JKSMI
Journal of the Korea Institute for
Structural Maintenance and Inspection
KSMI
Contact
Open Access
Bi-monthly
ISSN : 2234-6937 (Print)
ISSN : 2287-6979 (Online)
http://journal.auric.kr/jksmi/
Mobile QR Code
Journal of the Korea Concrete Institute
J Korea Inst. Struct. Maint. Insp.
Indexed by
Korea Citation Index (KCI)
Main Menu
Main Menu
About Journal
Aims and Scope
Subscription Inquiry
Editorial Board
For Contributors
Instructions For Authors
Ethical Guideline
Crossmark Policy
Submission & Review
Archives
Current Issue
All Issues
Journal Search
Home
All Issues
2023-10
(Vol.27 No.5)
10.11112/jksmi.2023.27.5.113
Journal XML
XML
PDF
INFO
REF
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.
2
Kim, H., and Kim, C. (2020), Deep-learning-based classification of point clouds for bridge inspection, Remote Sensing, 12(22), 3757.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.