Predicting the Punching Shear Capacity of RC Slab-Column Connections with FRP Bars Using Machine Learning Based Algorithms

Abstract

Abstract In this study, two novel machine learning (ML) models, developed using Gene Expression Programming (GEP) and Multi Expression Programming (MEP) algorithms, are proposed for predicting the punching shear capacity of reinforced concrete (RC) slab-column connections with fiber reinforced polymers (FRP) as longitudinal bars. Using the GEP and MEP models, the values of statistical indicators obtained from the training dataset were very close to those values obtained from the testing dataset. In addition, a comparative study was conducted on experimental results and prediction results from the design codes, existing models in the literature and proposed ML models. The comparison revealed that the two models with the highest coefficient of determination (R2) and the lowest mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of variation (COV) values belong to the GEP and the MEP model. The results indicated that the proposed GEP and MEP models outperformed the other models in terms of prediction accuracy and robustness. Finally, sensitivity and parametric analyses were conducted.