Intelligent Modeling for Shear Strength of RC Exterior Beam-Column Joint Subjected to Seismic Loading

Abstract

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.