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
2026-08
(Vol.30 No.4)
10.11112/jksmi.2026.30.4.17
Journal XML
XML
PDF
INFO
REF
References
1
Cha, Y.J., Choi, W.R., Büyüköztürk, O. (2017), Deep learning-based crack damage detection using convolutional neural networks, Computer-Aided Civil and Infrastructure Engineering, 32(5), 361-378.
2
He, K., Zhang, X., Ren, S., Sun, J. (2016), Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.
3
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., Meger, D. (2018), Deep reinforcement learning that matters, Proceedings of the AAAI Conference on Artificial Intelligence, 32(1), 3207-3214.
4
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H. (2017), MobileNets: Efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861
5
Kim, I.S., Yang, E.I. (2026), Effect of data size and hyperparameter settings on classification performance and computational efficiency in CNN-Based concrete damage classification, Journal of the Korea Institute for Structural Maintenance and Inspection, 30(2), 1-9. (in Korean)
6
Kim, I.S., Choi, S.Y., Yang, E.I. (2025), Analysis of performance and interpretability in CNN-based concrete damage classification using Grad-CAM, Journal of the Korea Institute for Structural Maintenance and Inspection, 29(6), 110-118. (in Korean)
7
Kim, I.S., Choi, S.Y., Yang, E.I. (2025), Concrete damage classification using CNN models with small-scale images: performance analysis and comparison, Journal of the Korea Institute for Structural Maintenance and Inspection, 29(6), 30-38. (in Korean)
8
Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012), ImageNet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems (NeurIPS), 25, 1097-1105.
9
Recht, B., Roelofs, R., Schmidt, L., Shankar, V. (2019), Do ImageNet classifiers generalize to ImageNet?, Proceedings of the 36th International Conference on Machine Learning (ICML), 5389-5400.
10
Roy, S., Yogi, B., Majumdar, R., Ghosh, P., Das, S.K. (2025), Deep learning‑based crack detection and prediction for structural health monitoring, Discover Applied Sciences, 7, 674
11
Shin, H.K. (2025), A comparative study on the performance and inference speed of deep learning models for structure crack detection, Journal of the Korea Institute for Structural Maintenance and Inspection, 29(6), 199-206. (in Korean)
12
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A. (2015), Going deeper with convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-9.
13
Tan, M., Le, Q. (2019), EfficientNet: Rethinking model scaling for convolutional neural networks, Proceedings of the 36th International Conference on Machine Learning (ICML), 6105-6114.
14
Zhang, L., Yang, F., Zhang, Y. D., Zhu, Y. J. (2016), Road crack detection using deep convolutional neural network, IEEE International Conference on Image Processing (ICIP), 3708-3712.