Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

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dc.contributor.author Mandal, S.
dc.contributor.author SubbaRao
dc.contributor.author Harish, N.
dc.contributor.author Lokesha
dc.date.accessioned 2012-07-27T11:52:52Z
dc.date.available 2012-07-27T11:52:52Z
dc.date.issued 2012
dc.identifier.citation International Journal of Naval Architecture and Ocean Engineering, vol.4; 2012; 112-122
dc.identifier.uri http://drs.nio.org/drs/handle/2264/4109
dc.description.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.
dc.language.iso en
dc.publisher The Society of Naval Architects of Korea
dc.rights 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.
dc.subject berms
dc.subject breakwaters
dc.subject damage
dc.title Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models
dc.type Journal Article


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