Punching shear strength of waffle flat slabs

Abstract

This research aimed to compare the ultimate load of 10 waffle flat slabs with different sizes of solid area and spacing between ribs. For this, a non-linear computational simulation of the slabs was carried out until their failure using the engineering software ANSYS. The failure modes and loads were analyzed, and the results showed that the models with less solid area presented less bearing capacity in comparison to the models with greater solid area when the failure mode was shearing of the ribs. The slabs with the largest solid regions experienced punching shear and behaved in a similar way as solid flat slabs, indicating compliance with the codes in relation to their punching shear strength provisions, especially with the NBR 6118. The results show that a square solid area whose length is 15% of the span is reasonable and that the ACI, Eurocode 2 and NBR 6118 provisions underestimate the shear strength of the ribs.

The Machine-Learning-Based Prediction of the Punching Shear Capacity of Reinforced Concrete Flat Slabs: An Advanced M5P Model Tree Approach

“…In recent years, the application of black-box machine learning (ML) and artificial intelligence techniques has shown great promise in predicting complex structural behaviors [1,[10][11][12][13][14][15][16][17]. These data-driven methods can have the potential to improve the accuracy and reliability of punching shear capacity predictions by capturing intricate relationships among the influencing factors.…”

“…Key parameters were assessed, highlighting the importance of the flexural reinforcement. In the pursuit of predicting punching shear resistance in reinforced concrete slabs, machine learning models such as artificial neural networks, decision trees, random forests, and extreme gradient boosting made significant strides [17]. Their predictive accuracy surpassed traditional design code models, marking a remarkable advancement.…”