Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

摘要

The empirical modeling methods are widely used in corrosion behavior analysis. But due to thelimited regression ability of conventional algorithms, modeling objects are often limited toindividual factors and specific environments. This study proposed a modeling method basedon machine learning to simulate the marine atmospheric corrosion behavior of low-alloy steels.The correlations between material, environmental factors and corrosion rate were evaluated, andtheir influences on the corrosion behavior of steels were analyzed intuitively. By using theselected dominating factors as input variables, an optimized random forest model was established with a high prediction accuracy of corrosion rate (R2 values, 0.94 and 0.73 to the training setand testing set) to different low-alloy steel samples in several typical marine atmosphericenvironments. The results demonstrated that machine learning was efficient in corrosion behavior analysis, which usually involves a regression analysis of multiple factors.