Open Access
Issue |
MATEC Web Conf.
Volume 400, 2024
5th International Conference on Sustainable Practices and Innovations in Civil Engineering (SPICE 2024)
|
|
---|---|---|
Article Number | 03005 | |
Number of page(s) | 8 | |
Section | Structural and Transportation Engineering | |
DOI | https://doi.org/10.1051/matecconf/202440003005 | |
Published online | 03 July 2024 |
- B. Butcher, C.R. Day, J.C. Austin, P.W. Haycock, D. Verstraeten and B. Schrauwen, “Defect Detection in Reinforced Concrete Using Random Neural Architectures”, Computer-Aided Civil and Infrastructure Engineering, vol. 29, no. 3, pp. 191–207, 2014. [CrossRef] [Google Scholar]
- Hongxia Li, Weixing Wang, Mengfai Wang and Limin Li, “A review of deep learning methods for pixel-level crack detection”, Journal of Traffic and Transportation Engineering, vol. 9, no. 6, pp. 945–968, 2022. [Google Scholar]
- Mohammad R. Jahanshahi, Sami F. Masri, Curtis W. Padgett and Gaurav S. Sukhatme, An innovative methodology for detection and quantification of cracks through incorporation of depth perception., vol. 24, no. 2, pp. 227–241, 2013. [Google Scholar]
- Krizhevsky Alex, Sutskever Ilya and Geoffrey E. Hinton, “ImageNet classification with deep convolutional neural networks”, Communications of the ACM, vol. 60, no. 6, pp. 84–90, 2017. [CrossRef] [Google Scholar]
- H. Moon and J. Kim, “Intelligent crack detecting algorithm on the concrete crack image using neural network”, Proceedings of the 28th ISARC Seoul Korea, pp. 1461–67, 2011. [Google Scholar]
- T. Nishikawa, J. Yoshida, T. Sugiyama and Y. Fujino, “Concrete crack detection by multiple sequential image filtering”, Computer-Aided Civil and Infrastructure Engineering, vol. 27, no. 1, pp. 29–47, 2012. [CrossRef] [Google Scholar]
- Cha Young-Jin, Choi Wooram and Buyukozturk Oral, “Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks”, Computer-Aided Civil and Infrastructure Engineering, vol. 32, pp. 361–378, 2017. [CrossRef] [Google Scholar]
- S. Teidj, A. Khamlichi and A. Driouach, “Identification of beam cracks by solution of an inverse problem”, Procedia Technology, vol. 22, 2016. [Google Scholar]
- E. N. Chatzi, B. Hiriyur, H. Waisman and A. W. Smyth, “Experimental application and enhancement of the XFEM-GA algorithm for the detection of flaws in structures”, Computers & Structures, vol. 89, no. 7, pp. 556–70, 2011. [CrossRef] [Google Scholar]
- M. Islam and J.-M. Kim, “Vision-based autonomous crack detection of concrete structures using a fully convolutional encoder-decoder network”, Sensors, vol. 19, no. 19, pp. 4251, 2019. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.