Abstract
In this work, a generalized regression neural network (GRNN) model is used to predict the corrosion potential values and corrosion current densities of ASTM A572-50 steel specimens embedded in nine soils with different physiochemical properties, i.e., pH, moisture content, resistivity, chloride content, sulfate and sulfite contents, and mean total organic carbon concentration. Experiments were conducted, and the corrosion current densities and corrosion potential values of the steel specimens embedded in the different soils were measured. The results obtained with the GRNN model agreed very well with the results of the experiments, suggesting that the proposed model is capable of predicting the corrosion activity of steel specimens embedded in different soils.
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