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dc.contributor.authorPatil, S.G.
dc.contributor.authorMandal, S.
dc.contributor.authorHegde, A.V.
dc.contributor.authorMuruganandam, A.
dc.date.accessioned2011-06-16T07:01:14Z
dc.date.available2011-06-16T07:01:14Z
dc.date.issued2011
dc.identifier.citationIn "13th International Conference of the International Association for Computer Methods and Advances in Geomechanics. Vol. 1. Computer Methods for Geomechanics: Frontiers and New Applications. eds. by: Khalili, N.; Oeser, M.", Centre for Infrastructure Engineering and Safety; Sydney; Australia; 2011; 557-563
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/3868
dc.description.abstractSupport Vector Machine (SVM) works on structural risk minimization principle that has greater generalization ability and is superior to the empirical risk minimization principle as adopted in conventional neural network models. However, it is noticed that one particular model in isolation cannot capture all data patterns easily. In the present paper, a hybrid genetic algorithm tuned support vector machine regression (HGASVMR) model was developed to predict wave transmission of horizontally interlaced multilayer moored floating pipe breakwater (HIMMFPB). Furthermore, parameters of both linear and nonlinear SVM models are determined by Genetic Algorithm. HGASVMR model was trained on the dataset obtained from experimental wave transmission of HIMMFPB using regular wave fl ume at Marine Structure Laboratory, National Institute of Technology, Surathkal, India. The results are compared with artifi cial neural network (ANN) model in terms of Correlation Coeffi cient, Root Mean Square Error and Scatter Index. Performance of HGASVMR is found to be reliably superior.
dc.language.isoen
dc.publisherCentre for Infrastructure Engineering and Safety; Sydney; Australia
dc.rightsCopyright [2011]. 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.subjectSupport Vector Machine
dc.subjectneural network
dc.subjectgenetic algorithm
dc.subjectbreakwater
dc.titleHybrid genetic algorithm tuned support vector machine regression for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater
dc.typeConference Article


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