Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32451
Scour Depth Prediction around Bridge Piers Using Neuro-Fuzzy and Neural Network Approaches

Authors: H. Bonakdari, I. Ebtehaj


The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation.

Keywords: Adaptive neuro-fuzzy inference system, ANFIS, artificial neural network, ANN, bridge pier, scour depth, nonlinear regression, NLR.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 830


[1] N. H. C. Breusers, A. J. Raudkivi, “Scouring,” 2nd hydraulic structures design manual, Balkema, Rotterdam, The Netherlands, 1991
[2] S. Das, R. Das, A. Mazumdar, “Circulation characteristics of horseshoe vortex in scour region around circular piers,” Water Sci. Eng., vol. 6, no. 1, pp. 59-77, 2013.
[3] S. Das, A. Mazumdar, “Comparison of Kinematics of Horseshoe Vortex at a Flat Plate and Different Shaped Piers,” Int. J. Fluid Mech. Res., vol. 42, no. 5, 2015.
[4] E. M. Laursen, A. Toch, “Scour Around Bridge Piers and Abutments,” Iowa Highway Research Board, Ames, IA, USA, Bulletin 4, 1956.
[5] B. V. Melville, A. J. Sutherland, “Design method for local scour at bridge piers,” J. Hydraul. Eng., vol. 114, no. 10, pp. 1210–1226, 1998.
[6] E. V. Richardson, S. R. Davis, “Evaluating Scour at Bridges,” Hydraulic Engineering Circular No. 18 (HEC-18), US Department of Transportation, Federal Highway, 2001.
[7] I. Ebtehaj, H. Bonakdari, F. Khoshbin, H. Azimi, “Pareto Genetic Design of GMDH-type Neural Network for Predict Discharge Coefficient in Rectangular Side Orifices,” Flow Meas. Instrum.,. vol. 41, pp. 67-74. 2015.
[8] I. Ebtehaj, H. Bonakdari, A. H. Zaji, H. Azimi, F. Khoshbin, “GMDH-type neural network approach for modeling the discharge coefficient of rectangular sharp-crested side weirs,” Eng. Sci. Technol. Int. J., vol. 18, no. 4, pp. 746-757, 2015.
[9] F. Khoshbin, H. Bonakdari, S. H. Ashraf Talesh, I. Ebtehaj, A. H. Zaji, H. Azimi, “Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs,” Eng. Optimiz., vol. 48, no. 6, pp. 933-948, 2015
[10] M. Najafzadeh, Gh-A. Barani, M. R. Hessami-Kermani, “Abutment scour in live-bed and clear-water using GMDH Network,” Water Sci. Technol., vol. 67, no. 5, pp. 1121-1128, 2013.
[11] M. Najafzadeh, G. A. Barani, H. M. Azamathulla, “Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling,” Neural Comput. Appli., vol. 24, no. 3-4, pp. 629-635, 2014.
[12] M. Najafzadeh, A. Zahiri, “Neuro-Fuzzy GMDH-Based Evolutionary Algorithms to Predict Flow Discharge in Straight Compound Channels,” J. Hydrol Eng., 04015035, 2015.
[13] A. R. Kambekar, M. C. Deo, “Estimation of Pile Group Scour Using Neural Networks,” Appl. Ocean Res., vol. 25, no. 4, pp. 225-234, 2003.
[14] S. U. Choi, S. Cheong, “Prediction of local scour around bridge piers using artificial neural networks,” J. American Water Resour. Assoc., vol. 42, no. 2, pp. 487-494, 2006
[15] I. Ebtehaj, H. Bonakdari, “Evaluation of Sediment Transport in Sewer using Artificial Neural Network,” Eng. Appl. Comput. Fluid Mech., vol. 7, no. 3, pp. 382–392.
[16] O. F. Dursun, N. Kaya, M. Firat, “Estimating discharge coefficient of semi-elliptical side weir using ANFIS,” J. Hydrol., vol. 426, pp. 55-62, 2012.
[17] F. Salmasi, M. Özger, “Neuro-Fuzzy Approach for Estimating Energy Dissipation in Skimming Flow over Stepped Spillways,” Arab. J. Sci. Eng., vol. 39, no. 8, pp. 6099-6108, 2014.
[18] I. Ebtehaj, H. Bonakdari, “Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers,” Water Resour. Manage., vol. 28, no. 13, pp. 4765–4779, 2014.
[19] K. Hornik, M. Stinchcombe, H. White, “Multilayer feedforward networks are universal approximators,” Neural networks, vol. 2, no. 5, pp. 359-366, 1989.
[20] Z. F. Liu, X. P. Liu, S. W. Wang, G. F. Liu, “Recycling strategy and a recyclability assessment model based on an artificial neural network,” J. mater. Process. Tech., vol. 129, no. 1, pp. 500-506, 2002.
[21] M. Gupta, L. Jin, N. Homma, “Static and dynamic neural networks: from fundamentals to advanced theory,” John Wiley & Sons, 2004.
[22] I. Ebtehaj, H. Bonakdari, “Assessment of evolutionary algorithms in predicting non-deposition sediment transport. Urban Water J., vol. 3, no. 5, pp. 499-510, 2016.
[23] J. S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Trans Syst Manage Cyb., vol. 23, no. 3, pp. 665–685, 1993.
[24] M. N. Landers, D. S. Mueller, “U.S. Geological survey field measurements of pier scour,” Proce. Compendium of papers on ASCE water resources engineering conference 1991 to 1998, pp. 585-607, 1999.
[25] T. H. Mohammed, M. J. M. M. Noor, A. H. Ghazali, B. B. K. Huat, “Validation of some bridge pier scour formulate using field and laboratory data.” Am. J. Environ. Sci., vol. 1, no. 2, pp. 119-125, 2005.