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Please use this identifier to cite or link to this item: http://drs.nio.org/drs/handle/2264/4109

Title: Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models
Authors: Mandal, S.
SubbaRao
Harish, N.
Lokesha
Citation: International Journal of Naval Architecture and Ocean Engineering, vol.4; 2012; 112-122
Issue Date: 2012
Publisher: The Society of Naval Architects of Korea
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.
Document type: Journal Article
Copyright: Copyright [2012]. All efforts have been made to respect the copyright to the best of our knowledge. Inadvertent omissions, if brought to our notice, stand for correction and withdrawal of document from this repository.
URI: http://drs.nio.org/drs/handle/2264/4109
Appears in Collections:Engineering & Instrumentation

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