235
Views
3
CrossRef citations to date
0
Altmetric
Research Articles

Bagged neural network for estimating the scour depth around pile groups

, , & ORCID Icon
Pages 401-412 | Received 11 May 2017, Accepted 22 Aug 2017, Published online: 18 Dec 2017

References

  • Amini, A., et al., 2011. A local scour prediction method for pile caps in complex piers. Proceedings of the Institution of Civil Engineers Water Management 164, 73–80. doi: 10.1680/wama.900064
  • Amini, A., et al., 2012. Clear-water local scour around pile groups in shallow-water flow. Journal of Hydraulic Engineering, ASCE, 138 (2), 177–185. doi: 10.1061/(ASCE)HY.1943-7900.0000488
  • Amini, A. and Mohamed, T.A., 2016. Local scour prediction around piers with complex geometry. Accepted Manuscript.
  • Arneson, L., et al., 2012. Evaluating scour at bridges. Hydraulic Engineering Circular No. 18 Report No. FHWA HIF 12-003, Federal Highway Administration, Washington, DC.
  • Ataie-Ashtiani, B. and Beheshti, A.A., 2006. Experimental investigation of clear-water local scour at pile groups. Journal of Hydraulic Engineering, ASCE, 132 (10), 1100–1104. doi: 10.1061/(ASCE)0733-9429(2006)132:10(1100)
  • Azmathullah, H.M., Deo, M.C., and Deolalikar, P.B., 2005. Neural network for estimation of scour downstream of a ski-jump bucket. Journal of Hydraulic Engineering, ASCE, 131 (10), 898–908. doi: 10.1061/(ASCE)0733-9429(2005)131:10(898)
  • Azamathulla, H.M., Deo, M.C., and Deolalikar, P.B., 2008. Alternative neural networks to estimate the scour below spillways. Advances in Engineering Software, 39 (8), 689–698. doi: 10.1016/j.advengsoft.2007.07.004
  • Bateni, S.M., Borghei, S.M., and Jeng, D.-S., 2007a. Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Engineering Applications of Artificial Intelligence, 20 (13), 401–414. doi: 10.1016/j.engappai.2006.06.012
  • Bateni, S.M., Jeng, D.S., and Melville, B.W., 2007b. Bayesian neural networks for prediction of equilibrium and time-dependant scour depth around bridge piers. Advances in Engineering Software, 38 (2), 10–111. doi: 10.1016/j.advengsoft.2006.08.004
  • Beale, M.H., Hagan, M.T., and Demuth, H.B., 2011. Neural network toolbox user’s guide. Natica: MathWorks, Inc, version 7.0.1(release 2011a) Edition.
  • Breiman, L., 1996. Bagging predictors. Machine Learning, 24 (2), 123–140.
  • Breusers, H.N.C., Nicollet, G., and Shen, H.W., 1977. Local scour around cylindrical piers. Journal of Hydraulic Research, IAHR, 15 (3), 211–252. doi: 10.1080/00221687709499645
  • Bühlmann, P., 2012. Bagging, boosting and ensemble methods. In: J.E. Gentle, W.K. Härdlel, and Y. Mori, eds. Handbook of computational statistics. Springer handbooks of computational statistics. Berlin: Springer, 985–1022.
  • Cheng, M., Cao, M., and Wu, Y., 2015. Predicting equilibrium scour depth at bridge piers using evolutionary radial basis function neural network. Journal of Computing in Civil Engineering, 29 (5), 04014070. doi: 10.1061/(ASCE)CP.1943-5487.0000380
  • Coleman, S., 2005. Clearwater local scour at complex piers. Journal of Hydraulic Engineering, ASCE, 131 (4), 330–334. doi: 10.1061/(ASCE)0733-9429(2005)131:4(330)
  • Dan Foresee, F. and Hagan, M.T., 1997. Gauss-newton approximation to Bayesian learning. In: International Conference on Neural Networks, Vol. 3, 1930–1935.
  • Deng, L. and Cai, C., 2010. Bridge scour: prediction, modeling, monitoring, and countermeasures – a review. Practice Periodical on Structural Design and Construction, 15 (2), 125–134. doi: 10.1061/(ASCE)SC.1943-5576.0000041
