首页|Data on Machine Learning Discussed by a Researcher at Xiangtan University (Construction and optimization of corrosion map in a broad region of acidic soil via machine learning)
Data on Machine Learning Discussed by a Researcher at Xiangtan University (Construction and optimization of corrosion map in a broad region of acidic soil via machine learning)
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New research on artificial intelligence is the subject of a new report. According to news reporting from Xiangtan, People’s Republic of China, by NewsRx journalists, research stated, “Machine learning has been widely applied to exploring the key affecting factors for metal corrosion in some local regions.” The news reporters obtained a quote from the research from Xiangtan University: “However, there is a lack of systemic research and practicable prediction model for the 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 the 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 HCO3concentration was higher than that of other factors.” According to the news editors, the research concluded: “The optimization model based on the ordinary least squares could be used for the optimization of the corrosion map of steels a broad region of acidic soils. The optimized corrosion map was a good alternative of the estimation methods for the corrosion rate of steels in soil.”
Xiangtan UniversityXiangtanPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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