Investigating the Synergistic Corrosion Protection Effect of an Alloy Element and Corrosion Inhibitor on Steel Reinforcement Using Machine Learning and Electrochemical Impedance Spectroscopy

Abstract

Steel reinforcement in marine concrete structures is vulnerable to chloride-induced corrosion, which compromises its structural integrity and durability. This study explores the combined effect of the alloying element Cr and the smart corrosion inhibitor LDH-NO2 on enhancing the corrosion resistance of steel reinforcement. Employing a machine learning approach with a support vector machine (SVM) algorithm, a predictive model was developed to estimate the polarization resistance of steel, considering Cr content, LDH-NO2 dosage, environmental pH, and chloride concentration. The model was rigorously trained and validated, demonstrating high accuracy, with a correlation coefficient exceeding 0.85. The findings reveal that the addition of Cr and application of LDH-NO2 synergistically improve corrosion resistance, with the model providing actionable insights for selecting effective corrosion protection methods in diverse concrete environments.

Keywords

• machine learning
• steel reinforcement
• corrosion
• LDH-NO₂
• Cr