Faculty Publications
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Publications by NITK Faculty
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Item ANN Model for Joint Shear Strength of RC Interior Beam-Column Joint(Springer Science and Business Media Deutschland GmbH, 2022) Alagundi, S.; Palanisamy, T.In the present study ANN model is developed to anticipate the Joint shear strength of interior Beam-Column joints. As there are many factors and parameters that influence the joint strength, it is challenging to determine the joint shear strength of joint. The current research aims to predict the Joint shear strength of the Beam-Column joint with the help of Artificial Intelligence. ANN models have recently gained popularity in Civil and Structural Engineering and have solved many non liner engineering problems. In the present research, ANN Model is constructed and the model is trained, tested and validated. Performance of the ANN model is measured by statistical relations. Error analysis is carried out to find out the deviation from experimental values. As the mean square error is less and correlation is nearly 95–100%, it has been concluded that the Present ANN model can accurately predict the Joint shear strength. The proposed ANN model is compared with design equations proposed by design code and found out that the ANN model shows more stability and accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Numerical Modeling on Buckling Behavior of Structural Stiffened Panel(Springer Science and Business Media Deutschland GmbH, 2023) Alagundi, S.; Palanisamy, T.Stiffened panels are essential building elements in weight-sensitive structures. They have various applications in marine, aircraft, and other structures. Plate structures can undergo buckling when subjected to axial compression loads and then exhibit out of plane displacements. The present work aims to study the buckling behavior of the stiffened panel. The finite element model of the stiffened panel is developed, and buckling analysis is performed using ANSYS software. This model is validated with the published experimental work. Once the model is validated, total of 320 numbers of models of stiffened panels with varying plate thickness, stiffener height, stiffener thickness, and distance between stiffeners are modeled in ANSYS-2020, and buckling analysis is performed. An artificial neural network model is proposed to predict the buckling load of the stiffened panel. Neural network model is created in MATLAB software, and it is trained, tested, and validated, and its performance is checked by statistical relations like coefficient of correlation and mean square error. Proposed ANN model shows high accuracy in the prediction of buckling load. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Neural network prediction of joint shear strength of exterior beam-column joint(Elsevier Ltd, 2022) Alagundi, S.; Palanisamy, T.Beam-Column joints are the critical locations in the reinforced concrete structures as they experience a massive amount of deformations under earthquake. The shear failure of the beam column joint should be avoided for the safety of the structure. In the present study, prediction of joint shear capacity of exterior Beam-column joint is proposed using artificial neural network (ANN). Experimental investigations performed by different authors have been examined and used to prepare the data sets for training, testing and validating the neural network. Parameters responsible for the shear strength of the exterior Beam-Column Joints are identified and the artificial neural network model is proposed to predict the joint shear strength. Input parameters for the ANN model are width and depth of the joint, concrete compressive strength, length of beam, top and bottom longitudinal reinforcement in the beam, yield strength of longitudinal reinforcement in beam, ratio of beam to column depth, joint Shear reinforcement index, beam bar index and column load index. The performance of the neural network model is evaluated by the statistical relations like Coefficient of correlation, Root mean square error and Scatter index. The proposed model is compared with an empirical formula and different equations suggested by the design codes. The results show that the proposed neural network model can effectively predict the joint shear strength of the Exterior Beam-Column joint. © 2022 Institution of Structural Engineers