747
Views
10
CrossRef citations to date
0
Altmetric
Articles

Application of a hybrid neural network structure for FWD backcalculation based on LTPP database

, , ORCID Icon &
Pages 3099-3112 | Received 26 Jun 2020, Accepted 25 Jan 2021, Published online: 09 Mar 2021

References

  • Abd El-Raof, S., et al., 2018. Simplified closed-form procedure for network-level determination of pavement layer moduli from falling weight deflectometer data. Journal of Transportation Engineering, Part B: Pavements, 144 (4), 04018052-1–04018052-10. doi:https://doi.org/10.1061/JPEODX.0000080.
  • Amin, J., et al., 2018. Big data analysis for brain tumor detection: deep convolutional neural networks. Future Generation Computer Systems, 87 (Oct), 290–297.
  • Ceylan, H., et al., 2005. Backcalculation of full-depth asphalt pavement layer moduli considering nonlinear stress-dependent subgrade behavior. International Journal of Pavement Engineering, 6, 171–182.
  • Cheng, H. T., et al., 2016. Wide & deep learning for recommender systems. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 2016, 7–10.
  • Cho, K., et al., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
  • Dua, M., Aggarwal, R. K., and Biswas, M, 2019. Discriminatively trained continuous hindi speech recognition system using interpolated recurrent neural network language modeling. Neural Computing and Applications, 31 (10), 6747–6755.
  • Elbagalati, O., et al., 2017. Development of an artificial neural network model to predict subgrade resilient modulus from continuous deflection testing. Canadian Journal of Civil Engineering, 44 (9), 700–706.
  • Everseries User’s Guide, 2005. Pavement analysis computer software and case studies. Available from: http://www.wsdot.wa.gov/biz/mats/pavement/EVERSERS/ [Accessed September 2020].
  • Fabricio, L.V., Vargas-Nordcbeck, A., and Timm, D. H, 2017. Non-destructive evaluation of sustainable pavement technologies using artificial neural networks. International Journal of Pavement Research and Technology, 10 (2), 139–147.
  • Fu, G. Z., et al., 2020. Determination of effective frequency range excited by falling weight deflectometer loading history for asphalt pavement. Construction and Building Materials, 235, 117792-1–117792-9.
  • Gonzalez, J. M., Carbonell, J. M., and Bijsterveld, W. V. 2016. Evaluation of multilayer pavement viscoelastic properties from falling weight deflectometer using neural networks: materials and infrastructures 1. New Jersey: John Wiley & Sons, Inc.
  • Gregor, K., et al., 2015. DRAW: a recurrent neural network for image generation. Computer Science, 2015, 1462–1471.
  • Grenier, S., Konrad, J. M., and LeBœuf, D, 2009. Dynamic simulation of falling weight deflectometer tests on flexible pavements using the spectral element method: forward calculations. Canadian Journal of Civil Engineering, 36 (6), 944–956.
  • He, K., et al., 2015. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, 770–778.
  • Hossain, M., Zaniewski, J., and Rajan, S, 1994. Estimation of pavement – layer moduli using nonlinear optimization technique. Journal of Transportation Engineering, 120 (3), 376–393.
  • Jozefowicz, R., Zaremba, W., and Sutskever, I., 2015. An empirical exploration of recurrent network architectures. International Conference on International Conference on Machine Learning, 2015, 2342–2350.
  • Kim, Y. R., Xu, B., and Kim, Y., 2000. A new backcalculation procedure based on dispersion analysis of FWD time-history deflections and surface wave measurements using artificial neural networks. Symposium on Nondestructive Testing of Pavements & Backcalculation of Moduli: Third Volume.
  • Kutay, M., Chatti, K., and Lei, L, 2011. Backcalculation of dynamic modulus mastercurve from falling weight deflectometer surface deflections. Transportation Research Record: Journal of the Transportation Research Board, 2227, 87–96.
  • LTPP InfoPave, 2020. Research quality pavement performance information. Available from: http://www.infopave.com/ [Accessed April 2020].
  • Marecos, V., et al., 2017. Evaluation of a highway pavement using non-destructive tests: falling weight deflectometer and ground penetrating radar. Construction and Building Materials, 154, 1164–1172.
  • Mousa, M., et al., 2019. Evaluation of interface bonding conditions based on non-destructing testing deflection measurements. Road Materials and Pavement Design, 20, 554–571.
  • Qin, X. R., et al., 2020. Extractive convolutional adversarial networks for network embedding. World Wide Web, 23 (3), 1925–1944.
  • Qureshi, A. S., and Khan, A., 2019. Adaptive transfer learning in deep neural networks: wind power prediction using knowledge transfer from region to region and between different task domains. Computational Intelligence, 35 (4), 1088–1112.
  • Saltan, M., Uz, Volkan, and Aktaş, B, 2013. Artificial neural networks-based backcalculation of the structural properties of a typical flexible pavement. Neural Computing and Applications, 23 (6), 1703–1710.
  • Seo, J. W., et al., 2009. Evaluation of layer properties of flexible pavement using a pseudo-static analysis procedure of falling weight deflectometer. Construction and Building Materials, 23 (10), 3206–3213.
  • Sharma, S., and Das, A., 2008. Backcalculation of pavement layer moduli from falling weight deflectometer data using an artificial neural network. Canadian Journal of Civil Engineering, 35 (1), 57–66.
  • Shi, X., et al., 2015. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems, 2015, 802–810.
  • Szegedy, C., et al., 2016. Inception-v4, inception-ResNET and the impact of residual connections on learning. Thirty-first AAAI conference on artificial intelligence.
  • TensorFlow User’s Guide. (2020). TensorFlow guide. Available from: https://www.tensorflow.org/ [Accessed September 2020].
  • Uzan, J., et al., 1988. A microcomputer based procedure for backcalculating layer moduli from FWD data. Journal of Medical Engineering & Technology, 11 (6), 278–281.
  • Varma, S., and Emin Kutay, M, 2016. Backcalculation of viscoelastic and nonlinear flexible pavement layer properties from falling weight deflections. International Journal of Pavement Engineering, 17 (5), 388–402.
  • Wu, Z., Shen, C., and Van Den Hengel, A., 2019. Wider or deeper: revisiting the ResNET model for visual recognition. Pattern Recognition, 90, 119–133.
  • Zaabar, I., Chatti, K., and Lajnef, N, 2014. Backcalculation of asphalt concrete modulus master curve from field measured falling weight deflectometer data using a new time domain viscoelastic dynamic solution and hybrid optimization scheme. Transportation Research Record: Journal of the Transportation Research Board, 2457, 80–92.
  • Zheng, Z., et al., 2018. Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Transactions on Industrial Informatics, 14 (4), 1606–1615.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.