Conference Papers
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Item Conventional prediction vs beyond data range prediction of loss coefficient for quarter circle breakwater using ANFIS(Springer Verlag service@springer.de, 2015) Hegde, A.V.; Raju, B.Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and also to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwaters (EPQCB) are artificial concrete breakwaters consisting of a curved perforated face fronting the waves with a vertical wall on rear side and a base slab resting on a low rubble mound base. The perforated curved front face has advantages like energy dissipation and good stability with less material as it is hollow inside. The estimation of hydrodynamic performance characteristics of EPQCB by physical model studies is complex, expensive and time consuming. Hence, computational intelligence (CI) methods are adopted for the evaluation of the performance characteristics like reflection, dissipation, transmission, runup, rundown etc. A number of CI methods like Artificial Neural Network (ANN), Fuzzy logic, and hybrids such as ANFIS, ANN-PCO (particle swarm optimization), ANN-ACO etc., are available and are being used. The paper presents the work carried out to predict the dependent output variable of loss coefficient (Kl) beyond the range of values of one of the input variables i.e., wave period (T) adopted in present work, using the input data on variables of wave height (H), wave period (T), structure height (hs), water depth (d), radius of the breakwater (R), spacing of perforations (S) and diameter of perforations (D) using ANFIS. For this purpose, both the conventional method of data segregation and also a new method called ‘beyond data range’ method are used for both training the ANFIS models and also to predict the dependent variable. Further, the input data was fed to the models in both dimensional and nondimensional form in order to understand the effect of using non-dimensional data in place of dimensional parametric data. The performance of ANFIS models for all the four cases mentioned above was studied and it was found that prediction using conventional method with non-dimensional parameters performed better than other three methods. ANFIS models can be used to predict the performance characteristic Kl of EPQCB beyond the input data range of wave period T. © Springer International Publishing Switzerland 2015.Item Computational intelligence on hydrodynamic performance characteristics of Emerged Perforated Quarter Circle Breakwater(Elsevier Ltd, 2015) Raju, B.; Hegde, A.V.; Sekhar, O.Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwater (EPQCB) is an artificial concrete breakwater consisting of a curved perforated face fronting the waves, a vertical wall on back and a base slab resting on a low rubble mound base. The perforated curved front face is having advantages like energy dissipation and good stability with less material as it is hollow inside. Computational Intelligence (CI) can be adopted for the evaluation of performance characteristics like reflection, dissipation, run-up and rundown which are complex, time consuming and expensive to perform in laboratory. The paper presents the work carried out to predict the reflection coefficient (Kr) for input parameters, wave period (T) beyond the data range used for training and of wave height (H) along with the data on input parameters of water depth (d), spacing-perforation ratio (S/D) and radius (R) of the EPQCB. The data on various parameters are taken in two categories for training and testing of ANN as mentioned below in order to understand the effect of using non-dimensional data in place of parametric values: 1) Input in the form of parametric data (H, T, d, R, S, D), and 2) Input in the form of non-dimensional values (H/gT2, d/gT2, S/D, R/H). Better correlation was found when individual dimensional parametric data was used instead of non-dimensional group values in both the methods of prediction. Similarly, the correlation between the beyond the data range prediction and actual values was found to be good in both methods of prediction. © 2015 The Authors. Published by Elsevier Ltd.