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Artificial neural network (ANN) models were developed to evaluate the effect of corrosion on the tensile mechanical properties of reinforcing B500C steel bars in coastal structures. This paper studies the effectiveness of radial basis function neural network models to predict the tensile mechanical properties degradation after several corrosion exposure times for bare reinforcing B500C steel bars of 8,10,12,16 and 18 mm nominal diameter. The input vector consisted of only two parameters, the nominal diameter and mass loss due to corrosion. This investigation shows that the established ANN models are available and effective in simulating the tensile mechanical behavior of corroded reinforcing B500C steel bars. For example, in the case of 16 mm diameter, the maximum prediction accuracy is 99.5% for yield strength and 99.74% for the tensile strength, respectively. [ABSTRACT FROM AUTHOR]