Machine learning-based corrosion rate prediction of steel embedded in soil-Reference-Cited by-同舟云学术

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Machine learning-based corrosion rate prediction of steel embedded in soil

Published:2024-08-06 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Dong Zheng,Ding Ling,Meng Zhou,Xu Ke,Mao Yongqi,Chen Xiangxiang,Ye Hailong,Poursaee Amir

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

US Department of Energy

Wisconsin Department of Transportation

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-68562-w.pdf

Reference71 articles.

1. Wasim, M., Shoaib, S., Mubarak, N. M. & Inamuddin, A. M. A. Factors influencing corrosion of metal pipes in soils. Environ. Chem. Lett. 16, 861–879. https://doi.org/10.1007/S10311-018-0731-X (2018).

2. Ding, L. & Poursaee, A. The impact of temperature, chlorides and sulfate-reducing bacteria on the corrosion of steel in soil. Environ. Geotechn. 572, 1–5. https://doi.org/10.1680/jenge.20.00021 (2021).

3. Azoor, R. M., Deo, R. N., Birbilis, N. & Kodikara, J. On the optimum soil moisture for underground corrosion in different soil types. Corros. Sci. 159, 108116. https://doi.org/10.1016/j.corsci.2019.108116 (2019).

4. Cai, Z. et al. Prediction of interference current of buried pipeline and study on corrosion of Q235A steel. Constr. Build. Mater 400, 132739. https://doi.org/10.1016/J.CONBUILDMAT.2023.132739 (2023).

5. Song, Y., Jiang, G., Chen, Y., Zhao, P. & Tian, Y. Effects of chloride ions on corrosion of ductile iron and carbon steel in soil environments. Sci. Rep. 7, 1–13. https://doi.org/10.1038/s41598-017-07245-1 (2017).

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