Faculty Publications
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Item Prediction of damage level of inner conventional rubble mound breakwater of tandem breakwater using swarm intelligence-based neural network (PSO-ANN) approach(Springer Verlag service@springer.de, 2019) Kuntoji, G.; Rao, S.; Rao, M.; Reddy, E.N.B.The conventional rubble mound breakwater is a coastal protective structure commonly used decades before which alone failed to withstand the deepwater wave and its energy, and suffered a catastrophic failure. Keeping in mind both the safe functioning of harbor and stability of the breakwater for the fast-growing economy of the country, different types of breakwaters are being developed to serve this purpose. Tandem breakwater is an innovative type of breakwater, which is a combination of main conventional rubble mound breakwater and submerged reef in front of it. One of the advantages of this breakwater is that most of the wave energy is dissipated and wave intensity is reduced by submerged reef and the smaller waves interact with main breakwater and ensure its stability. Experimental studies are laborious and time-consuming to conduct. Therefore, it is necessary to carry out the detailed study of tandem breakwater stability by making use of simple and alternate techniques using the experimental data. In the present study, an attempt is made to understand the suitability and applicability of PSO-ANN, a hybrid soft computing technique for predicting damage level of conventional rubble mound breakwater of tandem breakwater. Based on the experimental data available in Marine Structure Laboratory, NITK, Surathkal, India, soft computing models are developed. The performances of the models are evaluated using model performance indicators. Results obtained demonstrate that the proposed new approach can be used to predict the damage level of conventional rubble mound breakwater of tandem breakwater efficiently and accurately. © Springer Nature Singapore Pte Ltd. 2019Item Swarm intelligence-based support vector machine (PSO-SVM) approach in the prediction of scour depth around the bridge pier(Springer Verlag service@springer.de, 2019) Marulasiddappa, B.M.; Rao, M.; Mandal, S.The mechanism of scour around the bridge pier is a complex phenomenon, and it is very difficult to make a common method to predict or estimate the depth of scour hole. In this paper, a hybrid model is developed, combining support vector machine and particle swarm optimization (PSO-SVM) to predict scour depth around a bridge pier. The input parameters such as sediment size (d50), the velocity of flow (U), and time (t) are used in the study to predict the scour depth. The models are developed with RBF, polynomial, and linear kernel functions, and the performances are evaluated using different statistical parameters such as CC, RMSE, NSE, and NMB. The predicted results are compared with measured scour depth. The predicted scour depth reveals that PSO-SVM with RBF kernel function model is found to be reliable and efficient in predicting the scour depth around bridge piers. © Springer Nature Singapore Pte Ltd. 2019Item Application of an evolutionary technique (PSO–SVM) and ANFIS in clear-water scour depth prediction around bridge piers(Springer London, 2019) Marulasiddappa, B.M.; Rao, M.; Mandal, S.The mechanism of the local scour around bridge pier is so complicated that it is hard to predict the scour accurately using a traditional method frequently by considering all the governing variables and boundary conditions. The present study aims to investigate the application of different hybrid soft computing algorithms, such as particle swarm optimization (PSO)-tuned support vector machine (SVM) and a hybrid artificial neural network-based fuzzy inference system to predict the scour depth around different shapes of the pier using experimental data. The important independent input parameters used in developing the soft computing models are sediment particle size, a velocity of the flow and the time taken in the prediction of the scour depth around the bridge pier. Different pier shapes used in the present study are circular, round-nosed, rectangular and sharp-nosed piers. The accuracy and efficiency of the two hybrid models are analyzed and compared with reference to experimental results using model performance indices (MPI) such as correlation coefficient (CC), normalized root-mean-squared error (NRMSE), normalized mean bias (NMB) and Nash–Sutcliffe efficiency (NSE). The ANFIS model with Gbell membership and the PSO–SVM model with polynomial kernel function yield good results in terms of MPI. The performance of PSO–SVM with polynomial kernel function with CC of 0.949, NRMSE of 7.47, NMB of ? 0.009 and NSE of 0.90 reveals that the hybrid ANFIS model with Gbell membership function yields slightly better than that of the PSO–SVM model with CC of 0.950, NRMSE of 6.92, NMB of ? 0.002 and NSE of 0.91 for the optimum bridge pier with circular shape, whereas the performance of PSO–SVM model is better than that of ANFIS model for optimum bridge piers with rectangular and sharp nose shape. The PSO–SVM model can be adopted as accurate and efficient alternative approach in predicting scour depth of the pier. © 2018, The Natural Computing Applications Forum.Item Prediction of wave transmission over submerged reef of tandem breakwater using PSO-SVM and PSO-ANN techniques(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2020) Kuntoji, G.; Rao, M.; Rao, S.Protection of the damaged breakwater from the high-intensity wave action has become inevitable. Submerged reef can act as a protective structure in reducing the wave action. Further, placed the reef structures on the sea side of a conventional rubble mound breakwater will reduce the effects of wave action. The conventional breakwater and reef structure combination is a tandem breakwater. Keeping in mind the end goal to decrease the complexities associated in model scaling, time constraints and cost in conducting the experiments, an attempt is made to apply soft computing techniques such as an Artificial Neural Network (ANN) and Support Vector Machine (SVM) to model various problems of real case scenario, where mathematical modelling is also difficult. In the present study, Particle Swarm Optimization (PSO) optimizes various parameters of ANN and SVM model in predicting the wave transmission over a submerged reef of the tandem breakwater. The performance of proposed hybrid models such as PSO-ANN and PSO-SVM is evaluated using statistical indices. The results show that PSO-SVM tool performs better in predicting the wave transmission compared to PSO-ANN. © 2018, © 2018 Indian Society for Hydraulics.