Low carbon steel corrosion damage prediction in rural and urban environments

Abstract

This paper presents an Artificial Neural Network (ANN) model for the damage function of carbon steel, expressed in μm of corrosion penetration as a function of environmental variables. Working in the context of the Iberoamerican Atmospheric Corrosion Map Project, the experimental data comes as result of the corrosion of low alloy steel subtracts in three test sites in Uruguay, South America. In addition, we included experimental values obtained from short time kinetics studies, corresponding to special series from one of the sites. The ANN numerical model shows attractive results regarding goodness of fit and residual distributions. It achieves a RMSE value of 0.5 μm while a classical regression model lies in the range of 4.1 μm. Furthermore, a properly adjusted ANN model can be useful in the prediction of corrosion damage under different climatological and pollution conditions, while linear models cannot.