Evaluation and Screening of Atmospheric Corrosion Prediction Algorithms of Steels in Different Regions of China

doi:10.11902/1005.4537.2023.257

Journal of Chinese Society for Corrosion and protection

2024, Vol. 44

Issue (4): 939-948 DOI: 10.11902/1005.4537.2023.257

Current Issue | Archive | Adv Search

Evaluation and Screening of Atmospheric Corrosion Prediction Algorithms of Steels in Different Regions of China

SHEN Jian¹^,², WU Kexian¹^,²^,³(

), HE Xiaoyu¹^,², FANG Xinglong⁴

1. Zhejiang Institute of Communications Co., Ltd., Hangzhou 310006, China
2. Key Laboratory of Integrated Transportation Theory and Transportation Industry, Hangzhou 310006, China
3. Institute of Structural Engineering, Zhejiang University, Hangzhou 310058, China
4. Zhejiang Seaport River Port Development Co., Ltd., Hangzhou 310005, China

Cite this article:

SHEN Jian, WU Kexian, HE Xiaoyu, FANG Xinglong. Evaluation and Screening of Atmospheric Corrosion Prediction Algorithms of Steels in Different Regions of China. Journal of Chinese Society for Corrosion and protection, 2024, 44(4): 939-948.

Download:

HTML

PDF(2940KB)
Export: BibTeX | EndNote (RIS)

Abstract

Atmospheric corrosion of steels is a universal problem. Improving the prediction accuracy of atmospheric corrosion rate of steels in China is of great significance for setting corrosion margin, preventing corrosion failure and reducing the corrosion induced economic loss. For different type of steels in different regions of China, a prediction method of corrosion depth for steels based on machine learning algorithm was proposed, including data acquisition and processing, model training and testing, model evaluation and screening, feature ranking and other steps. The applicability of different algorithms was evaluated and the optimal algorithm of the corrosion prediction for steels was selected. Firstly, the corrosion data, environmental- and materials-features of 10 atmospheric exposure stations in China were collected. The corrosion depth of steels was predicted by using standard formulas and 6 machine learning algorithms. The grades of environmental corrosivity of atmospheric exposure stations were evaluated. Then, the prediction errors were analyzed, the accuracy of environmental corrosivity grade assessment was compared, and the prediction model suitable for steel corrosion was screened. Moreover, the sensitivity of materials- and environmental-features were analyzed, revealing the main factors of environments and materials affecting the atmospheric corrosion of steels. The results show that compared with the standard formula, the accuracy of the prediction models is greatly improved by RF and LSTM algorithms. In addition to the terms such as temperature, humidity, sulfate- and chloride-deposition rates mentioned in standard formulas, the acidity, alkalinity and rainwater corrosive ion concentration of rainwaters have a great impact on the corrosion of steels, which should be considered.

Key words: steel atmospheric exposure stations machine learning feature sensitivity environmental corrosive grade

Received: 17 August 2023 32134.14.1005.4537.2023.257

ZTFLH:

TG172.3

Fund: Science and Technology Plan Project of Zhejiang Provincial Department of Transportation(2020003);Science and Technology Plan Project of Zhejiang Provincial Department of Transportation(2023007);Transportation Industry Key Technology Project(2020-GT-010)

Corresponding Authors: WU Kexian, E-mail: wukexianzju@163.com

	Service

	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	（）
	Articles by authors
	SHEN Jian
	WU Kexian
	HE Xiaoyu
	FANG Xinglong

URL:

https://www.jcscp.org/EN/10.11902/1005.4537.2023.257 OR https://www.jcscp.org/EN/Y2024/V44/I4/939

Fig.1 Box plot of atmospheric station corrosion data

Fig.2 Heat maps of corrosion depth and characteristic correlation coefficient matrix: (a) material features, (b) environment features

Table 1 Influence coefficients of chemical elements

Fig.3 Prediction results of corrosion depth by GB (a), SVM (b), RF (c), RBFNN (d), BPNN (e), LSTM (f) and CNN (g)

Table 2 Error metrics of prediction outcomes for different machine learning models

Fig.4 RMSE of prediction results of atmospheric corrosion depth: (a) different regional datasets, (b) different experiment periods

