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Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning

Journal Article (2020)
Authors

Zibo Pei (University of Science and Technology Beijing)

D. Zhang (University of Science and Technology Beijing)

Yuanjie Zhi (Northwestern Polytechnical University)

Tao Yang (University of Science and Technology Beijing)

Lulu Jin (University of Science and Technology Beijing)

Dongmei Fu (University of Science and Technology Beijing)

Xuequn Cheng (University of Science and Technology Beijing)

H Terryn ((OLD) MSE-6, Vrije Universiteit Brussel)

Johannes M.C. Mol ((OLD) MSE-6)

Xiaogang Li (University of Science and Technology Beijing)

Research Group
(OLD) MSE-6
Copyright
© 2020 Zibo Pei, D. Zhang, Yuanjie Zhi, Tao Yang, Lulu Jin, Dongmei Fu, Xuequn Cheng, H.A. Terryn, J.M.C. Mol, Xiaogang Li
To reference this document use:
https://doi.org/10.1016/j.corsci.2020.108697
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Zibo Pei, D. Zhang, Yuanjie Zhi, Tao Yang, Lulu Jin, Dongmei Fu, Xuequn Cheng, H.A. Terryn, J.M.C. Mol, Xiaogang Li
Research Group
(OLD) MSE-6
Volume number
170
DOI:
https://doi.org/10.1016/j.corsci.2020.108697
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Abstract

The atmospheric corrosion of carbon steel was monitored by a Fe/Cu type galvanic corrosion sensor for 34 days. Using a random forest (RF)-based machine learning approach, the impacts of relative humidity, temperature and rainfall were identified to be higher than those of airborne particles, sulfur dioxide, nitrogen dioxide, carbon monoxide and ozone on the initial atmospheric corrosion. The RF model demonstrated higher accuracy than artificial neural network (ANN) and support vector regression (SVR) models in predicting instantaneous atmospheric corrosion. The model accuracy can be further improved after taking into consideration of the significant effect of rust formation on the sensor.