Predicting punching shear strength of ferrocement slabs using back-propagation neural network

Abstract EN

A back-propagation neural network (BPNN) model is developed to predict the punching shear strength of square fibrocement slabs.

The experimental data used for training and testing the neural network model, are collected from several sources.

They are arranged in a format of seven input parameters (the effective span, slab thickness, yield tensile strength of wire mesh, volume fraction of wire mesh, mortar compressive strength, width of square loaded area, boundary condition of the supported slabs) and one output parameter (punching shear strength).

A parametric study is carried out using BPNN to study the influence of each parameter affecting the punching shear strength of Ferro cement slabs.

A comparison with the experimental results and those from other existing empirical equations demonstrates that the predictions from BPNN are indeed better.

We conclude that the BPNN model may serve as a good tool for predicting the punching shear strength.