Browsing by Author "Marulasiddappa, B.M."

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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.
Bond strength characteristics of masonry using hemp fibre and chicken mesh reinforced mortar
(Elsevier Ltd, 2023) Mahesh, J.V.; Ramya, S.; Marulasiddappa, B.M.; Raveesh, R.M.
The bonding between the masonry unit and mortar plays very important role in strength of masonry. To improve the bond strength, we need to concentrate on the mortar properties. The present study focusses on the bond strength of masonry by adding reinforcement for the mortar. The hemp fibre and chicken mesh are used as a reinforcement for the mortar. For the test masonry triplets are used with mortar of proportion 1:4. The optimum dosage for hemp fibre is found to be 2% and chicken mesh is added along with mortar joints. The bond strength was tested in universal testing machine and the results have compared each other. From the study it was found that, the bond strength of hemp fibre reinforced triplets is found to be 27% higher than the chicken mesh reinforcement and 73% higher than the unreinforced mortar masonry. © 2023
Effect of vehicular vibrations on L-4 lumbar vertebrae – A finite element study
(Reed Elsevier India Pvt. Ltd., 2025) Kishore, Y.S.; Marulasiddappa, B.M.; Manoj, A.; Raveesh, R.M.; Rakesh, B.; Bhaskar, S.; Kuntoji, G.; Chethan, B.A.
Lower Back Pain (LBP) is a global health issue, with increasing prevalence, partly attributed to vehicular vibrations experienced by motorcyclists. The L4 lumbar vertebra is responsible for greater mobility and flexibility of the body, but also is the most crucial body element affected by vehicular vibrations. Anthropometric properties, types of speed humps, and vehicle types are the critical variables that impact bone health during riding, need to be studied. To understand the potential zones of injury, computational simulation can be performed under the influence of vehicle vibrations while crossing different types of speed humps at varying speeds. In the present study, finite element method (FEM) is used to evaluate stress and deformation in the bone. The L4 cortical bone is modelled by considering the CT-Scan data and assumed to be homogeneous and isotropic material. Vibration data is collected using two vehicle types (Type I and Type II) on four different humps (Trapezoidal, Bitumen Semi-circular, Rubber Semi-circular, and Rumble strip). The bone's dynamic behavior is studied using FEM simulation, which involved static structural, modal and transient dynamic analyses. The findings from static analysis indicate that the most concentrated stress is located in the lower pedicle region and is an expected commonplace for injuries because of vibrations. In transient dynamic analysis, Type I vehicle showed a 25 % higher stress than Type II. © 2024 Professor P K Surendran Memorial Education Foundation
PSO-SVM approach in the prediction of scour depth around different shapes of bridge pier in live bed scour condition
(Springer Verlag service@springer.de, 2019) Marulasiddappa, B.M.; Kuntoji, G.; Rao, M.; Mandal, S.
Scour is one of the major factors which affects directly on the durability and safety of the Bridge abutments. Based on the experimental data of Goswami in 2012, an effort is made to predict local scour by using a hybrid approach of Swarm Intelligence based algorithms which is today one of the powerful tools of optimization techniques. In this work, an intelligent model based on support vector machine in combination with the particle swarm optimization (PSO-SVM) technique is developed. The PSO-SVM models are developed with RBF, Polynomial and Linear kernel functions. The circular, rectangular, round-nosed, and sharp-nosed shapes of piers are considered in live bed scour condition. The scour depth around bridge piers is predicted by considering Sediment size, flow velocity, and time of flow as input parameters. Prediction accuracy of the models is evaluated using the model performance indicators such as Root Mean Square Error (RMSE, Correlation Coefficient (CC), Nash Succlift Error (NSE), etc. The results obtained from the model are compared with the measured scour depth to validate the reliability of the hybrid model. Based on the results, PSO based SVM model is found to be successful, reliable, and efficient in predicting the scour depth around the bridge pier. Â© Springer Nature Singapore Pte Ltd. 2019.
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. 2019