| Autor: |
Cheng Wen, Baitong Chen, Gongqi Lou, Nanchuan Wang, Yuwan Tian, Ningxia Yin |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Metals, Vol 14, Iss 8, p 865 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2075-4701 |
| DOI: |
10.3390/met14080865 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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