JKSCE
KSCE JOURNAL OF CIVIL AND
ENVIRONMENTAL ENGINEERING RESEARCH
KSCE
Contact
ISSN : 1015-6348 (Print)
ISSN : 2799-9629 (Online)
Mobile QR Code
Journal of the Korean Society of Civil Engineers
ISO Journal Title
KSCE J. Civ. Environ. Eng. Res.
Open Access, Bi-monthly
Main Menu
Main Menu
최근호
Current Issue
논문집
Journal Archive
저널소개
About Journal
편집위원회
Editorial Board
논문투고안내
For Authors and Reviewers
윤리규정
Publication Ethics
Principles of Transparency and Best Practice
Business Model
연락처
Contact Info
논문투고
E-submission
Journal Search
Home
All Issues
2026-08
(v.46 n.4)
10.12652/Ksce.2026.46.4.0363
Journal XML
XML
PDF
INFO
REF
References
1
Bai, Y., Sezen, H., Yilmaz, A. (2021). Detecting cracks and spalling automatically in extreme event reconnaissance, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2-2021, 161-168.
2
Caruana, R. (1997). Multitask learning, Machine Learning, 28(1), 41-75.
3
Cha, Y. J., Choi, W., Büyüköztürk, O. (2017). Deep learning-based crack damage detection using convolutional neural networks, Computer-Aided Civil and Infrastructure Engineering, 32(5), 361-378.
4
Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation, , 801-818.
5
Crawshaw, M. (2020). Multi-task learning with deep neural networks: A survey, arXiv preprint arXiv:2009.09796.
6
AI-Hub (2024). National Information Society Agency (NIA), Republic of Korea.
7
Ding, C., Lu, Z., Wang, S., Cheng, R., Boddeti, V. N. (2023). Mitigating task interference in multi-task learning via explicit task routing with non-learnable primitives, , 7756-7765.
8
Dorafshan, S., Thomas, R. J., Maguire, M. (2018). Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete, Construction and Building Materials, 186, 1031-1045.
9
Hinton, G., Vinyals, O., Dean, J. (2015). Distilling the knowledge in a neural network, arXiv preprint arXiv:1503.02531.
10
Kendall, A., Gal, Y., Cipolla, R. (2018). Multi-task learning using uncertainty to weigh losses for scene geometry and semantics, , 7482-7491.
11
Kim, B., Cho, S. (2020). Automated multiple concrete damage detection using instance segmentation deep learning model, Applied Sciences, 10(22), 8008.
12
He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition, , 770-778.
13
Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., Bengio, Y. (2015). FitNets: Hints for thin deep nets, .
14
Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation, , 234-241.
15
Vandenhende, S., Georgoulis, S., Van Gool, L. (2020). Multi-scale task interaction networks for multi-task learning, .
16
Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., Yang, X. (2018). Automatic pixel-level crack detection and measurement using fully convolutional network, Computer-Aided Civil and Infrastructure Engineering, 33(12), 1090-1109.
17
Zhang, L., Yang, F., Zhang, Y. D., Zhu, Y. J. (2016). Road crack detection using deep convolutional neural network, , 3708-3712.
18
Zou, Q., Zhang, Z., Li, Q., Qi, X., Wang, Q., Wang, S. (2019). DeepCrack: Learning hierarchical convolutional features for crack detection, IEEE Transactions on Image Processing, 28(3), 1498-1512.