EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 15 (2024) Int. J. Simul. Multidisci. Des. Optim., 15 (2024) 7 References
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 15, 2024
Modelling and Optimization of Complex Systems with Advanced Computational Techniques
Article Number 7
Number of page(s) 16
DOI https://doi.org/10.1051/smdo/2023023
Published online 19 April 2024
  1. R. Knight, Ground penetrating radar for environmental applications, Annu. Rev. Earth. Planet. Sci. 29, 229–255 (2001) [CrossRef] [Google Scholar]
  2. X.L. Lu, X.Q. Hu, C.P. Luo, Z.Y. Xu, X. Liao, L.H. Liu et al., A rapid imaging method of the seismic back‐scattered wavefield for urban road near‐surface anomalous structures, Near Surf. Geophys. 20, 315–328 (2022) [CrossRef] [Google Scholar]
  3. T. Thongprapha, K. Fuenkajorn, J. Daemen, Study of surface subsidence above an underground opening using a trap door apparatus, Tunn. Undergr. Sp. Tech. 46, 94–103 (2015) [CrossRef] [Google Scholar]
  4. C. Rodeick, Roadbed void detection by ground penetrating radar, Highw. Heavy Constr. 127, 60–61 (1984) [Google Scholar]
  5. H. Liu, Z.S. Shi, J.H. Li, C. Liu, X. Meng, Y.L. Du et al., Detection of road cavities in urban cities by 3d ground-penetrating radar, Geophys. 86, WA25–WA33 (2021) [CrossRef] [MathSciNet] [Google Scholar]
  6. T. Noreen, U. Khan, Using pattern recognition with HOG to automatically detect reflection hyperbolas in ground penetrating radar data, Proceedings of the IEEE International Conference on Electrical and Computing Technologies and Applications (ICECTA), 2017, pp. 1–6 [Google Scholar]
  7. A. Giannopoulos, Modelling ground penetrating radar by gprmax, Constr. Build. Mater. 19, 755–762 (2005) [CrossRef] [Google Scholar]
  8. W. Lei, J. Luo, F. Hou, L. Xu, R.Q. Wang, X.Y. Jiang, Underground cylindrical objects detection and diameter identification in GPR B-scans via the CNN-LSTM framework, Electronics 9, 1804 (2020) [CrossRef] [Google Scholar]
  9. Z. Tong, J. Gao, Z. Han, Z.J. Wang, Recognition of asphalt pavement crack length using deep convolutional neural networks, Road Mater. Pavement. Des. 19, 1334–1349 (2018) [CrossRef] [Google Scholar]
  10. S. Teng, X. Chen, G. Chen et al., Structural damage detection based on convolutional neural networks and population of bridges [J], Measurement, 202, 111747 (2022) [CrossRef] [Google Scholar]
  11. J. Kers, J. Majak, Modelling a new composite from a recycled GFRP [J], Mech. Compos. Mater. 44, 623–632 (2008) [CrossRef] [Google Scholar]
  12. C.C. Zhang, Z.O. Zhou, Research on automatic target detection and orientation of ground penetrating radar in shallow subsurface application, J. Electron. Inf. Technol. 27, 1065–1068 (2005) [Google Scholar]
  13. J. Zhang, X. Yang, W.G. Li, S.B. Zhang, Y.Y. Jia, Automatic detection of moisture damages in asphalt pavements from GPR data with deep CNN and IRS method, Autom. Constr. 113, 103119 (2020) [CrossRef] [Google Scholar]
  14. W.T. Lei, F.F. Hou, J.C. Xi, Q.Y. Tan, M.D. Xu, X.Y. Jiang et al., Automatic hyperbola detection and fitting in GPR B-scan image, Autom. Constr. 106, 102839 (2019) [CrossRef] [Google Scholar]
  15. X.G. Pan, J.P. Shi, P. Luo, X.G. Wang, X.O. Tang, Spatial as deep: Spstial CNN for traffic scene understanding. Thirty-Second AAAI Conference on Artificial Intelligence, 2018, Vol. 32, pp. 1–6 [Google Scholar]
  16. H.B. Hu, H.Y. Fang, N.N. Wang, D. Ma, J.X. Dong, B. Li et al., Defects identification and location of underground space for ground penetrating radar based on deep learning, Tunn. Undergr. Sp. Tech. 140, 105278 (2023) [CrossRef] [Google Scholar]
  17. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez et al., Attention is all you need. NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017, pp. 6000–6010 [Google Scholar]
  18. Z. Liu, Y.T. Lin, Y. Cao, H. Hu, Y.X. Wei, Z. Zhang et al., Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10012–10022 [Google Scholar]
  19. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner et al., An image is worth 16 × 16 words: Transformers for image recognition at scale, arXiv preprint arXiv 2010, 11929 (2020) [Google Scholar]
  20. Z. Ge, S.T. Liu, F. Wang, Z.M. Li, J. Sun, Yolox: Exceeding yolo series in 2021, arXiv preprint arXiv 2107, 08430 (2021) [Google Scholar]
  21. J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3431–3440 [CrossRef] [Google Scholar]
  22. K. He, G. Gkioxari, P. Dollár, R. Girshick, Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2961–2969 [Google Scholar]
  23. P. Asadi, M. Gindy, M. Alvarez, A machine learning based approach for automatic rebar detection and quantification of deterioration in concrete bridge deck ground penetrating radar b-scan images, KSCE J. Civ. Eng. 23, 2618–2627 (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.