Evaluation of Hydrodynamic Performance of Quarter Circular Breakwater Using Soft Computing Techniques

  • N. RameshEmail author
  • A. V. Hegde
  • Subba Rao
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 23)


Breakwaters are massive structures constructed to provide the required tranquility within the ports. They are also used for safeguarding the beaches from eroding due to the severe action of waves, especially during inclement weather. In recent years, innovative structures such as Semi-circular and Quarter-circular Breakwaters (QBW) are being evolved to fulfill the ever-increasing demand from the coastal sector. QBW is a caisson with quarter circular surface towards incident waves, with horizontal bottom and a vertical wall on its rear side placed on a rubble mound foundation. In this paper, the experimental data collected at National Institute of Technology, Surathkal is used. The data collected is analysed by plotting the non-dimensional graphs of reflection coefficient, reflected wave height and incident wave height for various values of wave steepness. The values are used for prediction of QBW adopting Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. Goodness-of-Fit (GoF) test using Kolmogorov–Smirnov (KS) test statistic is applied for checking the adequacy of MLP and RBF networks to the experimental data. The performance of these networks is evaluated by using Model Performance Indicators (MPIs), viz. correlation coefficient, mean absolute error and model efficiency. The GoF test results and values of MPIs indicated the MLP is better suited amongst two networks adopted for evaluation of hydrodynamic performance of QBW.


Correlation coefficient Kolmogorov–Smirnov test Mean absolute error Model efficiency Multi-layer perceptron Quarter-circular breakwater Radial basis function 



The authors are grateful to Dr. (Mrs.) V. V. Bhosekar, Additional Director and Director In-charge, Central Water and Power Research Station, Pune, for providing research facilities to carry out the study. The authors are thankful to National Institute of Technology, Surathkal, for the supply of experimental data used in the study.


  1. 1.
    Amr HE, El-Shafie A, Hasan GE, Shehata A, Taha MR (2011) Artificial neural network technique for rainfall forecasting applied to Alexandria. Int J Phys Sci 6(6):1306–1316Google Scholar
  2. 2.
    Balakrishna K, Hegde AV (2015) Reflection and dissipation characteristics of non-overtopping quarter circle breakwater with low-mound rubble base. J Adv Res Ocean Eng 1(1):44–054CrossRefGoogle Scholar
  3. 3.
    Binumol S, Rao S, Hegde AV (2015) Runup and rundown characteristics of an emerged seaside perforated quarter circle breakwater. Aquat Procedia 4(1):234–239CrossRefGoogle Scholar
  4. 4.
    Chen J, Adams BJ (2006) Integration of artificial neural networks with conceptual models in rainfall-runoff modelling. J Hydrol 318(1–4):232–249CrossRefGoogle Scholar
  5. 5.
    D’Agostino BR, Stephens AM (1986) Goodness-of-fit techniques. M/s Marcel Dekkar Inc., New York 10016, USAGoogle Scholar
  6. 6.
    Deshpandey RR (2012) On the rainfall time series prediction using multilayer perceptron artificial neural network. Int J Emerg Technol Adv Eng 2(1):2250–2459Google Scholar
  7. 7.
    Hafeeda V, Binumol S, Hegde AV, Rao S (2014) Wave reflection by emerged sea side perforated quarter circle breakwater. Int J Earth Sci Eng 7(2):454–460Google Scholar
  8. 8.
    Hegde AV, Ravikiran L (2013) Wave structure interaction for submerged quarter circle breakwaters of different radii-reflection characteristics. World Acad Sci Eng Technol 7(7):1367–1371Google Scholar
  9. 9.
    Jiang XL, Gu HB, Li YB (2008) Numerical simulation on hydraulic performances of quarter circular breakwater. China Ocean Eng 22(4):585–594Google Scholar
  10. 10.
    Kaltech M (2008) Rainfall-runoff modelling using artificial neural networks: modelling and understanding. J Environ Sci 6(1):53–58Google Scholar
  11. 11.
    Karthik S, Rao S (2017) Application of soft computing in breakwater studies—a review. Int J Innov Res Sci Eng Technol 6(5):8355–8359Google Scholar
  12. 12.
    Shao LM (2003) Separation of incident waves and reflected waves and study of reflection coefficient. Dalian University of Technology Press, Dalian (in Chinese)Google Scholar
  13. 13.
    Shi YJ, Wu Mi-Ling, Xue-Lian Jiang, Yan-bao Li (2011) Experimental research on reflection and transmitting performance of quarter circle breakwater under regular and irregular waves. China Ocean Eng 25(3):469–478CrossRefGoogle Scholar
  14. 14.
    Tokar S, Markus M (2000) Precipitation runoff modelling using artificial neural network and conceptual models. J Hydrol Eng 5(2):156–161CrossRefGoogle Scholar
  15. 15.
    Xie SL, Li YB, Wu YQ, Gu HB (2006) Preliminary research on wave forces on quarter circular breakwater. Ocean Eng 24(1):14–18Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National Institute of Technology Karnataka, SurathkalMangaloreIndia

Personalised recommendations