Neural Computing and Applications

, Volume 23, Issue 7–8, pp 2107–2112 | Cite as

Group method of data handling to predict scour depth around bridge piers

Original Article

Abstract

In this study, group method of data handling network with quadratic polynomial was used to predict scour depth around bridge piers. Effective parameters on scour phenomena include sediment size, geometry of bridge pier, and upstream flow conditions. Different shapes of piers have been utilized to develop the GMDH network. Back propagation algorithm was performed to train the GHMD network which updated weighting coefficients of quadratic polynomial in each iteration of the training stage. The GMDH performed with the lowest errors of training and testing stages for cylindrical pier. Also, Richardson and Davis, Johnson’s equations produced relatively good performances for different types of piers. Finally, the results indicated that GMDH could be provided more accurate prediction than those obtained using traditional equations.

Keywords

GMDH Scour depth Bridge pier Traditional equations 

References

  1. 1.
    Amanifard N, Nariman-Zadeh N, Farahani MH, Khalkhali A (2008) Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks. J Energy Conserv Manag 49:2588–2594CrossRefGoogle Scholar
  2. 2.
    Alavi AH, Gandomi AH (2011) Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing. Comput Struct 89(23–24):2176–2194CrossRefGoogle Scholar
  3. 3.
    Azmathulla HMD, Deo MC, Deolalikar PB (2005) Neural networks for estimation of scour downstream of a ski-jump bucket. J Hydraul Eng ASCE 131(10):898–908CrossRefGoogle Scholar
  4. 4.
    Azamathulla HMD, Guven A, Demir YK (2011) Linear genetic programming to scour below submerged pipeline. Ocean Eng 38(8–9):995–1000CrossRefGoogle Scholar
  5. 5.
    Bateni SM, Borghei SM, Jeng D-S (2006) Neural network and neuro-fuzzy assessments for scour depth around bridge piers. Eng Appl Artif Intell 20:401–414CrossRefGoogle Scholar
  6. 6.
    Dey S, Bose SK, Sastry GLN (1995) Clear-water scour at circular piers. J Hydraul Eng 121(12):869–876CrossRefGoogle Scholar
  7. 7.
    Ettema R (1990) Design method for local scour at bridge pier. Proc J Hydraul Eng 116(10):1290–1292CrossRefGoogle Scholar
  8. 8.
    Farlow SJ (ed) (1984) Self-organizing method in modeling: GMDH type algorithm. Marcel Dekker Inc., NewyorkGoogle Scholar
  9. 9.
    Gandomi AH, Alavi AH (2011) Applications of computational intelligence in behavior simulation of concrete materials. In: XS Yang, S Koziel (eds) Chapter 9 in computational optimization and applications in engineering and industry, Springer SCI, 359, pp 221–243Google Scholar
  10. 10.
    Guven A, Gunal M (2008) Prediction of scour downstream of grade-control structures using neural networks. J Hydraul Eng ASCE 134(11):1656–1660CrossRefGoogle Scholar
  11. 11.
    Iba H, de Garis H (1996) Extending genetic programming with recombinative guidance. In: Angeline P, Kinnear K (eds) Advances in genetic programming 2. MIT Press, CambridgeGoogle Scholar
  12. 12.
    Ivahnenko AG (1971) Polynomial theory of complex systems, IEEE Transactions on Systems, Man, and CyberneticsGoogle Scholar
  13. 13.
    Ivakhnenko AG, Ivakhnenko GA (2000) Problems of further development of the group method of data handling algorithms. Part 1. Pattern Recogn Image Anal 110:187–194Google Scholar
  14. 14.
    Johnson PA (1992) Reliability-based pier scour engineering. J Hydraul Eng 118(10):1344–1357CrossRefGoogle Scholar
  15. 15.
    Kalantary F, Ardalan H, Nariman-Zadeh N (2009) An investigation on the Su -NSPT correlation using GMDH type neural networks and genetic algorithms. Eng Geol 104(1–2):144–155CrossRefGoogle Scholar
  16. 16.
    Kazeminezhad MH, Etemad-Shahidi A, Yeganeh Bakhtiary A (2010) An alternative approach for investigation of the wave-induced scour around pipelines. J Hydroinform 12(1):51–65CrossRefGoogle Scholar
