All Issue

2020 Vol.22, Issue 5 Preview Page

Research Paper


September 2020. pp. 515-528
Abstract


References
1 

Arjovsky, M., Chintala, S., Bottou, L. (2017), "Wasserstein generative adversarial networks", Proceedings of the 34th International Conference on Machine Learning, Sydney, pp. 214-223.

2 

Cha, Y.J., Choi, W., Büyüköztürk, O. (2017), "Deep learning-based crack damage detection using convolutional neural networks", Computer-Aided Civil and Infrastructure Engineering, Vol. 32, No. 5, pp. 361-378.

10.1111/mice.12263
3 

Dawood, T., Zhu, Z., Zayed, T. (2017), "Machine vision-based model for spalling detection and quantification in subway networks", Automation in Construction, Vol. 81, pp. 149-160.

10.1016/j.autcon.2017.06.008
4 

Dorafshan, S., Thomas, R.J., Maguire, M. (2018), "SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks", Data in Brief, Vol. 21, pp. 1664-1668.

10.1016/j.dib.2018.11.01530505897PMC6247444
5 

Feng, C., Zhang, H., Wang, H., Wang, S., Li, Y. (2020), "Automatic pixel-level crack detection on dam surface using deep convolutional network", Sensors, Vol. 20, No. 7, pp. 2069.

10.3390/s2007206932272652PMC7180706
6 

FTA (Federal Transit Administration) (1997), "Inspection Policy and Procedures for Rail Transit Tunnels and Underground Structures", TCRP Synthesis 23, Transportation Research Board National Research Council. [Online]. Available:http://onlinepubs.trb.org/onlinepubs/tcrp/tsyn23.pdf

7 

He, K., Gkioxari, G., Dollár, P., Girshick, R. (2017), "Mask R-CNN", Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp. 2961-2969.

10.1109/ICCV.2017.322
8 

Hong, S.H., Kim, J.G., Cho, J.Y., Kim, T.H. (2020), "A study on the necessity of verification and certification system of inspection and diagnostic equipment for infrastructure using advanced technologies", Journal of the Society of Disaster Information, Vol. 16, No. 1, pp. 163-177.

9 

Hung, W.C., Tsai, Y.H., Liou, Y.T., Lin, Y.Y., Yang, M.H. (2018), "Adversarial learning for semi-supervised semantic segmentation", arXiv:1802.07934. [Online]. Available: https://arxiv.org/abs/1802.07934

10 

Kim, B., Cho, S. (2018), "Automated vision-based detection of cracks on concrete surfaces using a deep learning technique", Sensors, Vol. 18, No. 10, pp. 3452.

10.3390/s1810345230322206PMC6210028
11 

Kim, B., Cho, S. (2019), "Image-based concrete crack assessment using mask and region-based convolutional neural network", Structural Control and Health Monitoring, Vol. 26, No. 8, pp. e2381.

10.1002/stc.2381
12 

Kim, M.K., Sohn, H., Chang, C.C. (2015), "Localization and quantification of concrete spalling defects using terrestrial laser scanning", Journal of Computing in Civil Engineering, Vol. 29, No. 6, pp. 04014086.

10.1061/(ASCE)CP.1943-5487.0000415
13 

Kingma, D.P., Ba, J. (2015), "ADAM: a method for stochastic optimization", Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, pp. 1-15.

14 

Kohavi, R. (1995), "A study of cross-validation and bootstrap for accuracy estimation and model selection", Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Vol. 14, No. 2, pp. 1137-1145.

15 

Lee, Y.I., Kim, B., Cho, S. (2018), "Image-based spalling detection of concrete structure using deep learning", Journal of the Korea Concrete Institute, Vol. 30, No. 1, pp. 91-99.

10.4334/JKCI.2018.30.1.091
16 

Long, J., Shelhamer, E., Darrell, T. (2015), "Fully convolutional networks for semantic segmentation", Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, pp. 3431-3440.

10.1109/CVPR.2015.7298965
17 

Marszalek, M., Schmid, C. (2007), "Accurate object localization with shape masks", Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, pp. 1-8.

10.1109/CVPR.2007.383085
18 

O'Byrne, M., Ghosh, B., Schoefs, F., Pakrashi, V. (2014), "Regionally enhanced multiphase segmentation technique for damaged surfaces", Computer-Aided Civil and Infrastructure Engineering, Vol. 29, No. 9, pp. 644-658.

10.1111/mice.12098
19 

Park, C.H., Lee, H.I. (2016), Future trend of capital investment for Korean transportation infrastructure, Construction and Economy Research Institute of Korea, pp. 6-8.

20 

Pohlen, T., Hermans, A., Mathias, M., Leibe, B. (2017), "Full-resolution residual networks for semantic segmentation in street scenes", Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, pp. 4151-4160.

10.1109/CVPR.2017.353
21 

Radford, A., Metz, L., Chintala, S. (2015), "Unsupervised representation learning with deep convolutional generative adversarial networks", arXiv:1511.06434, [Online], Available: https://arxiv.org/abs/1511.06434

22 

Ruder, S. (2016), "An overview of gradient descent optimization algorithms", arXiv:1609.04747. [Online]. Available: https://arxiv.org/abs/1609.04747

23 

ScrapeBox-The Swiss Army Knife of SEO! Available online: http://www.scrapebox.com/ (accessed on 29 June 2020).

24 

Xu, B., Wang, N., Chen, T., Li, M. (2015), "Empirical evaluation of rectified activations in convolutional network", arXiv:1505.00853, [Online], Available: https://arxiv.org/abs/1505.00853

25 

Yu, S.N., Jang, J.H., Han, C.S. (2007), "Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel", Automation in Construction, Vol. 16, No. 3, pp. 255-261.

10.1016/j.autcon.2006.05.003
26 

Zhang, W., Zhang, Z., Qi, D., Liu, Y. (2014), "Automatic crack detection and classification method for subway tunnel safety monitoring", Sensors, Vol. 14, No. 10, pp. 19307-19328.

10.3390/s14101930725325337PMC4239952
Information
  • Publisher :Korean Tunneling and Underground Space Association
  • Publisher(Ko) :한국터널지하공간학회
  • Journal Title :Journal of Korean Tunnelling and Underground Space Association
  • Journal Title(Ko) :한국터널지하공간학회 논문집
  • Volume : 22
  • No :5
  • Pages :515-528
  • Received Date :2020. 07. 21
  • Revised Date :2020. 08. 04
  • Accepted Date : 2020. 08. 04