Mobile QR Code QR CODE

Journal of the Korea Concrete Institute

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

Journal Search

  1. Home
  2. All Issues
  3. 2025-12 (Vol.29 No.6)
  4. 10.11112/jksmi.2025.29.6.110
XML PDF INFO REF

References

1 
Ali, L., Alnajjar, F., Jassmi, H.A., Gocho, M., Khan, W., Serhani, M.A. (2021), Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures, Sensors, 21(5), 1688DOI
2 
Altaf, A., Mehmood, A., Filograno, M.L., Alharbi, S., Iqbal, J. (2025), Deployable deep learning models for crack detection: efficieny, interpretability, and severity estimation, Buildings, 15(18), 3362DOI
3 
Ding, W., A-Basset, M., Hawash, H., M.Ali, A. (2022), Explainability of artificial interlligence methods, applications and challenges: A comprehensive survey, Information Sciences, 615, 238-292.DOI
4 
Dou, Y.T., Dong, G.Q., Li, X. (2024), Automatic identification of GPR targets on roads based on CNN and Grad-CAM, Applied Geophysics, 22, 488-498.DOI
5 
Forest, F., Porta, H., Tuia, D., Fink, O. (2024), From classification to segmentation with explainable AI: A study on crack detection and growth monitoring, Automation in Construction, 165, 105497DOI
6 
Gipiskis, R., Tsai, C.W., Kurasova, O. (2024), Explainable AI (XAI) in image segmentation in medicine, industry, and beyond: A survey, ICT Express, 10, 1331-1354.DOI
7 
Huangfu, Z., Jiao, Y., Wei, F., Shi, G., Dong, H. (2025), A unified approach for weakly supervised crack detection via affine transformation and pseudo label refinement, Scientific Reports, 15, 8673DOI
8 
Jeong, H.P., Song, H.M., Choi, Y.C. (2024), Real-time road surface recognition and black ice prevention system for asphalt concrete pavements using image analysis, Journal of the Korea Institute for Structural Maintenance and Inspection, 28(1), 82-89.Google Search
9 
Kang, W., Li, D.S., Zhang, Y. (2025), Interpretable research on the health monitoring network of prefabricated building beam-column joints, Structural Health MonitoringDOI
10 
Kavitha, S., Baskaran, K.R., Dhanapriya, B. (2023), Explainable AI for detecting fissures on concrete surfaces using transfer learning, 376-384.DOI
11 
Kim, A.R., Kim, D.H., Byun, Y.S., Lee, S.W. (2018), Crack detection of concrete structure using deep learning and image processing method in geotechnical engineering, Journal of the Korean Geotechnical Society, 34(12), 145-154.DOI
12 
Kim, B.H., Cho, S.J., Chae, H.J., Kim, H.K., Kang, J.H. (2021), Development of crack detection system for highway tunnels using imaging device and deep learning, Journal of the Korea Institute for Structural Maintenance and Inspection, 25(4), 65-74.DOI
13 
Lee, Y.I., Kim, B.H., Cho, S.J. (2018), Image-based spalling detection of concrete structures using deep learning, Journal of the Korea Concrete Institute, 30(1), 91-99.DOI
14 
Liu, C., Xu, B. (2023), Weakly-supervised structural surface crack detection algorithm based on class activation map and superpixel segmentation, Advances in Bridge Engineering, 4, 27DOI
15 
Lundberg, S.M., Lee, S.I. (2017), A unified approach interpreting model predictions, 1-10.Google Search
16 
Ogunjinmi, P.D., Park, S.S., Kim, B.R., Lee, D.E. (2022), Rapid post-earthquake structural damage assessment using convolutional neural networks and transfer learning, Sensors, 22(9), 3471DOI
17 
Philip, R.E., Andrushia, A.D., Nammalvar, A., Gurupatham, B.G.A., Roy, K. (2023), A comparative study on crack detection in concrete walls using transfer learning techniques, Journal of Composites Sciences, 7(4), 169DOI
18 
Ribeiro, M.T., Singh, S., Guestrin, C. (2016), “Why Should I Trust You?”: Expalining the predictions of any classifier, 1135-1144.DOI
19 
Saarela, M., Podgorelec, V. (2024), Recent applications of explainable AI (XAI): A systematic literature review, Applied Sciences, 14(19), 8884DOI
20 
Sam Rajadurai, R., Kang, S.T. (2021), Automated vision-based crack detection on concrete surfaces using deep learning, Applied sciences, 11(11), 5229DOI
21 
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D. (2019), Grad-CAM: Visual explanations from deep networks via gradient-based localization, International Journal of Computer Vision, 128, 336-359.Google Search
22 
Shomal Zadeh, S., Aalipour birgani, S., Khorshidi, M., Kooban, F. (2023), Concrete surface crack detection with convolutional-based deep learning models, International Journal of Novel Research in Civil Structural and Earth Sciences, 10(3), 25-35.DOI
23 
Sohaib, M., Hasan, M.J., Hasan, M.A., Zheng, Z. (2024), A robust self-supervised approach for fine-grained crack detection in concrete structures, Scientific Reports, 14, 12646DOI
24 
Swarna, R.A., Hossain, M.M., Khatun, Mst.R., Rahman, M.M., Munir, A. (2024), Concrete crack detection and segregation: A feature fusion, crack isolation, and explainable AI-Based approach, Journal of Imaging, 10(9), 215DOI
25 
Wu, J., He, Y., Xu, C., Jia, X., Huang, Y., Chen, Q., Huang, C., Eslamlou, A.D., Huang, S. (2023), Interpretability analysis of convolutional neural networks for crack detection, Buildings, 13(12), 3095DOI
26 
Yuan, Q., Shi, Y., Li, M. (2024), A review of computer vision-based crack detection methods in civil infrastructure: progress and challenges, Remote Sensing, 16(16), 2910DOI
27 
Zoubir, H., Rguig, M., Aroussi, M.E., Chehri, A., Saadane, R., Jeon, G. (2022), Concrete bridge defects identification and localization based on classification deep convolutional neural networks and transfer learning, Remote Sensing, 14(19), 4882DOI