ANN AND STATISTICAL MODELLING TO PREDICT THE DEFLECTION OF CONTINUOUS REINFORCED CONCRETE DEEP BEAMS

Abstract

This comparative study investigates the adoption of artificial neural networks and statistical modelling in the prediction of the deflection under ultimate strength of continuous reinforced concrete deep beams. All experimental data collected from the literature covers a case of a continuous deep beam with two point loads acting symmetrically in each span. The data set consist of many input parameters cover the geometrical and material properties. The corresponding output value was the deflection under ultimate strength of the continuous deep beam. The model takes into account the effects of the effective depth, shear span-to-depth ratio, length of one span, section width, ratio of reinforcement, and compressive strength of concrete cubes. Training, validation and testing of the developed neural network have been achieved using a comprehensive database compiled from 75 continuous deep beam specimens. The results show high correlation through using ANN modeling with 99.13% and 97.27% for extended and original data set. This model was compared with the multi-linear model which was of 81.16% correlation coefficient. Both model reflect high correlation with observed data and proved that they can be used to predict the deflection of deep beam with high degree of confidence.