Neural network prediction of load capacity for eccentrically loaded reinforced concrete columns
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This paper presents neural networks prediction of load capacity for eccentrically loaded reinforced concrete (RC) columns. The direct modelling of the load capacity of RC columns by means of the finite element method presents several difficulties, mainly in geometry rep resentation and handling of several nonlinearities. Properly trained neural network can provide a useful surrogate model for such columns. The paper discusses architecture and training methods of the both mul ti-layer perceptron (MLP) and fuzzy weights neural networks (FWNN) for this application. It also presen ts the performance analysis of the networks trained on data from three independent databases available in the literature.
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