Prediction of punching shear strength of slab-column connections: A comprehensive evaluation of machine learning and deep learning based approaches

Despite the complex punching shear behavior of reinforced concrete slabs have been comprehensively addressed in the literature, it is further essential to develop a universal design model comprising high accuracy and the simplicity for design practicability, adaptable to diverse conditions encountered in practice. Artificial intelligence applications, artificial neural networks (ANN), and more recently, various machine learning (ML) and deep learning (DL) techniques veer off in a new direction in structural engineering context with improved accuracy and efficiency. The paper begins with the assessment of the capabilities of various artificial intelligence applications in predicting the punching shear strength of slab-column connections without shear reinforcement through the extensive database using 650 punching shear experiments from the literature. Critical parameters influencing the punching shear strength as well as the precision of the current code provisions in predicting this feature were then thoroughly examined in the paper. The results shown in this paper validated the competency of artificial intelligence applications in predicting the punching shear strength of such connections with increased accuracy and improved simplicity in practical terms. The proposed models utilizing the artificial intelligence applications encourage the ultimate rehabilitation policies to be proposed and improved code provisions to be developed for contemporary structures.