Features	GB	SVM	RF	RBFNN	BPNN	LSTM	CNN	RF importance
Experiment time	16.1%	6.8%	18.1%	5.5%	7.4%	17.6%	31.2%	15.0%
R_pH	-	18.1%	27.8%	22.2%	52.1%	14.5%	0.1%	9.6%
Cr	0.1%	6.5%	0.3%	2.5%	3.2%	8.1%	4.5%	8.1%
R $S O 4 2 -$	-	3.9%	1.4%	5.5%	1.4%	1.5%	20.1%	5.6%
t_sun	-	2.7%	5.5%	3.2%	3.9%	0.5%	0.5%	5.5%
D_dust	-	1.5%	2.4%	0.5%	0.6%	1.8%	1.0%	5.2%
D_sulfation	10.3%	3.1%	0.1%	2.1%	0.4%	2.0%	0.2%	4.6%
T	4.7%	3.3%	2.5%	3.8%	3.9%	4.2%	9.8%	4.5%
D $N O 2$	-	2.2%	0.9%	3.1%	0.5%	3.0%	0%	4.4%
C	0%	5.6%	6.7%	1.5%	0.5%	1.1%	0.1%	4.3%
Cu	0.1%	3.0%	0.6%	10.7%	1.9%	2.6%	0%	3.9%
Si	0.5%	1.0%	7.9%	4.5%	1.9%	5.5%	0.4%	3.3%
D_seasalt	7.1%	0.1%	4.9%	1.5%	2.0%	3.8%	0.1%	3.2%
D $N H 3$	-	1.5%	1.2%	2.3%	2.9%	1.0%	0%	3.2%
RH	59.5%	13.8%	3.3%	6.5%	5.6%	13.6%	7.1%	2.9%
H	-	0.1%	0.5%	0.6%	0.4%	1.3%	13.2%	2.9%
Mn	-	2.5%	2.8%	6.7%	0.6%	1.4%	0.7%	2.7%
S	1.0%	0.6%	3.5%	6.9%	2.3%	2.3%	0%	2.3%
D $H 2 S$		1.3%	1.0%	0.8%	0.2%	2.5%	0%	2.3%
P	0.4%	7.1%	6.3%	1.5%	3.6%	5.4%	0%	2.1%
R_Cl⁻	-	0.6%	1.6%	4.7%	2.6%	2.5%	10.8%	1.7%
V_wind	-	1.3%	0.3%	1.6%	0.8%	1.8%	0%	1.7%
Ni	0.1%	2.8%	0%	0.1%	0%	0.1%	0.1%	1.5%
Mo	-	0.1%	0.2%	0.1%	1.1%	0.4%	0%	0.7%
Ti	-	5.5%	0%	0.2%	0.2%	0.1%	0%	0%
V	-	3.1%	0%	1.0%	0.1%	1.0%	0%	0%
Nb	-	1.2%	0%	0.2%	0%	0.6%	0%	0%
Al	-	0.8%	0.1%	0.2%	0.1%	0%	0%	0%

Table 3 Feature sensitivity metrics for different machine learning models

Fig.5 Sensitivity percentages of three types of features for different machine learning models

Fig.6 Radar chart of prediction accuracies for various models

Fig.7 Differences in material types for assessing the accuracy of atmospheric corrosive grades: (a) GB, (b) RF

Fig.8 Regional differences in the accuracy of atmospheric corrosive grade assessment

