Machine learning has been widely applied to exploring the key affecting factors for metal corrosion in some local regions. However, there is a lack of systemic research and a practicable prediction model for metal corrosion in a broad region. In this paper, the corrosion map of Q235 steel in a broad region of acidic soils of Hunan province of Central China was constructed and optimized via field experiment and machine learning. Both the experimental and optimized corrosion maps confirmed that the corrosion rate of the steel decreased from the western to the eastern part of the province. The concentrations of pH, F−, Cl−, NO3−, HCO3−, K+, and Mg2+ were the key affecting factors in the broad region of acidic soils of the province. Among them, the contribution rate of the HCO3− concentration was higher than that of other factors. The optimization model based on the ordinary least squares could be used for the optimization of the corrosion map of steels in a broad region of acidic soils. The optimized corrosion map was a good alternative to the estimation methods for the corrosion rate of steel in soil.
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1 April 2024
Research Article|
January 24 2024
Construction and Optimization of Corrosion Map in a Broad Region of Acidic Soil via Machine Learning
Hui Su;
Hui Su
*School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105, China.
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Jun Wang;
Jun Wang
**Electric Power Research Institute, State Grid Hunan Electric Power Company, Changsha 410007, China.
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Yuxing Zeng;
Yuxing Zeng
***Climate Center of Hunan Province, Changsha 410118, China.
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Chenmeng Dang;
Chenmeng Dang
*School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105, China.
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Yi Xie;
Yi Xie
**Electric Power Research Institute, State Grid Hunan Electric Power Company, Changsha 410007, China.
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Song Xu;
Song Xu
**Electric Power Research Institute, State Grid Hunan Electric Power Company, Changsha 410007, China.
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Yongli Huang;
Yongli Huang
*School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105, China.
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Zhi Li;
Zhi Li
*School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105, China.
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Tangqing Wu
Tangqing Wu
‡
*School of Materials Science and Engineering, Xiangtan University, Xiangtan 411105, China.
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CORROSION (2024) 80 (4): 384–394.
Citation
Hui Su, Jun Wang, Yuxing Zeng, Chenmeng Dang, Yi Xie, Song Xu, Yongli Huang, Zhi Li, Tangqing Wu; Construction and Optimization of Corrosion Map in a Broad Region of Acidic Soil via Machine Learning. CORROSION 1 April 2024; 80 (4): 384–394. doi: https://doi.org/10.5006/4498
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