Punching shear strength of column footings

Column footings find extensive application in practice for their economic advantages. However, the expressions found in building codes and literature to design these footings against punching shear (PS) do not incorporate the influence of all crucial parameters and provide imprecise strength estimation, because they were developed using the test results of flat slabs. This paper aimed to estimate the punching shear strength (PSS) of reinforced concrete column footings without shear reinforcements using two models developed by the artificial neural network (ANN) and the nonlinear regression methods, using a wide-ranging database that includes the test results of 215 column footings from the literature. The models account for the concrete strength, the longitudinal reinforcement ratio, the effective depth, the size of a column, and the shear span-depth ratio. The ANN model is consistent with the available database, with an R-value of 0.98. It is further applied to carry out a parametric study to investigate the effect of the main parameters on the PSS. The proposed design model is also compared well with the test results and is more consistent than the PS provisions of the current design codes.