Extraction of important variables and mining of dependencies of atmospheric corrosion of carbon steel based on a comprehensive intelligent model

ZHANG Ming; FU Dong-mei; ZHANG Da-wei; MA Ling-wei; SHAO Li-zhen

doi:10.13374/j.issn2095-9389.2022.01.10.007

Volume 45 Issue 3

Mar. 2023

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Engineering > 2023 > 45(3): 407-418

ZHANG Ming, FU Dong-mei, ZHANG Da-wei, MA Ling-wei, SHAO Li-zhen. Extraction of important variables and mining of dependencies of atmospheric corrosion of carbon steel based on a comprehensive intelligent model[J]. Chinese Journal of Engineering, 2023, 45(3): 407-418. doi: 10.13374/j.issn2095-9389.2022.01.10.007

Citation:

ZHANG Ming, FU Dong-mei, ZHANG Da-wei, MA Ling-wei, SHAO Li-zhen. Extraction of important variables and mining of dependencies of atmospheric corrosion of carbon steel based on a comprehensive intelligent model[J]. Chinese Journal of Engineering, 2023, 45(3): 407-418. doi: 10.13374/j.issn2095-9389.2022.01.10.007

Citation:

ZHANG Ming, FU Dong-mei, ZHANG Da-wei, MA Ling-wei, SHAO Li-zhen. Extraction of important variables and mining of dependencies of atmospheric corrosion of carbon steel based on a comprehensive intelligent model[J]. Chinese Journal of Engineering, 2023, 45(3): 407-418. doi: 10.13374/j.issn2095-9389.2022.01.10.007

PDF( 3328 KB)

Extraction of important variables and mining of dependencies of atmospheric corrosion of carbon steel based on a comprehensive intelligent model

doi: 10.13374/j.issn2095-9389.2022.01.10.007

ZHANG Ming^{1, 2
,},
FU Dong-mei^{1, 3
,
,},
ZHANG Da-wei^{4, 5
,
,},
MA Ling-wei^{4, 5},
SHAO Li-zhen^{1, 2}

1.
Shunde Graduate School, University of Science and Technology Beijing, Foshan 528300, China
2.
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
3.
Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China
4.
Institution for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
5.
National Materials Corrosion and Protection Data Center, Beijing 100083, China

More Information

Corresponding author: FU Dong-mei, E-mail: fdm_ustb@ustb.edu.cn; ZHANG Da-wei, E-mail: dzhang@ustb.edu.cn
Received Date: 2022-01-10
Available Online: 2022-02-21
Publish Date: 2023-03-01

Abstract

Abstract

Machine learning algorithms are widely used to predict the corrosion rate of materials in a specific environment. However, the interpretability of such black-box models is poor, which hinders their application in the field of material corrosion. Therefore, to increase algorithm transparency in practical applications, the causal relationship in the material corrosion phenomenon based on machine learning models needs to be further explored. To solve the aforementioned problems, this study analyzed the corrosion process of carbon steel in the atmosphere with many variables and complex mechanisms and proposed an important variable mining framework based on the comprehensive intelligent model. This framework can mine the important environmental variables that affect the early atmospheric corrosion of carbon steel and their influence on the corrosion galvanic current. This study collected the hour-level atmospheric corrosion data, including relative humidity, temperature, rainfall, and O₃, SO₂, NO₂, PM_2.5, and PM₁₀ concentrations, of 45# carbon steel from five test sites in China using the atmospheric corrosion monitor of the China Meteorological Administration. To ensure the stability of the results, three machine learning models with different fitting strategies, namely, random forest, gradient boosted regression trees, and backpropagation neural network, are constructed. Then, the multimodel ensemble important variable selection (MEIVS) algorithm is used to quantify the importance of environmental variables and extract important environmental variables that severely affect the early atmospheric corrosion of carbon steel. Eventually, the partial dependence plot (PDP) of the environmental variables and corrosion galvanic current is drawn. Based on the simulation results, three significant conclusions are obtained: (1) Compared with Pearson’s and Spearman’s correlation coefficients, the important environmental variables mined using the MEIVS algorithm are more consistent with the prior law of early atmospheric corrosion of carbon steel. Relative humidity, temperature, and rainfall have the most significant impact on the early atmospheric corrosion of carbon steel, and O₃ has a considerable influence on the early atmospheric corrosion of carbon steel in Sanya. Moreover, other pollutants in various regions have a weak impact on the early atmospheric corrosion of carbon steel. (2) PDP shows that, in most cases, the corrosion galvanic current of 45# carbon steel is negatively correlated with temperature and positively correlated with relative humidity. (3) PDP and MEIVS are well consistent. The simulation reveals that PDP corresponding to important environmental variables has a greater range of change, and the changing trend of PDP can reflect the influence of environmental variables on the corrosion galvanic current.
- atmospheric corrosion,
- carbon steel,
- model integration,
- important variable extraction,
- partial dependence plot

