Abstract
ABSTRACT The empirical modeling methods are widely used in corrosion behavior analysis. But due to the limited regression ability of conventional algorithms, modeling objects are often limited to individual factors and specific environments. This study proposed a modeling method based on machine learning to simulate the marine atmospheric corrosion behavior of low-alloy steels. The correlations between material, environmental factors and corrosion rate were evaluated, and their influences on the corrosion behavior of steels were analyzed intuitively. By using the selected 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 set and testing set) to different low-alloy steel samples in several typical marine atmospheric environments. The results demonstrated that machine learning was efficient in corrosion behavior analysis, which usually involves a regression analysis of multiple factors.
Highlights
Low-alloy steels are steels containing Cu, Cr, Ni, Mn, Si and P elements with a total concentration of 5 wt.% or less [1]
Diaz et al [5] reported that the presence of high nickel (1–3 wt.%) contents in the steel raises the proportion of nanophase goethite in the inner rust layer, which increases its compactness and its corrosion resistance in moderate marine atmospheres
Soares et al [7] demonstrated that corrosion in marine atmosphere is primarily influenced by moisture and is accentuated by contaminants such as sodium chloride, and the corrosion rate was determined by the combined effects of different environmental factors
Summary
Low-alloy steels are steels containing Cu, Cr, Ni, Mn, Si and P elements with a total concentration of 5 wt.% or less [1]. To understand the corrosion behavior of low-alloy steel in the marine atmosphere, factors that may affect the corrosion resistance have been extensively explored [2,3]. Due to the effects of various environmental factors, the corrosion behavior of low-alloy steel in actual marine atmosphere environments is often very complicated [6]. Soares et al [7] demonstrated that corrosion in marine atmosphere is primarily influenced by moisture and is accentuated by contaminants such as sodium chloride, and the corrosion rate was determined by the combined effects of different environmental factors. Considering the variety of low-alloy steels and the complexity of marine atmosphere environments, a systematic study of the material and environmental factors requires a large number of experiments [8]. Advanced experimental design methods and data analysis techniques are necessary
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