Faculty Publications
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Item Predicting the Axial Load Carrying Capacity of Columns Reinforced with GFRP Rebars Using ANN Modelling(Springer Science and Business Media Deutschland GmbH, 2023) Sumesh Manohar, G.; Palanisamy, T.In recent years most of the concrete structures are getting exposed to environments that are resulting in the corrosion of steel. To eliminate this, studies have been carried out to replace steel in RCC by Glass Fiber Reinforced Polymer (GFRP) rebars. In this paper, several experimental results were considered and the impacts of substituting steel by GFRP rebars were studied. Parameters affecting the load-carrying capacity of columns reinforced with GFRP rebars were identified from various literature and a database has been created. Twelve such parameters describing the material property and geometric configuration are chosen as inputs and the axial load carrying capacity as an output. An ANN model is developed with optimized architecture for predicting the compressive strength of columns reinforced with GFRP rebars. The model is then trained, tested, and validated on this database. The accuracy of the ANN model is evaluated by various regression evaluation metrics such as MSE, RMSE and R2. Comparison with the existing empirical equations and code provisions showed that the ANN model outperformed all these models. For the purpose of determining the efficiency of ANN model, a subset of the experimental data collected from work done on GFRP reinforced columns is used. Sensitivity analysis is carried out and the results showed that the most important parameters for the estimation of the strength of GFRP reinforced columns are the geometrical dimensions of the column. The results obtained showed that the ANN model is in good agreement with the experimental results. © 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