Predicting Punching Shear Strength of Ferrocement Slabs Using Back-Propagation Neural Network

Abstract

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