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PREDICTION OF ULTIMATE STRENGTH OF AXIALY LOADED REINFORCED CONCRETE SHORT COLUMNS USING ARTIFICIAL NEURAL NETWORKS | ||
| Basrah Journal for Engineering Science | ||
| Article 1, Volume 10, Issue 1, 2010, Pages 76-84 PDF (0 K) | ||
| Authors | ||
| Mustafa S. Zewalr; Nabeel A. Jasim | ||
| Abstract | ||
| The present study deals with the analysis of short reinforced concrete columns subjected Ic axial load. One of efficient techniques is applied, known as artiflcial neural networks. The descent gradient backpropagation algorithm is employed for analysis. The optimum topology (which gives least mean square error for both training and testing with 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 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. | ||
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