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Journal of the Korea Concrete Institute

J Korea Inst. Struct. Maint. Insp.
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  • Korea Citation Index (KCI)

References

1 
Jeong, Y., Kim, W., Lee, I., and Lee, J. (2018), Bridge inspection practices and bridge management programs in China, Japan, Korea, and US, Journal of Structural Integrity and Maintenance, 3(2), 126-135.DOI
2 
Kim, H., and Kim, C. (2020), Deep-learning-based classification of point clouds for bridge inspection, Remote Sensing, 12(22), 3757.DOI
3 
Adhikari, R. S., Moselhi, O., and Bagchi, A. (2014), Image-based retrieval of concrete crack properties for bridge inspection, Automation in construction, 39, 180-194.DOI
4 
Ye, X. W., Jin, T., Yun, C. B. (2019), A review on deep learning-based structural health monitoring of civil infrastructures, Smart Structures and Systems, 24(5), 567-585.URL
5 
Abdel-Qader, I., Abudayyeh, O., and Kelly, M. E. (2003), Analysis of edge-detection techniques for crack identification in bridges, Journal of Computing in Civil Engineering, 17(4), 255-263.DOI
6 
Long, J., Shelhamer, E., and Darrell, T. (2015), Fully convolutional networks for semantic segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA, 3431-3440.URL
7 
Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., and Shoaib, M. A. (2022), Structural crack detection using deep convolutional neural networks, Automation in Construction, 133, 103989.DOI
8 
Li, G., Wan, J., He, S., Liu, Q., and Ma, B. (2020), Semi-supervised semantic segmentation using adversarial learning for pavement crack detection, IEEE Access, 8, 51446-51459.DOI
9 
Shim, S., Kim, J., Lee, S. W., and Cho, G. C. (2022), Road damage detection using super-resolution and semi-supervised learning with generative adversarial network, Automation in Construction, 135, 104139.DOI
10 
Shim, S., Kim, J., Cho, G. C., and Lee, S. W. (2023), Stereo- vision-based 3D concrete crack detection using adversarial learning with balanced ensemble discriminator networks, Structural Health Monitoring, 22(2), 1353-1375.DOI
11 
Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T., and Omata, H. (2021), Generative adversarial network for road damage detection, Computer-Aided Civil and Infrastructure Engineering, 36(1), 47-60.DOI
12 
Zhang, K., Zhang, Y., and Cheng, H. D. (2020), Self-supervised structure learning for crack detection based on cycle-consistent generative adversarial networks, Journal of Computing in Civil Engineering, 34(3), 04020004.DOI
13 
Shim, S., Kim, J., Cho, G. C., and Lee, S. W. (2020), Multiscale and adversarial learning-based semi-supervised semantic segmentation approach for crack detection in concrete structures, IEEE Access, 8, 170939-170950.DOI
14 
Tarvainen, A., and Valpola, H. (2017), Mean teachers are better role models: Weight-averaged consistency targets improve semi- supervised deep learning results. Advances in neural information processing systems, 30.URL
15 
Zheng, M., You, S., Huang, L., Wang, F., Qian, C., and Xu, C. (2022), Simmatch: Semi-supervised learning with similarity matching, Proceedings of the IEEE conference on computer vision and pattern recognition, New Orleans, LA, USA, 14471-14481.URL
16 
Romera, E., Alvarez, J. M., Bergasa, L. M., and Arroyo, R. (2017), Erfnet: Efficient residual factorized convnet for real-time semantic segmentation, IEEE Transactions on Intelligent Transportation Systems, 19(1), 263-272.DOI
17 
Bang, S., Park, S., Kim, H., and Kim, H. (2019), Encoder-decoder network for pixel-level road crack detection in black-box images. Computer-Aided Civil and Infrastructure Engineering, 34(8), 713-727.DOI