Faculty Publications
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Publications by NITK Faculty
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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 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 Adaptive Neuro-Fuzzy Systems and Ensemble Methods in Joint Shear Prediction and Sensitivity Analysis(Springer Science and Business Media Deutschland GmbH, 2024) Palkar, S.S.; Palanisamy, T.In the absence of ductile design, beam-column joints form weak links in the frame during seismic activities, hence jeopardizing the entire structure. Deducing from the views of researchers, estimation of joint shear strength of RC beam-column joint is a necessity with a complexity. This complexity highlights the importance of machine learning models due to their data handling and predictive capabilities. This study used 233 beam-column joints with 132 exterior and 101 interior joints for training and testing the ensemble machine-learning models and an Adaptive neuro-fuzzy inference system. The performance indices of the models built and their comparison is carried out to find the optimum model to be deployed. The sensitivity analysis of the features considered was conducted to infer the differences in exterior and interior beam-column joints’ behavior. © 2023, Springer Science and Business Media Deutschland GmbH. All rights reserved.Item Predicting joint shear in beam–column connections using convolutional neural networks(Springer Science and Business Media B.V., 2025) Sidvilasini, S.; Palanisamy, T.Predicting joint shear at beam-column junctions (BCJ) is essential in structural engineering to ensure the safety and reliability of systems. Current methodologies using empirical calculations may rely on simplistic assumptions and insufficiently account for the many geometric factors and material properties that influence shear in BCJ. This research introduces a novel approach using Convolutional neural networks (CNNs) to predict joint shear. The collection comprises 515 joints, categorized into 210 exterior joints and 305 interior joints, characterized by 14 fundamental factors delineating their form and material properties. The predictive performance of the CNN model has been evaluated using known engineering codes, including ACI 318-19, NZS 3101:1-2006, IS 13920:2016, and several other data-driven models in the domain. Furthermore, it has been contrasted with an ensemble regression method. The study includes a thorough sensitivity analysis using a gradient-based method to determine the relative importance of input factors in predicting shear stress. The findings demonstrate the effectiveness of CNN in identifying complex relationships among joint parameters, thereby enabling precise predictions of joint shear. This method offers a promising alternative to traditional empirical formulas and enhances the understanding of structural behavior in BCJ. This study integrates contemporary machine learning algorithms with structural engineering concepts to optimize design processes and augment the safety and reliability of built environments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.