FullText(HTML)

References(33)

References

[1]	Tsuru T, Nishikata A, Wang J. Electrochemical studies on corrosion under a water film. Mater Sci Eng A, 1995, 198(1-2): 161 doi: 10.1016/0921-5093(95)80071-2
[2]	Wan S, Hou J, Zhang Z F, et al. Monitoring of atmospheric corrosion and dewing process by interlacing copper electrode sensor. Corros Sci, 2019, 150: 246
[3]	Palsson N S, Wongpinkaew K, Khamsuk P, et al. Outdoor atmospheric corrosion of carbon steel and weathering steel exposed to the tropical-coastal climate of Thailand. Mater Corros, 2020, 71(6): 1019 doi: 10.1002/maco.201911340
[4]	Li Z L, Fu D M, Li Y, et al. Application of an electrical resistance sensor-based automated corrosion monitor in the study of atmospheric corrosion. Materials, 2019, 12(7): 1065 doi: 10.3390/ma12071065
[5]	Wang J R, Bai Z H, Xiao K, et al. Influence of atmospheric particulates on initial corrosion behavior of printed circuit board in pollution environments. Appl Surf Sci, 2019, 467-468: 889 doi: 10.1016/j.apsusc.2018.10.244
[6]	Meng J T, Zhang H, Wang X, et al. Data mining to atmospheric corrosion process based on evidence fusion. Materials, 2021, 14(22): 6954
[7]	Oesch S. The effect of SO₂, NO₂, NO and O₃ on the corrosion of unalloyed carbon steel and weathering steel—The results of laboratory exposures. Corros Sci, 1996, 38(8): 1357 doi: 10.1016/0010-938X(96)00025-X
[8]	Cai Y K, Zhao Y, Ma X B, et al. Influence of environmental factors on atmospheric corrosion in dynamic environment. Corros Sci, 2018, 137: 163 doi: 10.1016/j.corsci.2018.03.042
[9]	International Organization for Standardization. ISO 9226—2012 Corrosion of Metals and Alloys—Corrosivity of Atmospheres—Determination of Corrosion Rate of Standard Specimens for the Evaluation of Corrosivity. Geneve: International Organization for Standardization Press, 2012
[10]	International Organization for Standardization. ISO 9223—2012 Corrosion of Metals and Alloys— Corrosivity of Atmospheres—Classification, Determination and Estimation. Geneve: International Organization for Standardization Press, 2012
[11]	Zhi Y J, Jin Z H, Lu L, et al. Improving atmospheric corrosion prediction through key environmental factor identification by random forest-based model. Corros Sci, 2021, 178: 109084
[12]	Yan L C, Diao Y P, Lang Z Y, et al. Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach. Sci Technol Adv Mater, 2020, 21(1): 359 doi: 10.1080/14686996.2020.1746196
[13]	Mansfeld F, Kenkel J V. Electrochemical monitoring of atmospheric corrosion phenomena. Corros Sci, 1976, 16(3): 111
[14]	Yang D M, Mei H W, Wang L M. Corrosion measurement of the atmospheric environment using galvanic cell sensors. Sensors, 2019, 19(2): 331 doi: 10.3390/s19020331
[15]	Mizuno D, Suzuki S, Fujita S, et al. Corrosion monitoring and materials selection for automotive environments by using Atmospheric Corrosion Monitor (ACM) sensor. Corros Sci, 2014, 83: 217
[16]	Shi Y N, Fu D M, Zhou X Y, et al. Data mining to online galvanic current of zinc/copper Internet atmospheric corrosion monitor. Corros Sci, 2018, 133: 443 doi: 10.1016/j.corsci.2018.02.005