[1]	Zhi Y J, Fu D M, Zhang D W, et al. Prediction and knowledge mining of outdoor atmospheric corrosion rates of low alloy steels based on the random forests approach [J]. Metals, 2019, 9(3): 383
[2]	Wang S S, Ma S J, Che K, et al. Application status of machine learning in field of natural environment corrosion assessment and prediction [J]. J. Chin. Soc. Corros. Prot., 2023, 43: 441
	王莎莎, 马帅杰, 车琨等. 机器学习在自然环境腐蚀评估与预测领域的应用现状 [J]. 中国腐蚀与防护学报, 2023, 43: 441 doi: 10.11902/1005.4537.2022.147
[3]	Ministry of Science and Technology of the People’s Republic of China. National environmental corrosion platform of China [EB/OL]. (2023-07-20). https://most.gov.cn/ztzl/kjzykfgx/kjzygjjctjpt/kjzyptml/201407/t20140716_114280.html
	中华人民共和国科学技术部. 国家材料环境腐蚀野外科学观测研究平台 [EB/OL]. (2023-07-20). https://most.gov.cn/ztzl/kjzykfgx/kjzygjjctjpt/kjzyptml/201407/t20140716_114280.html
[4]	Fang S F, Wang M P, Qi W H, et al. Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials [J]. Comput. Mater. Sci., 2008, 44(2): 647
[5]	Jiménez-Come M J, Turias I J, Ruiz-Aguilar J J. A two-stage model based on artificial neural networks to determine pitting corrosion status of 316L stainless steel [J]. Corros. Rev., 2016, 34(1-2): 113
[6]	Pintos S, Queipo N V, de Rincón O T, et al. Artificial neural network modeling of atmospheric corrosion in the MICAT project [J]. Corros. Sci., 2000, 42(1): 35
[7]	Zhi Y J. Data-driven prediction model for small sample data of metal materials atmospheric corrosion [D]. Beijing: University of Science and Technology Beijing, 2019
	支元杰. 大气环境下小样本金属材料腐蚀的数据驱动预测模型 [D]. 北京: 北京科技大学, 2019
[8]	Zhi Y J, Jin Z H, Lu L, et al. Improving atmospheric corrosion prediction through key environmental factor identification by random forest-based model [J]. Corros. Sci., 2021, 178: 109084
[9]	Yang X J. Research on the control of corrosion resistance of Cr-containing low-alloy steel based on corrosion big data technology [D]. Beijing: University of Science and Technology Beijing, 2021
	杨小佳. 基于腐蚀大数据技术的含Cr低合金钢耐蚀性能调控研究 [D]. 北京: 北京科技大学, 2021
[10]	Li Q, Xia X J, Pei Z B, et al. Long-term corrosion monitoring of carbon steels and environmental correlation analysis via the random forest method [J]. npj Mater. Degrad., 2022, 6: 1
[11]	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 [J]. Corros. Sci., 2020, 170: 108697
[12]	Zhang M, Fu D M, Zhang D W, et al. Extraction of important variables and mining of dependencies of atmospheric corrosion of carbon steel based on a comprehensive intelligent model [J]. Chin. J. Eng., 2023, 45(3): 407
	张明, 付冬梅, 张达威等. 基于综合智能模型的碳钢大气腐蚀重要变量提取和依赖关系挖掘 [J]. 工程科学学报, 2023, 45(3): 407
[13]	Feng D C, Wu G. Interpretable machine learning-based modeling approach for fundamental properties of concrete structures [J]. J. Build. Struct., 2022, 43(4): 228
	冯德成, 吴刚. 混凝土结构基本性能的可解释机器学习建模方法 [J]. 建筑结构学报, 2022, 43(4): 228
[14]	China Gateway to Corrosion and Protection. Data management system [EB/OL]. (2023-07-20). http://data.ecorr.org.cn/
	中国腐蚀与防护网. 数据管理系统 [EB/OL]. (2023-07-20). http://data.ecorr.org.cn/
[15]	Evans U R. The Corrosion and Oxidation of Metals: Scientific Principles and Practical Applications [M]. London: Edward Arnold, 1960
[16]	State Administration for Market Regulation, Standardization Administration of the People's Republic of China. Corrosion of metals and alloys—Corrosivity of atmospheres—Part 1: Classification, determination and estimation [S]. Beijing: Standards Press of China, 2018
	国家市场监督管理总局, 中国国家标准化管理委员会. 金属和合金的腐蚀大气腐蚀性第1部分: 分类、测定和评估 [S]. 北京: 中国标准出版社, 2018
[17]	State Administration for Market Regulation, Standardization Administration of the People's Republic of China. Corrosion of metals and alloys—Corrosivity of atmospheres—Part 2: Guiding values for the corrosivity categories [S]. Beijing: Standards Press of China, 2018
	国家市场监督管理总局, 中国国家标准化管理委员会. 金属和合金的腐蚀大气腐蚀性第2部分: 腐蚀等级的指导值 [S]. 北京: 中国标准出版社, 2018
[18]	Cortes C, Vapnik V. Support-vector networks [J]. Mach. Learn., 1995, 20(3): 273
[19]	Ho T K. The random subspace method for constructing decision forests [J]. IEEE Trans. Pattern Analy. Mach. Intell., 1998, 20(8): 832
[20]	Broomhead D S, Lowe D. Multivariable functional interpolation and adaptive networks [J]. Complex Syst., 1988, 2: 321
[21]	Goodfellow I, Bengio Y, Courville A. 6.5 Back-Propagation and Other Differentiation Algorithms [M]. MIT Press, 2016
[22]	Hochreiter S, Schmidhuber J. Long short-term memory [J]. Neural Comput., 1997, 9(8): 1735 doi: 10.1162/neco.1997.9.8.1735 pmid: 9377276
[23]	Valueva M V, Nagornov N N, Lyakhov P A, et al. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation [J]. Math. Comput. Simul., 2020, 177: 232

