016/j.cemconcomp.2019.04.012
- Liu, Y., Yeoh, J.K. (2021) Robust Pixel-Wise Concrete Crack Segmentation and Properties Retrieval using Image Patches, Autom. Constr, 123, p.103535.
- Long, J., Shelhamer, E., Darrell, T. (2015) Fully Convolutional Networks for Semantic Segmentation, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3431~3440.
- Marr, D., Hildreth, E. (1980) Theory of Edge Detection, Proc. Royal Soc. B, 207(1167), pp.187~217.
- Meyer, F. (2001) An Overview of Morphological Segmentation, Int. J. Pattern Recognit. Artif. Intell., 15(07), pp.1089~1118.
- Ronneberger, O., Fischer, P., Brox, T. (2015) U-net: Convolutional Networks for Biomedical Image Segmentation, In Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, pp.234~241.
- Sharma, M.K. (2014) A Survey of Thresholding Techniques over Images, J. Jaipur Nat. Univ., 3(2), pp.461~478. https://doi.org/10.5958/2277-4912.2014.00010.1
- Simonyan, K., Zisserman, A. (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv preprint arXiv:1409.1556.
- The MathWorks Inc. (2023) MATLAB version: 9.13.0 (R2023b), Natick, Massachusetts: The MathWorks Inc. https://www.mathworks.com.
- Ullah, M., Mir, J., Husain, S.S., Shahid, M.L.U.R., Ahmad, A. (2024) Concrete Forensic Analysis using Deep Learning-based Coarse Aggregate Segmentation, Autom. Constr., 162, p.105372.
- Werner, A.M., Lange, D.A. (1999) Quantitative image analysis of masonry mortar microstructure. J. Comput. Civ. Eng., 13(2), pp.110~115. https://doi.org/10.1061/(ASCE)0887-3801(1999)13:2(110)
- Xiao, J., Li, J., Zhang, C. (2005) Mechanical Properties of Recycled Aggregate Concrete under Uniaxial Loading, Cem. Concr. Res., 35(6), pp.1187~1194.
- Yang, R., Buenfeld, N.R. (2001) Binary Segmentation of Aggregate in SEM Image Analysis of Concrete, Cem. Concr. Res., 31(3), pp.437~441.
- Yasnoff, W.A., Mui, J.K., Bacus, J.W. (1977) Error Measures for Scene Segmentation, Pattern Recognit., 9(4), pp.217~231.