Walk-Through Corrosion Assessment of Slurry Pipeline Using Machine Learning
Abdou Khadir Dia,
Axel Gambou Bosca,
Nadia Ghazzali
Abstract:The study of pipeline corrosion is crucial to prevent economic losses, environmental degradation, and worker safety. In this study, several machine learning methods such as recursive feature elimination (RFE), principal component analysis (PCA), gradient boosting method (GBM), support vector machine (SVM), random forest (RF), K-nearest neighbors (KNN), and multilayer perceptron (MLP) were used to estimate the thickness loss of a slurry pipeline subjected to erosion corrosion. These different machine learning m… Show more
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