Precorroded steel A-106 B specimens were prepared at different surface roughness. These specimens were immersed in corrosive ferric chloride solution in different concentrations (1.5, 3.0, 4.5, 6.0% wt.) at specified durations to initiate primarily the pitting corrosion. The corrosion pits distribution depend on the corrosive concentration, degree of surface roughness, and immersion duration.The pits were characterized using metallurgical microscope. Also, The pitting characteristics aimed to be predicted by "Artificial Neural Networks" (ANNs). The results obtained of pit quantification by ANNs predictions are shown to be agreed well against experimental values. i.e. R2=0.9839

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The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be quite effective; the results were validated by the experimental agreement with those acquired from laboratory tests. Specifically, the correlation coefficient, R = 0.9944.