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.
Highlights
Uruguay takes part of the collaborative project operating four atmospheric corrosiveness stations
Amongst meteorological ones we include hourly values of relative humidity, temperature, wind speed and direction, as well as daily precipitation and precipitation run in number of days
The task has shown to be difficult, because of nonlinearity's associated with the physicochemical process responsible for the atmospheric corrosion phenomena
Summary
Uruguay takes part of the collaborative project operating four atmospheric corrosiveness stations. Most of the predictive models used to date are linear regression models that fit the data such that the root mean square error is minimized. They have been shown to be effective only in few areas. This paper tackles the modeling of corrosion penetration in terms of standard meteorological variables for low carbon steel alloy. Given a set of n observations at time i of m meteorological variables X¿ and the corresponding observed corrosion penetration values /i, I = 1 ... / (X, P) is the function providing the corrosion penetration of carbon steel, while X is a vector of cummulated meteorological variables (in our case, TDH, CI, SO2, P, % HR less than 40 % ). The specimens were exposed in each test sites corresponding major climatological parameters described in table I
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