Predictive Model for Load Carrying Capacity of Reinforced Concrete Beam-Column Joints Using Gene Expression Programming.

This study emphasizes the significance of beam-column joints (BCJs) within reinforced concrete (RC) structures and investigates their performance when subjected to seismic forces. Accurately predicting the load-carrying capacity of exterior BCJs under seismic loading poses a significant challenge. The development of a reliable and user-friendly predictive model is of paramount importance for facilitating cost-effective and safe design practices for RC structures. To address this requirement, we propose an artificial intelligence (AI)-based model that utilizes gene expression programming (GEP) to accurately predict the load-carrying capacity of exterior BCJs under monotonic loading conditions. The model is developed using GEP and utilizes a database of 128 joint load-carrying capacity results of exterior BCJs obtained from a validated finite element (FE) model using ABAQUS, which considers the effects of material and geometric factors, which have often been overlooked in prior studies. These factors encompass multiple aspects, including the beam and column dimensions, concrete material properties, longitudinal reinforcements in beams and columns, and axial loads applied to the columns. This study also compared the results of the proposed GEP model with the numerical data obtained from the validated FE model, demonstrating good accuracy and reliability. The proposed model has the potential to improve the accuracy and reliability of joint load-carrying capacity predictions, thereby aiding the design of safe and cost-effective RC structures.