PREDICTION OF ULTIMATE STRENGTH OF AXIALY LOADED REINFORCED CONCRETE SHORT COLUMNS USING ARTIFICIAL NEURAL NETWORKS

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  Nabeel A. Jasim   |      Mustafa S. Zewair   |   

The present study deals with the analysis of short reinforced concrete columns subjected to axial load. One of the efficient techniques is applied, known as artificial neural networks. The descent gradient backpropagation algorithm is employed for analysis. The optimum topology (which gives the least mean square error for both training and testing with a fewer number of epochs) is presented. The effects of the number of nodes in input and hidden layer(s), and selecting of learning rate and momentum coefficient, on the behaviour of neural network have been investigated. Due to the slow convergence of results when using descent gradient backpropagation, the faster algorithm called "resilient backpropagation algorithm" has been used to improve the performance of the neural network and the results have been compared with those obtained using the descent gradient backpropagation algorithm.