International Journal of Naval Architecture and Ocean Engineering

The Society of Naval Architects of Korea (대한조선학회)
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Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

  • Mandal, Sukomal (Ocean Engineering Division, National Institute of Oceanography) ;
  • Rao, Subba (Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka) ;
  • N., Harish (Ocean Engineering Division, National Institute of Oceanography) ;
  • Lokesha, Lokesha (Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka)
  • Published : 2012.06.30
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Abstract

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

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References

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