Prediction of the Ultimate Shear Load of Flat Slabs Using a Hybrid ANN-FEM Approach: Implementation and Accuracy Insights

This study presents a hybrid approach that combines the Finite Element Method (FEM) and Artificial Neural Networks (ANNs) to predict the ultimate punching shear capacity of flat slabs supported by interior columns. FEM models were first developed and validated against experimental results to simulate slab behavior. These models were then used to generate a numerical dataset containing 1000 entries with varying geometric and mechanical parameters. Based on this dataset, 600 ANN architectures were trained and evaluated, from which three were selected for their high predictive performance (R2 > 0.99, MAE < 8 kN). A sensitivity analysis on network hyperparameters and dataset size revealed that 500–750 samples are sufficient for accurate ANN training. Finally, the selected ANN–FEM models were tested against 20 experimental cases and compared to predictions from ACI 318, Eurocode 2, and NBR 6118. While the design codes showed significant underestimations (up to 95%), the ANN and FEM models resulted in mean percentage errors of 11.6% and 10.1%, respectively. These findings demonstrate that the proposed hybrid approach provides an accurate, efficient, and practical alternative for predicting punching shear capacity in structural design.