Artificial neural network modeling of atmospheric corrosion in the MICAT project

Abstract

This paper presents an Artificial Neural Network(ANN)-based solution methodology for modeling atmospheric corrosion processes from observed experimental values, and an ANN model developed using the cited methodology for the prediction of the corrosion rate of carbon steel in the context of the Iberoamerican Corrosion Map (MICAT) Project, which includes seventy-two test sites in fourteen countries throughout Iberoamerica. The ANN model exhibited superior performance in terms of goodness of fit (sum of square errors) and residual distributions when compared against a classical regression model also developed in the context of this study, and is expected to provide reasonable corrosion rates for a variety of climatological and pollution conditions. Furthermore, the proposed methodology holds promise to be an effective and efficient tool for the construction of analytical models associated with corrosion processes of other metals in the context of the MICAT project, and, in general, in the modeling of corrosion phenomena from experimental data.