Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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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.