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Particle swarm optimization based support vector machine for damage level prediction of non-reshaped berm breakwater

DRS at CSIR-National Institute of Oceanography

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Title Particle swarm optimization based support vector machine for damage level prediction of non-reshaped berm breakwater
 
Creator Harish, N.
Mandal, S.
Rao, S.
Patil, S.G.
 
Subject MARINE TECHNOLOGY
OFFSHORE AND COASTAL STRUCTURES
 
Description The damage analysis of coastal structure is very much essential for better and safe design of the structure. In the past, several researchers have carried out physical model studies on non-reshaped berm breakwaters, but failed to give a simple mathematical model to predict damage level for non-reshaped berm breakwaters by considering all the boundary conditions. This is due to the complexity and non-linearity associated with design parameters and damage level determination of non-reshaped berm breakwater. Soft computing tools like Artificial Neural Network, Fuzzy Logic, Support Vector Machine (SVM), etc, are successfully used to solve complex problems. In the present study, SVM and hybrid of Particle Swarm Optimization (PSO) with SVM (PSO–SVM) are developed to predict damage level of non-reshaped berm breakwaters. Optimal kernel parameters of PSO–SVM are determined by PSO algorithm. Both the models are trained on the data set obtained from experiments carried out in Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, India. Results of both models are compared in terms of statistical measures, such as correlation coefficient, root mean square error and scatter index. The PSO–SVM model with polynomial kernel function outperformed other SVM models
 
Date 2015-07-01T05:40:12Z
2015-07-01T05:40:12Z
2015
 
Type Journal Article
 
Identifier Applied Soft Computing, vol.27; 313-321p.
http://drs.nio.org/drs/handle/2264/4718
 
Language en
 
Rights An edited version of this paper was published by Elsevier. Copyright [2015] Elsevier
 
Publisher Elsevier