文献类型：期刊文献

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

作者：Wen, Cheng[1,2];Chen, Baitong[1];Lou, Gongqi[1];Wang, Nanchuan[1];Tian, Yuwan[1,2];Yin, Ningxia[1]

机构：[1]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Guangdong Prov Ocean Equipment & Mfg Engn Technol, Zhanjiang 524088, Peoples R China

年份：2024

卷号：14

期号：8

外文期刊名：METALS

收录：SCI-EXPANDED(收录号：WOS:001306708000001)、、Scopus(收录号：2-s2.0-85202615291)、WOS

基金：This research was funded by the Natural Science Foundation of China (No. 52203293), the Natural Science Foundation of Guangdong, China (No. 2021A1515110382), and the Program for Innovation Team of Guangdong Ocean University (CXTD2024008).

语种：英文

外文关键词：machine learning; steel reinforcement; corrosion; LDH-NO2; Cr

外文摘要：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.