Neural Computing and Applications

, Volume 28, Issue 2, pp 335–344 | Cite as

Prediction of local scour around bridge piers using the ANFIS method

  • Sung-Uk ChoiEmail author
  • Byungwoong Choi
  • Seonmin Lee
Original Article


Local scour around bridge piers is a complicated physical process and involves highly three-dimensional flows. Thus, the scour depth, which is directly related to the safety of a bridge, cannot be given in the form of the exact relationship of dependent variables via an analytical method. This paper proposes the use of the adaptive neuro-fuzzy inference system (ANFIS) method for predicting the scour depth around a bridge pier. Five variables including mean velocity, flow depth, size of sediment particles, critical velocity for particles’ initiation of motion, and pier width were used for the scour depth. For comparison, predictions by the artificial neural network (ANN) model were also provided. Both the ANN model and ANFIS method were trained and validated. The findings indicate that the modeling with dimensional variables yields better predictions than when normalized variables are used. The ANN model was applied to a field-scale dataset. Prediction results indicated that the errors are much larger compared to the case of a laboratory-scale dataset. The MAPE by the ANN model trained with part of the field data was not seriously different from that by the model trained with the laboratory data. However, the application of the ANFIS method improved the predictions significantly, reducing the MAPE to the half of that by the ANN model. Five selected empirical formulas were also applied to the same dataset, and Sheppard and Melville’s formula was found to provide the best prediction. However, the MAPEs for the scour depths predicted by empirical formulas are much larger than MAPEs by either the ANN or the ANFIS method. The ANFIS method predicts much better if the range of the training dataset is sufficiently wide to cover the range of the application dataset.


Adaptive network-based fuzzy inference system Artificial neural network Local scour Bridge piers 



This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (NRF-2012R1A2A2A02047549).