  17. 17.
    Khan M, Azamathulla HMD, Tufail M, Ghani AA (2011) Bridge pier scour prediction by gene expression programming. Proceedings of the Institution of Civil Engineers, Water Management, Issue WM1, pp 1–13Google Scholar
  18. 18.
    Landers MN, Mueller DS (1999) U.S. Geological survey field measurements of pier scour. In: Compendium of papers on ASCE water resources engineering conferences 1991 to 1998, pp 585–607Google Scholar
  19. 19.
    Laursen EM, Toch A (1956) Scour around bridge piers and abutments. Iowa highway research board, Ames, IA, Bulletin 4Google Scholar
  20. 20.
    Mehrara M, Moeini A, Ahrari M, Erfanifard A (2009) Investigating the efficiency in oil futures market based on GMDH approach. Expert Syst Appl 36(4):7479–7483CrossRefGoogle Scholar
  21. 21.
    Melville BW, Sutherland AJ (1988) Design method for local scour abridge piers. J Hydraul Eng 114(10):1210–1226CrossRefGoogle Scholar
  22. 22.
    Mia F, Nago H (2003) Design method of time-dependent local scour at circular bridge pier. J Hydraul Eng 129(6):420–427CrossRefGoogle Scholar
  23. 23.
    Mohammed TH, Noor MJMM, Ghazali AH, Huat BBK (2005) Validation of some bridge pier scour formulae using field and laboratory data. Am J Environ Sci 1(2):119–125CrossRefGoogle Scholar
  24. 24.
    Muzzammil M, Ayyub M (2010) ANFIS-based approach for scour depth prediction at piers in non-uniform sediments. J Hydroinform 12(3):303–317CrossRefGoogle Scholar
  25. 25.
    Najafzadeh M, Barani Gh-A (2011) Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers. Sci Iran Trans A Civ Eng 18(6):1207–1213Google Scholar
  26. 26.
    Oh S-K, Pedrycz W (2006) Genetic optimization-driven multi-layer hybrid fuzzy neural networks. Simul Model Pract Theory 14:597–613CrossRefGoogle Scholar
  27. 27.
    Oh S-K, Pedrycz W, Park H-S (2005) Multi-layer hybrid fuzzy polynomial neural networks: a design in the framework of computational intelligence. Neurocomputing 64:397–431CrossRefGoogle Scholar
  28. 28.
    Onwubolu GC (2008) Design of hybrid differential evolution and group method in data handling networks for modeling and prediction. Inf Sci 178:3618–3634CrossRefGoogle Scholar
  29. 29.
    Richardson EV, Davis SR (2001) Evaluating scour at bridges, Hydraulic Engineering Circular No. 18 (HEC-18). US Department of Transportation, Federal HighwayGoogle Scholar
  30. 30.
    Sanchez E, Shibata T, Zadeh LA (1997) Genetic algorithms and fuzzy logic systems. World ScientificGoogle Scholar
  31. 31.
    Sakaguchi A, Yamamoto TA (2000) GMDH network using back propagation and its application to a controller design. J IEEE 4:2691–2697Google Scholar
  32. 32.
    Shen HW, Schneider VR, Karaki S (1969) Local scour around bridge piers. J Hyd Div 95(6):1919–1940Google Scholar
  33. 33.
    Sheppard DM, Odeh M, Glasser T (2004) Large scale clear-water local pier scour experiments. J Hydraul Eng 130(10):957–963CrossRefGoogle Scholar
  34. 34.
    Sheppard DM, Miller W (2006) Live-bed local pier scour experiments. J Hydraul Eng 132(7):635–642CrossRefGoogle Scholar
  35. 35.
    Srinivasan D (2008) Energy demand prediction using GMDH networks. Neuro-Computing 72(1–3):625–629Google Scholar
  36. 36.
    Witczak M, Korbicz J, Mrugalski M, Patton R (2006) A GMDH neural network-based approach to robust fault diagnosis: application to the DAMADICS benchmark problem. J Control Eng Pract 14(6):671–683CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Mohammad Najafzadeh
    • 1
  • Hazi Mohammad Azamathulla
    • 2
  1. 1.Department of Civil EngineeringShahid Bahonar UniversityKermanIran
  2. 2.River Engineering and Urban Drainage Research Centre (REDAC)Universiti Sains MalaysiaPenangMalaysia

Personalised recommendations