Faculty Publications
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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 Intelligent Modeling for Shear Strength of RC Exterior Beam-Column Joint Subjected to Seismic Loading(Springer Science and Business Media Deutschland GmbH, 2023) Swapnil, B.; Palanisamy, T.RC beam-column joints are subjected to impounding shear demand and bond-slip during the event of an earthquake. Accurate prediction of joint shear strength is necessary to avoid brittle shear failure in design and retrofitting procedures. In this study the accurate shear strength of RC exterior beam-column joints are predicted by providing a contemporary intelligent modeling approach through eXtreme Gradient Boosting regressor (XGBoost), an ensemble learning technique that combines several weak learners to generate a strong predictive model. From the experimental results of diverse publications on exterior beam-column joints, parameters affecting joint shear strength are found through examination of current models, and a vast database is constructed. Eleven such parameters that describe the material property, geometric configuration and bond resistance, are chosen as the inputs, and joint shear strength as the output. The model is then trained, tested and validated on this database. The performance of this model is evaluated by various regression evaluation metrics such as MSE, RMSE, and R2. Comparison of this model with the existing empirical equation, code provisions, and even with an individual ML algorithm, demonstrated its superiority over all the models in terms of accuracy and computation time. Sensitivity analysis done using predictive power score (PPS) showed that the most important parameter for the estimation of the shear strength of RC exterior beam-column joint is the percentage of beam longitudinal reinforcement. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.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