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.

中文翻译：

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基于机器学习方法的海洋环境中低合金钢腐蚀速率预测及影响因素评价

摘要 经验建模方法广泛用于腐蚀行为分析。但由于传统算法的回归能力有限，建模对象往往受限于个体因素和特定环境。本研究提出了一种基于机器学习的建模方法来模拟低合金钢的海洋大气腐蚀行为。评估了材料、环境因素与腐蚀速率之间的相关性，直观地分析了它们对钢腐蚀行为的影响。以选定的主导因素为输入变量，建立了腐蚀速率预测精度高（R2值分别为0.94和0.94）的优化随机森林模型。73 到训练集和测试集）到几种典型海洋大气环境中的不同低合金钢样品。结果表明，机器学习在腐蚀行为分析中是有效的，腐蚀行为分析通常涉及多个因素的回归分析。