Reinforced concrete (RC) flat slabs are widely employed in modern construction, and accurately predicting their load-carrying capacity is crucial for ensuring safety and reliability. Existing design methods and empirical equations still exhibit discrepancies in determining the ultimate load capacity of flat slabs. This study aims to develop a robust machine learning model, specifically the M5P model tree, for predicting the punching shear capacity of a RC flat slab without shear reinforcement. A comprehensive dataset of 482 experimentally tested flat slabs without shear reinforcement was gathered through an extensive literature review and utilized for the development of the M5P model. The model takes into account influential parameters, such as slab thickness, longitudinal reinforcement ratios, and concrete strength. The performance of the proposed M5P model was compared with existing design codes and other empirical models. The comparison highlights that the developed M5P model tree provides a more accurate and reliable prediction of the punching shear capacity of RC flat slabs. This study contributes to the advancement of structural engineering knowledge and has the potential to improve the design and safety assessment of concrete flat slab structures.

This article presents a mathematical model developed using the M5P tree to predict the shear strength of steel-fiber-reinforced concrete (SFRC) for slender beams using soft computing techniques. This method is becoming increasingly popular for addressing complex technical problems. Other approaches, such as semi-empirical equations, can show known inaccuracies, and some soft computing methods may not produce predictive equations. The model was trained and tested using 332 samples from an experimental database found in the previous literature, and it takes into account independent variables such as the effective depth d, beam width b_w, longitudinal reinforcement ratio ρ, concrete compressive strength f_c, shear span to effective depth ratio a/d, and steel fiber factor F_sf. The predictive performance of the proposed M5P-based model was also compared with the one of existing models proposed in the previous literature. The evaluation revealed that the M5P-based model provided a more consistent and accurate prediction of the actual strength compared to the existing models, achieving an R² value of 0.969 and an RMSE value of 37.307 for the testing dataset. It was found to be a reliable and also straightforward model. The proposed model is likely to be highly helpful in assessing the shear capacity of SFRC beams during the pre-planning and pre-design stages and could also be useful to help for future revisions of design standards.