[17]	Pei Z B, Cheng X Q, Yang X J, et al. Understanding environmental impacts on initial atmospheric corrosion based on corrosion monitoring sensors. J Mater Sci Technol, 2021, 64: 214 doi: 10.1016/j.jmst.2020.01.023
[18]	Lipton Z C. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 2018, 16(3): 31
[19]	Pei Z B, Xiao K, Chen L H, et al. Investigation of corrosion behaviors on an Fe/Cu-type ACM sensor under various environments. Metals, 2020, 10(7): 905
[20]	Pei Z B, Zhang D W, Zhi Y J, et al. Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning. Corros Sci, 2020, 170: 108697
[21]	張明, 付冬梅, 程學群, 等. 基于變量選擇的尖點突變模型的兩步構建方法. 工程科學學報,http://www.shandonghometex.com/article/doi/10.13374/j.issn2095-9389.2021.07.19.006 Zhang M, Fu D M, Cheng X Q, et al. A two-step method for constructing a cusp catastrophe model based on the selection of important variables. Chin J Eng,http://www.shandonghometex.com/article/doi/10.13374/j.issn2095-9389.2021.07.19.006
[22]	Breiman L. Random forests. Machine Learning, 2001, 45(1): 5 doi: 10.1023/A:1010933404324
[23]	Fisher A, Rudin C, Dominici F. All models are wrong, but many are useful: Learning a variable's importance by studying an entire class of prediction models simultaneously [J/OL]. arXiv preprint (2018-06-23) [2022-01-10].https://arxiv.org/abs/1801.01489
[24]	Biecek P. DALEX: explainers for complex predictive models [J/OL]. arXiv preprint (2018-06-23) [2022-01-10].https://arxiv.org/abs/1806.08915
[25]	Zhang Z H, Beck M W, Winkler D A, et al. Opening the black box of neural networks: Methods for interpreting neural network models in clinical applications. Ann Transl Med, 2018, 6(11): 216 doi: 10.21037/atm.2018.05.32
[26]	Nishikata A, Ichihara Y, Hayashi Y, et al. Influence of electrolyte layer thickness and pH on the initial stage of the atmospheric corrosion of iron. J Electrochem Soc, 1997, 144(4): 1244 doi: 10.1149/1.1837578
[27]	Xia Y, Liu P, Zhang J Q, et al. Corrosion behavior of weathering steel under thin electrolyte layer at different relative humidity. J Mater Eng Perform, 2018, 27(1): 202 doi: 10.1007/s11665-017-3101-0
[28]	Xiao K, Gao X, Yan L D, et al. Atmospheric corrosion factors of printed circuit boards in a dry-heat desert environment: Salty dust and diurnal temperature difference. Chem Eng J, 2018, 336: 92 doi: 10.1016/j.cej.2017.11.017
[29]	Koushik B G, van den Steen N, Mamme M H, et al. Review on modelling of corrosion under droplet electrolyte for predicting atmospheric corrosion rate. J Mater Sci Technol, 2021, 62: 254
[30]	Zhi Y J, Yang T, Fu D M. An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels. J Mater Sci Technol, 2020, 49: 202
[31]	Ha M Y, Jeon S H, Jeong Y S, et al. Corrosion environment monitoring of local structural members of a steel truss bridge under a marine environment. Int J Steel Struct, 2021, 21(1): 167 doi: 10.1007/s13296-020-00424-3
[32]	Rajendran K, Sumi A, Bhattachariya M K, et al. Influence of relative humidity in Vibrio cholerae infection: A time series model. Indian J Med Res, 2011, 133: 138
[33]	Yoon Y, Angel J D, Hansen D C. Atmospheric corrosion of silver in outdoor environments and modified accelerated corrosion chambers. Corrosion, 2016, 72(11): 1424