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Article

Deep neural network based pier scour modeling

Pages 80-85 | Received 09 Jul 2019, Accepted 09 Oct 2019, Published online: 16 Oct 2019

References

  • Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jozefowicz, R., Jia, Y., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Schuster, M., Monga, R., Moore, S., Murray, D., Olah, C., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Xiaoqiang Zheng, X. (2016). “Tensor flow: Large-scale machine learning on heterogeneous systems.” https://arxiv.org/pdf/1603.04467.
  • Azmathullah, H.M., Deo, M.C., and Deolalikar, P.B. (2006). “Estimation of scour below spillway using neural networks.” J. Hydraul. Res. IAHR, 44(1), 61–69. doi:https://doi.org/10.1080/00221686.2006.9521661
  • Bateni, S.M., Borghei, S.M., and Jeng, D.-S. (2007). “Neural network and neuro-fuzzy assessments for scour depth around bridge piers.” Eng. Appl. Artif. Intell., 20(3), 401–414.
  • Bogdan, T., Magdalena, S., Zbigniew, T., and Tadeusz, L. (2012). “Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms.” Int. J. Appl. Math. Comput. Sci., 22(4), 867–881.
  • Deng, F., He, Y., Zhou, S., Yu, Y., Cheng, H., and Wu, X. (2018). “Compressive strength prediction of recycled concrete based on deep learning.” Constr. Build. Mater., 175, 562–569
  • Dick, K., Russell, L., Souley Dosso, Y., Kwamena, F., and Green, J.R. (2019). “Deep learning for critical infrastructure resilience.” J. Infrastruct. Syst., 25(2), 05019003. doi:https://doi.org/10.1061/(ASCE)IS.1943-555X.0000477
  • Ding, F., Zhang, Z., Zhou, Y., Chen, X., and Ran, B. (2019). “Large-scale full-coverage traffic speed estimation under extreme traffic conditions using a big data and deep learning approach: Case study in China.” J. Transp. Eng., Part A Syst., 145(5), 05019001.
  • Ebtehaj, I., Sattar, A., Bonakdari, H., and Zaji, A.H. (2017). “Prediction of scour depth around bridge piers using self-adaptive extreme learning machine.” J. Hydroinf., 19(2), 207–224. doi:https://doi.org/10.2166/hydro.2016.025
  • Eghbalzadeh, A., Hayati, M., Rezaei, A., and Javan, M. (2018). “Prediction of equilibrium scour depth in uniform non-cohesive sediments downstream of an apron using computational intelligence.” Eur. J. Environ. Civ. Eng., 22(1), 28–41.
  • Glorot, X., and Bengio, Y. (2010). “Understanding the difficulty of training deep feedforward neural networks.” Proceedings of the thirteenth international conference on artificial intelligence and statistics Y.W. Teh and M. Titterington, eds., (May 13–15), Sardinia, Italy, 249–256.
  • Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning, MIT press, Cambridge, USA.
  • Hochreiter, S., and Schmidhuber, J. (1997). “Long short-term memory.” Neural Comput., 9(8), 1735–1780.
  • Hosseini, R., Fazloula, R., Saneie, M., and Amini, A. (2018). “Bagged neural network for estimating the scour depth around pile groups.” Int. J. River Basin Manage., 16(4), 401–412. doi:https://doi.org/10.1080/15715124.2017.1372449
  • Jain, P., Deo, M.C., Latha, G., and Rajendran, V. (2011). “Real time wave forecasting using wind time history and numerical model.” Ocean Modell., 36(1–2), 26–39. doi:https://doi.org/10.1016/j.ocemod.2010.07.006
  • Jones, J.S. (1984).“Comparison of prediction equations for bridge pier and abutment scour.” Trans. Res. Rec. 1950, Transportation Research Board, Washington.
  • Kambekar, A.R., and Deo, M.C. (2003). “Estimation of group pile scour using neural networks.” J Appl. Ocean Res., 25(4), 225–234. doi:https://doi.org/10.1016/j.apor.2003.06.001
  • Kandasamy, J.K., and Melville, B.W. (1998). “Maximum local scour depth at bridge piers and abutments.” J. Hydraul. Res. IAHR, 36(2), 183–198. doi:https://doi.org/10.1080/00221689809498632