  • Dennis, J.E. and More, J.J., 1977. Quasi-newton methods, motivation and theory. SIAM Review, 19 (1), 46–89. doi: 10.1137/1019005
  • Dietz, J.W., 1973. Kolkbildung an einem Kreiszylindrischen Pfeilerpaar. Die Bautechnik, 50 (6), 203–208.
  • Eghbalzadeh, A., et al., 2016. Discharge prediction of circular and rectangular side orifices using artificial neural networks. KSCE Journal of Civil Engineering, 20 (2), 990–996. doi: 10.1007/s12205-015-0440-y
  • Froehlich, D.C., 1989. Local scour at bridge abutments. In: 1989th National Conference on Hydraulic Engineering. ASCE, pp. 13–18.
  • Gencay, R. and Qi, M., 2001. Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging. IEEE Transactions on Neural Networks, 12 (4), 726–734. doi: 10.1109/72.935086
  • Guven, A., Azamathulla, H., and Zakaria, N., 2009. Linear genetic programming for prediction of circular pile scour. Ocean Engineering, 36 (12), 985–991. doi: 10.1016/j.oceaneng.2009.05.010
  • Guven, A., Azamathulla, H., and Gunal, M., 2012. A comparative study of predicting scour around a circular pile. ICE Maritime Engnieering, 165 (1), 31–40. doi: 10.1680/maen.2012.165.1.31
  • Hagan, M.T. and Menhaj, M.B., 1994. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5 (6), 989–993. doi: 10.1109/72.329697
  • Hagan, M.T., Dcmuth, H.B., and Beale, M., 1996. Neural network design. Boston: PWS.
  • Hannah, C.R., 1978. Scour at pile groups. Master’s thesis. University of Canterbury, Christchurch, New Zealand.
  • Heskes, T., 1996. Balancing between bagging and bumping. In: M.C. Mozer, M.I. Jordan, and T. Petsche, eds. Advances in neural information processing systems. Cambridge: MIT Press, 466–472.
  • Hornik, K., Stinchcombe, M., and White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2 (5), 359–366. doi: 10.1016/0893-6080(89)90020-8
  • Hosseini, R. and Amini, A., 2015. Scour depth estimation methods around pile groups. KSCE Journal of Civil Engineering, 19 (7), 2144–2156. doi: 10.1007/s12205-015-0594-7
  • Ismail, A., et al., 2013. Predictions of bridge scour: application of a feed-forward neural network with an adaptive activation function. Engineering Applications of Artificial Intelligence, 26 (5–6), 1540–1549. doi: 10.1016/j.engappai.2012.12.011
  • Kambekar, A.R. and Deo, M.C., 2003. Estimation of pile group scour using neural networks. Applied Ocean Research, 25 (4), 225–234. doi: 10.1016/j.apor.2003.06.001
  • Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence, 14, 1137–1145.
  • Liang, F., et al., 2017. Experimental observations and evaluations of formulae for local scour at pile groups in steady currents. Marine Georesources & Geotechnology, 35 (2), 245–255. doi: 10.1080/1064119X.2016.1147510
  • Melville, B.W. and Sutherland, A., 1988. Design method for local scour at bridge piers. Journal of Hydraulic Engineering, ASCE, 114 (10), 1210–1226. doi: 10.1061/(ASCE)0733-9429(1988)114:10(1210)
  • Melville, B.W. and Chiew, Y., 1999. Time scale for local scour at bridge piers. Journal of Hydraulic Engineering, ASCE, 125 (1), 59–65. doi: 10.1061/(ASCE)0733-9429(1999)125:1(59)
  • Muzzammil, M., Alama, J., and Danish, M., 2015. Scour prediction at bridge piers in cohesive bed using gene expression programming. Aquatic Procedia, 4, 789–796.
  • Nagy, H.M., Watanabe, K., and Hirano, M., 2002. Prediction of sediment load concentration in river using artificial neural network model. Journal of Hydraulic Engineering, ASCE 128, (6), 588–595. doi: 10.1061/(ASCE)0733-9429(2002)128:6(588)