  1. 1.
    Melville BW, Coleman SE (2000) Bridge scour. Water Resources Publications, LLC, USAGoogle Scholar
  2. 2.
    Ettema R, Melville BW, Barkdoll B (1998) Scale effect in pier-scour experiments. J Hydraul Eng 124(6):639–642CrossRefGoogle Scholar
  3. 3.
    Ge L, Sotiropoulos F (2005) 3D unsteady RANS modeling of complex hydraulic engineering flows. I: numerical model. J Hydraul Eng 131(9):800–808CrossRefGoogle Scholar
  4. 4.
    Ge L, Lee SO, Sturm T (2005) 3D unsteady RANS modeling of complex hydraulic engineering flows. I: model validation and flow physics. J Hydraul Eng 131(9):809–820CrossRefGoogle Scholar
  5. 5.
    Kirkil G, Constantinescu G, Ettema R (2009) Detached eddy simulation investigation of turbulence at a circular pier with scour hole. J Hydraul Eng 135(11):888–901CrossRefGoogle Scholar
  6. 6.
    Baranya S, Olsen NRB, Stoesser T, Sturm T (2012) Three-dimensional RANS modeling of flow around circular piers using nested grids. Eng Appl Comput Fluid Mech 6(4):648–662Google Scholar
  7. 7.
    Jia Y, Altinakar M, Guney MS, Aksoy AO, Bombar G (2013) 3D numerical simulations of local scouring around bridge piers under non-uniform sediment conditions. In: Proceedings of 2013 IAHR world congress, IAHR, Chengdu, China, pp 1–12Google Scholar
  8. 8.
    Choi S-U, Cheong S (2006) Prediction of local scour around bridge piers using artificial neural networks. J Am Water Resour Assoc 42(2):487–494CrossRefGoogle Scholar
  9. 9.
    Bateni SM, Jeng DS, Melville BW (2007) Bayesian neural networks for prediction of equilibrium and time-dependent scour depth around bridge piers. Adv Eng Softw 38(2):102–111CrossRefGoogle Scholar
  10. 10.
    Bateni SM, Borghei SM, Jeng DS (2007) Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Eng Appl Artif Intell 20(3):401–414CrossRefGoogle Scholar
  11. 11.
    Firat M (2009) Scour depth prediction at bridge piers by Anfis approach. Proc ICE-Water Manag 162(4):279–288Google Scholar
  12. 12.
    Muzzammil M (2010) ANFIS approach to the scour depth prediction at a bridge abutment. J Hydroinform 12(4):474–485CrossRefGoogle Scholar
  13. 13.
    Bateni SM, Jeng DS (2007) Estimation of pile group scour using adaptive neuro-fuzzy approach. Ocean Eng 34(8):1344–1354CrossRefGoogle Scholar
  14. 14.
    Zounemat-Kermani M, Beheshti AA, Ataie-Ashtiani B, Sabbagh-Yazdi SR (2009) Estimation of current-induced scour depth around pile groups using neural network and adaptive neuro-fuzzy inference system. Appl Soft Comput 9(2):746–755CrossRefGoogle Scholar
  15. 15.
    Akib S, Mohammadhassani M, Jahangirzadeh A (2014) Application of ANFIS and LR in prediction of scour depth in bridges. Comput Fluids 91:77–86CrossRefGoogle Scholar
  16. 16.
    Keshavarzi A, Gazni R, Homayoon SR (2012) Prediction of scouring around an arch-shaped bed sill using neuro-fuzzy model. Appl Soft Comput 12(1):486–493CrossRefGoogle Scholar
  17. 17.
    Sheppard DM, Melville B, Demir H (2014) Evaluation of existing equations for local scour at bridge piers. J Hydraul Eng 140(1):14–23CrossRefGoogle Scholar
  18. 18.
    Rumelhart DE, McClelland JL, PDP Research Group (1986) Parallel distributed processing: explorations in the microstructure of cognition, volume 1: foundations. MIT Press, CambridgeGoogle Scholar
  19. 19.
    Jang JSR (1993) ANFIS: adaptive network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23(3):665–685CrossRefGoogle Scholar
  20. 20.
    Takagi MT, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132CrossRefzbMATHGoogle Scholar
  21. 21.
    Chabert J, Engeldinger P (1956) Etude des affouillements autour des piles des ponts. Laboratoire Nationale d’Hydraulique, ChatouGoogle Scholar
  22. 22.
    Shen HW, Schneider VR, Karaki S (1969) Local scour around bridge piers. J Hydraul Div 95(HY5):1919–1940Google Scholar
  23. 23.
    Jain SC, Fischer EE (1979) Scour around circular bridge piers at high Froude numbers. In: FHWA-RD-79-104, Federal Highway Administration, US Department of Transportation, Washington, D.C., USAGoogle Scholar
  24. 24.
    Dey S, Bose SK, Sastry GLN (1995) Clear water scour at circular piers: a model. J Hydraul Eng 121(12):869–876CrossRefGoogle Scholar
  25. 25.
    Yanmaz AM, Altinbilek HD (1991) Study of time dependent local scour around bridge piers. J Hydraul Eng 117:1247–1268CrossRefGoogle Scholar
  26. 26.
    Gao D, Posada GL, Nordin CF (1993) Pier scour equations used in the People’s Republic of China—review and summary. In: FHWA-SA-93-076, Federal Highway Administration, US Department of Transportation, Washington, D.C., USAGoogle Scholar
  27. 27.
    Jain SC (1981) Maximum clear-water scour around circular piers. J Hydraul Div 107(HY5):611–626Google Scholar
  28. 28.
    Froehlich DC (1988) Analysis of onsite measurements of scour at piers. In: ASCE National Hydraulic Engineering Conference, ASCE, Colorado Springs, CO, pp 534–539Google Scholar
  29. 29.
    Richardson EV, Davis SR (1995) Evaluating scour at bridges. In: FHWA-IP-90-017, Hydraulic Engineering Circular No. 18 (HEC-18) (3rd edn), Office of Technology Applications, HTA-22, Federal Highway Administration, US Department of Transportation, Washington, D.C., USAGoogle Scholar
  30. 30.
    Melville BW (1997) Pier and abutment scour: integrated approach. J Hydraul Eng 123(2):125–136CrossRefGoogle Scholar
  31. 31.
    Chiew YM (1984) Local scour at bridge piers. Ph.D. thesis, School of Engineering, The University of Auckland, New ZealandGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringYonsei UniversitySeoulKorea

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