Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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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 Multistep ahead groundwater level time-series forecasting using gaussian process regression and ANFIS(Springer Verlag service@springer.de, 2016) Naganna, N.S.; Deka, P.C.Groundwater level is regarded as an environmental indicator to quantify groundwater resources and their exploitation. In general, groundwater systems are characterized by complex and nonlinear features. Gaussian Process Regression (GPR) approach is employed in the present study to investigate its applicability in probabilistic forecasting of monthly groundwater level fluctuations at two shallow unconfined aquifers located in the Kumaradhara river basin near Sullia Taluk, India. A series of monthly groundwater level observations monitored during the period 2000–2013 is utilized for the simulation. Univariate time-series GPR and Adaptive Neuro Fuzzy Inference System (ANFIS) models are simulated and applied for multistep lead time forecasting of groundwater levels. Individual performance of the GPR and ANFIS models are comparatively evaluated using various statistical indices. In overall, simulation results reveal that GPR model provided reasonably accurate predictions than that of ANFIS during both training and testing phases. Thus, an effective GPR model is found to generate more precise probabilistic forecasts of groundwater levels. © Springer India 2016.Item Relative wave run-up parameter prediction of emerged semicircular breakwater(Springer Science and Business Media Deutschland GmbH, 2021) Kundapura, S.; Rao, S.; Arkal, V.H.Relative wave run-up parameter (Ru/Hi) on breakwaters is a vital component in fixing the elevation of the breakwater crest. In the present study, several soft computing methods has been employed to predict the wave run-up on the emerged seaside perforated semicircular breakwater for the prevailing Arabian sea wave climate, off Mangaluru coast in India. Unlike the mathematical modeling techniques, the soft computing tools have no complexity involved about understanding the nature of underlying process and prediction consumes less time when proper physical model data is available. The soft computing methods like artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), genetic algorithm based adaptive neuro fuzzy inference system (GA-ANFIS) and particle swarm based adaptive neuro fuzzy inference system (PSO-ANFIS) are the four models employed in the study. The ANN predicted well for the set architecture of (5-7-1). The ANFIS is used to predict the wave run-up on semicircular breakwater models using the hybrid efficiency of fuzzy logic and neural network. An initial FIS is generated for input variables by mapping the input-output data; the training is done using ANN; and the objective of GA and PSO is set to find the best FIS, reducing the root mean square error in the prediction of wave run-up. The most influencing input parameters (Hi/gT2, d/gT2, S/D, hs/d, R/Hi) are taken in non-dimensional form. The data required has been acquired from the physical model experiments conducted in the Marine structures laboratory of National Institute of Technology Karnataka (NITK), Surathkal, India. The GA-ANFIS prediction of wave run-up is found to be better than that of ANFIS prediction in terms of Correlation coefficient (R), Root mean square error (RMSE), Nash sutcliffe efficiency (NSE), Bias and Scatter index (SI). However, among the four models developed the ANN prediction outperformed the other three considered models with a higher R = 0.9467. © Springer Nature Singapore Pte Ltd 2021.Item Adaptive Neuro-Fuzzy Systems and Ensemble Methods in Joint Shear Prediction and Sensitivity Analysis(Springer Science and Business Media Deutschland GmbH, 2024) Palkar, S.S.; Palanisamy, T.In the absence of ductile design, beam-column joints form weak links in the frame during seismic activities, hence jeopardizing the entire structure. Deducing from the views of researchers, estimation of joint shear strength of RC beam-column joint is a necessity with a complexity. This complexity highlights the importance of machine learning models due to their data handling and predictive capabilities. This study used 233 beam-column joints with 132 exterior and 101 interior joints for training and testing the ensemble machine-learning models and an Adaptive neuro-fuzzy inference system. The performance indices of the models built and their comparison is carried out to find the optimum model to be deployed. The sensitivity analysis of the features considered was conducted to infer the differences in exterior and interior beam-column joints’ behavior. © 2023, Springer Science and Business Media Deutschland GmbH. All rights reserved.