[1]	LIU Chao, DAI Wenhe, WANG Kejun, CHEN Zengyao, AN Yanjun, LIU Zhiyong. Effect of Cu Content on Corrosion Behavior of Q420 Steel in an Artificial Solution of Bottom Plate Environment of Cargo Oil Tanks[J]. 中国腐蚀与防护学报, 2024, 44(4): 927-938.
[2]	WANG Bihui, LIU Ju, CUI Zhixiang, XIAO Bo, YANG Tianrang, ZHANG Naiqiang. Long-term Stability of MnCo Spinel Coatings Prepared by Electrophoretic Deposition at High Temperatures[J]. 中国腐蚀与防护学报, 2024, 44(4): 972-978.
[3]	YU Jiahui, WANG Tongtong, GAO Yun, GAO Rongjie. Fabrication and Photocathodic Protection Performance of Bi₂S₃/TiO₂ Nanocomposites for 304 Stainless Steel[J]. 中国腐蚀与防护学报, 2024, 44(4): 901-908.
[4]	WANG Gang, LI Zhao, WANG Tao, DUAN Teng, DU Cuiwei. Corrosion Behavior in Soil of Xinjiang of Steels for Photovoltaic Pile Foundation[J]. 中国腐蚀与防护学报, 2024, 44(4): 987-992.
[5]	WANG Yuxue, ZHU Aohong, WANG Liwei, CUI Zhongyu. Comparative Study on Corrosion Behavior of Two Novel Ni-Cr-Mo-V Steels in Simulated Seawater Environment[J]. 中国腐蚀与防护学报, 2024, 44(4): 918-926.
[6]	QIAO Ze, LI Qingquan, LIU Xiaohang, LI Yizhou. Effect of Nitrate and Galvanic Couple on Crevice Corrosion Behavior of 7075-T651 Al-alloy in Neutral NaCl Solution[J]. 中国腐蚀与防护学报, 2024, 44(4): 1047-1054.
[7]	ZHANG Jiawei, HUANG Feng, WANG Hanmin, LANG Fengjun, YUAN Wei, LIU Jing. Effect of Rolling Scale on Evolution of Fast-stabilized Rust Layer and Corrosion Resistance of a Weathering Steel[J]. 中国腐蚀与防护学报, 2024, 44(4): 891-900.
[8]	FU Jiangyue, GUO Jianxi, YANG Yange, LENG Zhe, WANG Wen. Erosion-corrosion Behavior of a High Strength Low Alloy Steel in Flowing 3.5%NaCl Solution[J]. 中国腐蚀与防护学报, 2024, 44(3): 585-600.
[9]	FENG Xingguo, GU Zhuoran, FAN Qiqi, LU Xiangyu, YANG Yashi. Corrosion Resistance of 2205 Stainless Steel Bar in Modified Coral Concretes[J]. 中国腐蚀与防护学报, 2024, 44(3): 789-796.
[10]	LI Xiaohui, TAO Yiqi, HUANG Hao, ZHANG Zhicheng, LI Entong, LYU Zhanpeng, CHEN Junjie. Effect of Magnetic Field on Anodic Processes of X70 Pipeline Steel in Sodium Carbonate Solution[J]. 中国腐蚀与防护学报, 2024, 44(3): 765-771.
[11]	HU Lihua, YI Hualei, YANG Weijian, SUN Chong, SUN Jianbo. Effect of Water Content on Corrosion Behavior of X65 Pipeline Steel in Supercritical CO₂ Fluids[J]. 中国腐蚀与防护学报, 2024, 44(3): 576-584.
[12]	LI Chan, WANG Qingtian, YANG Chenggang, ZHANG Xianwei, HAN Dongao, LIU Yuwei, LIU Zhiyong. Corrosion Behavior of 904L Super-austenitic Stainless Steel in Simulated Primary Water in Nuclear Power Plants[J]. 中国腐蚀与防护学报, 2024, 44(3): 716-724.
[13]	XIE Hui, YU Chao, HUA Jing, JIANG Xiu. Effect of NH⁺₄ Concentration on Corrosion Behavior of N80 Steel in CO₂-saturated 3%NaCl Solutions[J]. 中国腐蚀与防护学报, 2024, 44(3): 745-754.
[14]	XING Shaohua, PENG Wenshan, QIAN Yao, LI Xiangbo, MA Li, ZHANG Dalei. Effect of Seawater Flow Velocity on Pitting Corrosion of 2205 Stainless Steel with Different Surface Treatments[J]. 中国腐蚀与防护学报, 2024, 44(3): 658-668.
[15]	SUN Jiayu, PENG Wenshan, XING Shaohua. Combined Effect of Stress and Dissolved Oxygen on Corrosion Behavior of Ni-Cr-Mo-V High Strength Steel[J]. 中国腐蚀与防护学报, 2024, 44(3): 755-764.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed