Predicting Punching Shear Strength of RC Interior Flat Slabs Using an Attention-Based Transformer and Differential Evolution

Punching shear strength (PSS) prediction in reinforced concrete (RC) interior flat slabs remains challenging for conventional empirical methods. Thus, this study proposes a hybrid attention-based transformer model optimized via differential evolution to address this limitation. The methodology combines multi-head attention mechanisms to capture nonlinear interactions among critical parameters (support dimensions, slab thickness, concrete strength, rate of steel bars, and steel yield strength). The model is trained and tested using 417 experimental results of flat slabs and processes eight input features encompassing geometric properties. The experimental results show superior accuracy, achieving a mean squared error (MSE) of 0.00017, a root mean squared error (RMSE) of 0.0113, and an R-squared of 0.998. The proposed model is benchmarked with well-known machine learning (ML) models and achieves a superior performance. These results emphasize the model’s potential as a scalable and precise tool for predicting PSS in RC flat slabs.