Faculty Publications
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Item The basic principle involved in the design of S-shaped breakwater is the provision of a wide berm at or around the water level with smaller size armor stones than that used in conventional design, which are allowed to reshape till an equilibrium slope is achieved. An attempt is made to assess the influence of wave height, wave period, and berm width on the stability of S-shaped breakwater with reduced (30% reduction in armor stone weight) armor unit weight. From the investigation, it is found that the berm breakwater with 30% reduced armor weight would be stable for the design wave height if the berm width is 60 cm and wave period 1.2 s. For higher wave periods studied, zero damage wave height reduces by 20-40% of the design wave height. Wave period has large influence on the stability of berm breakwaters. The runup increases with decrease in weight up to Wo/W=0.9. © 2004 Elsevier Ltd. All rights reserved.(Stability of berm breakwater with reduced armor stone weight) Rao, S.; Pramod, Ch.; Balakrishna Rao, K.B.2004Item Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models(Society of Naval Architects of Korea, 2012) Mandal, S.; Rao, S.; Narayana, N.; Lokesha, u.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. ©SNAK, 2012.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.