Long-term corrosion monitoring of carbon steels and environmental correlation analysis via the random forest method

Qing Li; Xiaojian Xia; Zibo Pei; Xuequn Cheng; Dawei Zhang; Kui Xiao; Jun Wu; Xiaogang Li

doi:10.1038/s41529-021-00211-3

Long-term corrosion monitoring of carbon steels and environmental correlation analysis via the random forest method

Qing Li
, Xiaojian Xia
, Zibo Pei
, Xuequn Cheng^*
, Dawei Zhang
, Kui Xiao
, Jun Wu
, Xiaogang Li

^*此作品的通讯作者

University of Science and Technology Beijing
Electric Power Research Institute of State Grid Fujian Electric Power Company Limited
China Academy of Machinery Science and Technology

科研成果: 期刊稿件 › 文章 › 同行评审

102 引用（Scopus）

摘要

In this work, the atmospheric corrosion of carbon steels was monitored at six different sites (and hence, atmospheric conditions) using Fe/Cu-type atmospheric corrosion monitoring technology over a period of 12 months. After analyzing over 3 million data points, the sensor data were interpretable as the instantaneous corrosion rate, and the atmospheric “corrosivity” for each exposure environment showed highly dynamic changes from the C1 to CX level (according to the ISO 9223 standard). A random forest model was developed to predict the corrosion rate and investigate the impacts of ten “corrosive factors” in dynamic atmospheres. The results reveal rust layer, wind speed, rainfall rate, RH, and chloride concentration, played a significant role in the corrosion process.

源语言	英语
文章编号	1
期刊	npj Materials Degradation
卷	6
期	1
DOI	https://doi.org/10.1038/s41529-021-00211-3
出版状态	已出版 - 12月 2022
已对外发布	是

访问文件

10.1038/s41529-021-00211-3

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{c92232036c9c4e54b15ff6afe392f599,

title = "Long-term corrosion monitoring of carbon steels and environmental correlation analysis via the random forest method",

abstract = "In this work, the atmospheric corrosion of carbon steels was monitored at six different sites (and hence, atmospheric conditions) using Fe/Cu-type atmospheric corrosion monitoring technology over a period of 12 months. After analyzing over 3 million data points, the sensor data were interpretable as the instantaneous corrosion rate, and the atmospheric “corrosivity” for each exposure environment showed highly dynamic changes from the C1 to CX level (according to the ISO 9223 standard). A random forest model was developed to predict the corrosion rate and investigate the impacts of ten “corrosive factors” in dynamic atmospheres. The results reveal rust layer, wind speed, rainfall rate, RH, and chloride concentration, played a significant role in the corrosion process.",

author = "Qing Li and Xiaojian Xia and Zibo Pei and Xuequn Cheng and Dawei Zhang and Kui Xiao and Jun Wu and Xiaogang Li",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1038/s41529-021-00211-3",

language = "英语",

volume = "6",

journal = "npj Materials Degradation",

issn = "2397-2106",

publisher = "Nature Publishing Group",

number = "1",

}

TY - JOUR

T1 - Long-term corrosion monitoring of carbon steels and environmental correlation analysis via the random forest method

AU - Li, Qing

AU - Xia, Xiaojian

AU - Pei, Zibo

AU - Cheng, Xuequn

AU - Zhang, Dawei

AU - Xiao, Kui

AU - Wu, Jun

AU - Li, Xiaogang

PY - 2022/12

Y1 - 2022/12

N2 - In this work, the atmospheric corrosion of carbon steels was monitored at six different sites (and hence, atmospheric conditions) using Fe/Cu-type atmospheric corrosion monitoring technology over a period of 12 months. After analyzing over 3 million data points, the sensor data were interpretable as the instantaneous corrosion rate, and the atmospheric “corrosivity” for each exposure environment showed highly dynamic changes from the C1 to CX level (according to the ISO 9223 standard). A random forest model was developed to predict the corrosion rate and investigate the impacts of ten “corrosive factors” in dynamic atmospheres. The results reveal rust layer, wind speed, rainfall rate, RH, and chloride concentration, played a significant role in the corrosion process.

AB - In this work, the atmospheric corrosion of carbon steels was monitored at six different sites (and hence, atmospheric conditions) using Fe/Cu-type atmospheric corrosion monitoring technology over a period of 12 months. After analyzing over 3 million data points, the sensor data were interpretable as the instantaneous corrosion rate, and the atmospheric “corrosivity” for each exposure environment showed highly dynamic changes from the C1 to CX level (according to the ISO 9223 standard). A random forest model was developed to predict the corrosion rate and investigate the impacts of ten “corrosive factors” in dynamic atmospheres. The results reveal rust layer, wind speed, rainfall rate, RH, and chloride concentration, played a significant role in the corrosion process.

UR - https://www.scopus.com/pages/publications/85122777632

U2 - 10.1038/s41529-021-00211-3

DO - 10.1038/s41529-021-00211-3

M3 - 文章

AN - SCOPUS:85122777632

SN - 2397-2106

VL - 6

JO - npj Materials Degradation

JF - npj Materials Degradation

IS - 1

M1 - 1

ER -

Long-term corrosion monitoring of carbon steels and environmental correlation analysis via the random forest method

摘要

访问文件

其它文件与链接

指纹

引用此