  • Kaya, A. (2010). “Artificial neural network study of observed pattern of scour depth around bridge piers.” Comput. Geotech., 37(3), 413–418. doi:https://doi.org/10.1016/j.compgeo.2009.10.003
  • Kingma, D.P., and Ba, J. (2015). “Adam: A method for stochastic optimization.” 3rd International Conference on Learning Representations, May 7–9, 2015, San Diego. https://arxiv.org/abs/1412.6980 (Jun. 15, 2019).
  • Kothyari, U.C., Grade, R.J., and RangaRaju, K.G. (1992). “Temporal variation of scour around circular bridge piers.” J. Hydraul. Eng., 118(8), 1091–1106. doi:https://doi.org/10.1061/(ASCE)0733-9429(1992)118:8(1091)
  • Kumar, S.S., and Abraham, D.M. (2019). “A deep learning based automated structural defect detection system for sewer pipelines.” ASCE International conference on computing in civil engineering 2019: Smart cities, sustainability, and resilience, Y.K. Cho, F. Leite, A. Behzadan and C. Wang, eds., (June 17–19), Atlanta, Georgia, 226–233
  • Lee, T.L., Jeng, D.S., Zhang, G.H., and Hong, J.H. (2007). “Neural network modeling for estimation of scour depth around bridge piers.” J. Hydrodyn., 19(3), 378–386. doi:https://doi.org/10.1016/S1001-6058(07)60073-0
  • Mueller, D.S., and Wagner, C.R. (2005). “Field observations and evaluations of streambed scour at bridges.” Office of engineering research and development, federal highway administration, Report No. FHWA–RD–03–052, 134 .
  • Nair, V., and Hinton, G.E. (2010). “Rectified linear units improve restricted boltzmann machines.” In Proceedings of the 27th international conference on machine learning (ICML-10), Haifa, Israel, June 21–24, 807–814.
  • Nguyen, T., Kashani, A., Ngo, T., and Bordas, S. (2019). “Deep neural network with high‐order neuron for the prediction of foamed concrete strength.” Comput.-Aided Civ. Infrastruct. Eng, 34(4), 316–332. doi:https://doi.org/10.1111/mice.2019.34.issue-4
  • Pal, M., Singh, N.K., and Tiwari, N.K. (2012). “M5 model tree for pier scour prediction using field dataset.” KSCE Civ. En., 16(6), 1079–1084. doi:https://doi.org/10.1007/s12205-012-1472-1
  • Pal, M., Singh, N.K., and Tiwari, N.K. (2013). “Pier scour modelling using random forest regression.” ISH J. Hydraul. Eng., 19(2), 69–75. doi:https://doi.org/10.1080/09715010.2013.772763
  • Pal, M., Singh, N.K., and Tiwari, N.K. (2014). “Kernel methods for pier scour modeling using field data.” J. Hydroinf., 16(4), 784–796. doi:https://doi.org/10.2166/hydro.2013.024.
  • Singh, K.K., Pal, M., and Singh, V.P. (2010). “Estimation of mean annual flood in indian catchments using backpropagation neural network and M5 model tree.” Water Res. Manage., 24(10), 2007–2019. doi:https://doi.org/10.1007/s11269-009-9535-x.
  • Solomatine, D.P., and Siek, M.B. (2004). “Flexible and optimal M5 model trees with applications to flow predictions.” Proceedings of 6th international conference on hydroinformatics, Liong, Phoon, and Babovic, Eds., World Scientific Press, Singapore, 1719–1726.
  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014). “Dropout: A simple way to prevent neural networks from overfitting.” J. Mach. Learn. Res., 15(1), 1929–1958.
  • Zhang, Z., Wang, Y., Chen, P., and Yu, G. (2017). “Application of long short-term memory neural network for multi-step travel time forecasting on urban expressways.” Proceedings of the 17th COTA International Conference of Transportation Professionals, H. Wang, J. Sun, J. Lu, L. Zhang, Y. Zhang and S. Fang, eds., (July 7–9), Shanghai, China, 444–454.
  • Zhou, L., and Chen, X. (2017). “Short-term forecasting of traffic flow and speed: A deep learning approach.” Proceedings of the 17th COTA International conference of transportation professionals, H. Wang, J. Sun, J. Lu, L. Zhang, Y. Zhang and S. Fang, eds., (July 7–9), Shanghai, China, 2186–2196.

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