  • Najafzadeh, M. and Azamathulla, H., 2015. Neuro-fuzzy GMDH to predict the scour pile groups due to waves. Journal of Computing in Civil Engineering, 29 (5), 04014068. doi: 10.1061/(ASCE)CP.1943-5487.0000376
  • Nazariha, M., 1996. Design relationships for maximum local scour depth for bridge pier groups. Ph.D. thesis. University of Ottawa.
  • Nguyen, D. and Widrow, B., 1990. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. IJCNN International Joint Conference on Neural Networks, 3, 21–26.
  • Nourani, V. and Babakhani, A., 2013. Integration of artificial neural networks with radial basis function interpolation in earthfill dam seepage modeling. Journal of Computing in Civil Engineering, 27 (2), 183–195. doi: 10.1061/(ASCE)CP.1943-5487.0000200
  • Prechelt, L., 2012. Early stopping, but when? In: G. Montavon, G.B. Orr, and K.-R. Müller, eds. Neural networks: tricks of the trade. Berlin: Springer, 53–67.
  • Richardson, E.V. and Davis, S.R., 2001. Evaluating scour at bridges. Hydraulic Engineering Circular No. 18 Report No. FHWA NHI 01-001, Federal Highway Administration, Washington, DC.
  • Salim, M. and Jones, J.S., 1996. Scour around exposed pile foundations. In: C.T. Bathala, ed. North American water and environment congress. Anaheim, CA: ASCE, 2202–2211.
  • Sewell, M., 2011. Ensemble learning. Research Note RN/11/02, UCL department of computer science.
  • Sheppard, D.M., 1998. Conditions of maximum local structure-induced sediment scour. In: E.V. Richardson and P.F. Lagasse, eds. Stream stability and scour at highway bridges, compendium of papers ASCE water resource engineering conferences 1991–1998. Reston, VA: ASCE, 347–364.
  • Sheppard, D.M., Odeh, M., and Glasser, T., 2004. Large scale clear-water local pier scour experiments. Journal of Hydraulic Engineering, ASCE, 130 (10), 957–963. doi: 10.1061/(ASCE)0733-9429(2004)130:10(957)
  • Sheppard, D.M. and Glasser, T., 2009. Local scour at bridge piers with complex geometries. In: M. Iskander, D.F. Laefer, and M.H. Hussein, eds. Contemporary topics in in situ testing, analysis, and reliability of foundations. Orlando: ASCE, 506–513.
  • Sheppard, D.M., Demir, H., and Melville, B.W., 2011. Scour at wide piers and long skewed piers. National Cooperative Highway Research Program, National Research Board, Washington, DC.
  • Shigidi, A. and Garcia, L., 2003. Parameter estimation in groundwater hydrology using artificial neural networks. Journal of Computing in Civil Engineering, 17 (4), 281–289. doi: 10.1061/(ASCE)0887-3801(2003)17:4(281)
  • Solaimani, N., et al., 2017. The effect of pile spacing and arrangement on bed formation and scour hole dimensions in pile groups. International Journal of River Basin Management, 15 (2), 219–225. doi: 10.1080/15715124.2016.1274321
  • Wang, W., Van Gelder, P.H.A.J.M., and Vrijling, J.K., 2007. Comparing Bayesian regularization and crossvalidated early-stopping for streamflow forecasting with ANN models. In: Z.W. Kundzewicz, ed. International association of hydrological sciences. Nanjing: IAHS-AISH, 216–221.
  • Zhao, G. and Sheppard, D.M., 1998. The effect of flow skew angle on sediment scour near pile groups. In: E.V. Richardson and P.F. Lagasse, eds. Stream stability and scour at highway bridges, compendium of papers ASCE water resource engineering conferences 1991–1998. Reston, VA: ASCE, 377–391.
  • Zounement-Kermani, M., et al., 2009. Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Applied Soft Computing, 9 (2), 746–755. doi: 10.1016/j.asoc.2008.09.006

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.