Evaluation and Screening of Atmospheric Corrosion Prediction Algorithms of Steels in Different Regions of China
Atmospheric corrosion of steels is a universal problem.Improving the prediction accuracy of atmospheric corrosion rate of steels in China is of great significance for setting corrosion margin,pre-venting corrosion failure and reducing the corrosion induced economic loss.For different type of steels in different regions of China,a prediction method of corrosion depth for steels based on machine learning al-gorithm was proposed,including data acquisition and processing,model training and testing,model eval-uation and screening,feature ranking and other steps.The applicability of different algorithms was evalu-ated and the optimal algorithm of the corrosion prediction for steels was selected.Firstly,the corrosion da-ta,environmental-and materials-features of 10 atmospheric exposure stations in China were collected.The corrosion depth of steels was predicted by using standard formulas and 6 machine learning algo-rithms.The grades of environmental corrosivity of atmospheric exposure stations were evaluated.Then,the prediction errors were analyzed,the accuracy of environmental corrosivity grade assessment was compared,and the prediction model suitable for steel corrosion was screened.Moreover,the sensitivity of materials-and environmental-features were analyzed,revealing the main factors of environments and materials affecting the atmospheric corrosion of steels.The results show that compared with the standard formula,the accuracy of the prediction models is greatly improved by RF and LSTM algorithms.In addi-tion to the terms such as temperature,humidity,sulfate-and chloride-deposition rates mentioned in stan-dard formulas,the acidity,alkalinity and rainwater corrosive ion concentration of rainwaters have a great impact on the corrosion of steels,which should be considered.
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