Faculty Publications
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Item Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater(2011) Patil, S.G.; Mandal, S.; Hegde, A.V.; Alavandar, S.The ocean wave system in nature is very complicated and physical model studies on floating breakwaters are expensive and time consuming. Till now, there has not been available a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. This is due to complexity and vagueness associated with many of the governing variables and their effects on the performance of breakwater. In the present paper, Adaptive Neuro-Fuzzy Inference System (ANFIS), an implementation of a representative fuzzy inference system using a back-propagation neural network-like structure, with limited mathematical representation of the system, is developed. An ANFIS is trained on the data set obtained from experimental wave transmission of horizontally interlaced multilayer moored floating pipe breakwater using regular wave flume at Marine Structure Laboratory, National Institute of Technology Karnataka, Surathkal, India. Computer simulations conducted on this data shows the effectiveness of the approach in terms of statistical measures, such as correlation coefficient, root-mean-square error and scatter index. Influence of input parameters is assessed using the principal component analysis. Also results of ANFIS models are compared with that of artificial neural network models. © 2010 Elsevier Ltd. All rights reserved.Item Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater(Elsevier Ltd, 2012) Patil, S.G.; Mandal, S.; Hegde, A.V.Planning and design of coastal protection works like floating pipe breakwater require information about the performance characteristics of the structure in reducing the wave energy. Several researchers have carried out analytical and numerical studies on floating breakwaters in the past but failed to give a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. Computational intelligence techniques, such as, Artificial Neural Networks (ANN), fuzzy logic, genetic programming and Support Vector Machine (SVM) are successfully used to solve complex problems. In the present paper, a hybrid Genetic Algorithm Tuned Support Vector Machine Regression (GA-SVMR) model is developed to predict wave transmission of horizontally interlaced multilayer moored floating pipe breakwater (HIMMFPB). Furthermore, optimal SVM and kernel parameters of GA-SVMR models are determined by genetic algorithm. The GA-SVMR model is trained on the data set obtained from experimental wave transmission of HIMMFPB using regular wave flume at Marine Structure Laboratory, National Institute of Technology, Karnataka, Surathkal, Mangalore, India. The results are compared with ANN and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in terms of correlation coefficient, root mean square error and scatter index. Performance of GA-SVMR is found to be reliably superior. b-spline kernel function performs better than other kernel functions for the given set of data. © 2011 Elsevier Ltd. All rights reserved.Item Particle Swarm Optimization based support vector machine for damage level prediction of non-reshaped berm breakwater(Elsevier Ltd, 2015) Narayana, N.; Mandal, S.; Rao, S.; Patil, S.G.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. © 2014 Elsevier B.V.