Estimation of RC slab-column joints effective strength using neural networks

The nominal strength of slab-column joints made of high-strength concrete (HSC) columns and normal strength concrete (NSC) slabs is of great importance in structural design and construction of concrete buildings. This topic has been intensively studied during the last decades. Different types of column-slab joints have been investigated experimentally providing a basis for developing design provisions. However, available data does not cover all classes of concretes, rein-forcements, and possible loading cases for the proper calculation of joint stresses necessary for design purposes. New numerical methods based on modern software seem to be effective and may allow reliable prediction of column-slab joint strength. The current research is focused on analysis of available experimental data on different slab-to-column joints with the aim of predicting the nominal strength of slab-column joint. Neural networks technique is proposed herein using MATLAB routines developed to analyze available experimental data. The obtained results allow prediction of the effective strength of column-slab joints with accuracy and good correlation coefficients when compared to regression based models. The proposed method enables the user to predict the effective design of column-slab joints without the need for conservative safety coefficients generally promoted and used by most construction codes.