Machine learning-based corrosion rate prediction of steel embedded in soil
Zheng Dong,
Ling Ding,
Zhou Meng
et al.
Abstract:Predicting the corrosion rate for soil-buried steel is significant for assessing the service-life performance of structures in soil environments. However, due to the large amount of variables involved, existing corrosion prediction models have limited accuracy for complex soil environment. The present study employs three machine learning (ML) algorithms, i.e., random forest, support vector regression, and multilayer perception, to predict the corrosion current density of soil-buried steel. Steel specimens were… Show more
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