Neural Computing and Applications

, Volume 31, Issue 11, pp 7335–7349 | Cite as

Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers

  • B. M. SreedharaEmail author
  • Manu Rao
  • Sukomal Mandal
Original Article


The mechanism of the local scour around bridge pier is so complicated that it is hard to predict the scour accurately using a traditional method frequently by considering all the governing variables and boundary conditions. The present study aims to investigate the application of different hybrid soft computing algorithms, such as particle swarm optimization (PSO)-tuned support vector machine (SVM) and a hybrid artificial neural network-based fuzzy inference system to predict the scour depth around different shapes of the pier using experimental data. The important independent input parameters used in developing the soft computing models are sediment particle size, a velocity of the flow and the time taken in the prediction of the scour depth around the bridge pier. Different pier shapes used in the present study are circular, round-nosed, rectangular and sharp-nosed piers. The accuracy and efficiency of the two hybrid models are analyzed and compared with reference to experimental results using model performance indices (MPI) such as correlation coefficient (CC), normalized root-mean-squared error (NRMSE), normalized mean bias (NMB) and Nash–Sutcliffe efficiency (NSE). The ANFIS model with Gbell membership and the PSO–SVM model with polynomial kernel function yield good results in terms of MPI. The performance of PSO–SVM with polynomial kernel function with CC of 0.949, NRMSE of 7.47, NMB of − 0.009 and NSE of 0.90 reveals that the hybrid ANFIS model with Gbell membership function yields slightly better than that of the PSO–SVM model with CC of 0.950, NRMSE of 6.92, NMB of − 0.002 and NSE of 0.91 for the optimum bridge pier with circular shape, whereas the performance of PSO–SVM model is better than that of ANFIS model for optimum bridge piers with rectangular and sharp nose shape. The PSO–SVM model can be adopted as accurate and efficient alternative approach in predicting scour depth of the pier.


Bridge pier Scour depth PSO–SVM ANFIS 



The authors like to express their sincere thanks to Dr. Goswami Pankaj and his supervisor Dr. Saikia Bibha Das, Gauhati University for providing experimental data. The authors would like to thank Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, for necessary support.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Kothyari UC (2007) Indian practice on estimation of scour around bridge piers—a comment. Sadhana 32(3):187–197CrossRefGoogle Scholar
  2. 2.
    Hamill L (1998) Bridge hydraulics. CRC Press, New YorkGoogle Scholar
  3. 3.
    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
  4. 4.
    Lee TL, Jeng DS, Zhang GH, Hong JH (2007) Neural network modeling for estimation of scour depth around bridge piers. J Hydrodyn Ser B 19(3):378–386CrossRefGoogle Scholar
  5. 5.
    Kaya A (2010) Artificial neural network study of observed pattern of scour depth around bridge piers. Comput Geotech 37(3):413–418CrossRefGoogle Scholar
  6. 6.
    Uyumaz A, Altunkaynak A, Özger M (2006) Fuzzy logic model for equilibrium scour downstream of a dam’s vertical gate. J Hydraul Eng 132(10):1069–1075CrossRefGoogle Scholar
  7. 7.
    Wang YM, Elhag TM (2007) A fuzzy group decision making approach for bridge risk assessment. Comput Ind Eng 53(1):137–148CrossRefGoogle Scholar
  8. 8.
    Guven A, Azamathulla HM, Zakaria NA (2009) Linear genetic programming for prediction of circular pile scour. Ocean Eng 36(12):985–991CrossRefGoogle Scholar
  9. 9.
    Najafzadeh M, Barani GA, Kermani MRH (2013) GMDH based back propagation algorithm to predict abutment scour in cohesive soils. Ocean Eng 59:100–106CrossRefGoogle Scholar
  10. 10.
    Pal M, Singh NK, Tiwari NK (2012) M5 model tree for pier scour prediction using field dataset. KSCE J Civil Eng 16(6):1079–1084CrossRefGoogle Scholar
  11. 11.
    Goel A, Pal M (2009) Application of support vector machines in scour prediction on grade-control structures. Eng Appl Artif Intell 22(2):216–223CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Azamathulla HM (2012) Gene expression programming for prediction of scour depth downstream of sills. J Hydrol 460:156–159CrossRefGoogle Scholar
  14. 14.
    Basser H, Karami H, Shamshirband S, Akib S, Amirmojahedi M, Ahmad R, Javidnia H (2015) Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike. Appl Soft Comput 30:642–649CrossRefGoogle Scholar
  15. 15.
    Najafzadeh M, Barani GA (2011) Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers. Sci Iran 18(6):1207–1213CrossRefGoogle Scholar
  16. 16.
    Hasanipanah M, Noorian-Bidgoli M, Armaghani DJ, Khamesi H (2016) Feasibility of PSO–ANN model for predicting surface settlement caused by tunneling. Eng Comput 32(4):705–715CrossRefGoogle Scholar
  17. 17.
    Cus F, Balic J, Zuperl U (2009) Hybrid ANFIS-ants system based optimisation of turning parameters. J Achiev Mater Manuf Eng 36(1):79–86Google Scholar
  18. 18.
    Pankaj Goswami (2013) Evaluation of scour depth around bridge piers. Guwahati University, GuwahatiGoogle Scholar
  19. 19.
    Jang JS, Sun CT (1995) Neuro-fuzzy modeling and control. Proc IEEE 83(3):378–406CrossRefGoogle Scholar
  20. 20.
    Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRefGoogle Scholar
  21. 21.
    Catto JW, Linkens DA, Abbod MF, Chen M, Burton JL, Feeley KM, Hamdy FC (2003) Artificial intelligence in predicting bladder cancer outcome. Clin Cancer Res 9(11):4172–4177Google Scholar
  22. 22.
    Mahabir C, Hicks F, Fayek AR (2006) Neuro-fuzzy river ice breakup forecasting system. Cold Reg Sci Technol 46(2):100–112CrossRefGoogle Scholar
  23. 23.
    Wang YM, Elhag TM (2008) An adaptive neuro-fuzzy inference system for bridge risk assessment. Expert Syst Appl 34(4):3099–3106CrossRefGoogle Scholar
  24. 24.
    Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999CrossRefGoogle Scholar
  25. 25.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297zbMATHGoogle Scholar
  26. 26.
    Cheng KH, Hsu CF, Lin CM, Lee TT, Li C (2007) Fuzzy–neural sliding-mode control for DC–DC converters using asymmetric Gaussian membership functions. IEEE Trans Industr Electron 54(3):1528–1536CrossRefGoogle Scholar
  27. 27.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. 1995 IEEE Int Conf Neural Netw ICNN 95(4):1942–1948. CrossRefGoogle Scholar
  28. 28.
    Ghasemi H, Kolahdoozan M, Pena E, Ferreras J, Figuero A (2017) A new hybrid ANN model for evaluating the efficiency of π-type floating breakwater. Coast Eng Proc 1(35):25CrossRefGoogle Scholar
  29. 29.
    Harish N, Mandal S, Rao S, Patil SG (2015) Particle Swarm Optimization based support vector machine for damage level prediction of non-reshaped berm breakwater. Appl Soft Comput 27:313–321CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Applied Mechanics and HydraulicsNational Institute of Technology KarnatakaSurathkal, MangaloreIndia
  2. 2.Department of Civil Engineering (Formerly Chief Scientist in CSIR-NIO Goa)PES UniversityBangaloreIndia

Personalised recommendations