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| 机器学习在自然环境腐蚀评估与预测领域的应用现状 |
王莎莎, 马帅杰, 车琨, 杜艳霞( ) |
| 北京科技大学新材料技术研究院 北京 100083 |
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| Application Status of Machine Learning in Field of Natural Environment Corrosion Assessment and Prediction |
| WANG Shasha, MA Shuaijie, CHE Kun, DU Yanxia() |
| Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China |
引用本文:
王莎莎, 马帅杰, 车琨, 杜艳霞. 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(3): 441-451.
WANG Shasha,
MA Shuaijie,
CHE Kun,
DU Yanxia.
Application Status of Machine Learning in Field of Natural Environment Corrosion Assessment and Prediction. Journal of Chinese Society for Corrosion and protection, 2023, 43(3): 441-451.
链接本文:
https://www.jcscp.org/CN/10.11902/1005.4537.2022.147
或
https://www.jcscp.org/CN/Y2023/V43/I3/441
|
| 1 |
Wang C. Study on soil corrosion monitoring of substation in Zhejiang province [D]. Hangzhou: Zhejiang University of Technology, 2020
|
| 1 |
王 传. 浙江省变电站土壤腐蚀监测研究 [D]. 杭州: 浙江工业大学, 2020
|
| 2 |
Koch G, Varney J, Thompson N, et al. International measures of prevention, application, and economics of corrosion technologies study [A]. 2016 NACE International [C]. Houston, Texas: NACE
|
| 3 |
Hou B R, Lu D Z. Corrosion cost and preventive strategies in China [J]. Bull. Chin. Acad. Sci., 2018, 33: 601
|
| 3 |
侯保荣, 路东柱. 我国腐蚀成本及其防控策略 [J]. 中国科学院院刊, 2018, 33: 601
|
| 4 |
Zhu Z P, Wang L J, Pei F, et al. Evaluation of soil corrosivity for ground grid of substations in an area by factor analysis [J]. J. Chin. Soc. Corros. Prot., 2014, 34: 147
|
| 4 |
朱志平, 王磊静, 裴 锋 等. 因子分析法在变电站土壤腐蚀性评价中的应用 [J]. 中国腐蚀与防护学报, 2014, 34: 147
doi: 10.11902/1005.4537.2013.074
|
| 5 |
Scully J R. Editorial-Future frontiers in corrosion science and engineering, Part II: Managing the many stages of corrosion [J]. Corrosion, 2019, 75: 123
doi: 10.5006/3132
|
| 6 |
He Y D, Qi H B. An Introduction to Material Corrosion and Protection [M]. Beijing: Machinery Industry Press, 2005
|
| 6 |
何业东, 齐慧滨. 材料腐蚀与防护概论 [M]. 北京: 机械工业出版社, 2005
|
| 7 |
Xie J X, Su Y J, Xue D Z, et al. Machine learning for materials research and development [J]. Acta Metall. Sin., 2021, 57: 1343
doi: 10.11900/0412.1961.2021.00357
|
| 7 |
谢建新, 宿彦京, 薛德祯 等. 机器学习在材料研发中的应用 [J]. 金属学报, 2021, 57: 1343
doi: 10.11900/0412.1961.2021.00357
|
| 8 |
PRATAP A, SARDANA N. Machine learning-based image processing in materials science and engineering: A review [J]. Mater. Today: Proc., 2022, 62: 7341
|
| 9 |
Mi X X, Tang A T, Zhu Y C, et al. Research progress of machine learning in material science [J]. Mater. Rev., 2021, 35: 15115
|
| 9 |
米晓希, 汤爱涛, 朱雨晨 等. 机器学习技术在材料科学领域中的应用进展 [J]. 材料导报, 2021, 35: 15115
|
| 10 |
Kim J, Kang D, Kim S, et al. Catalyze materials science with machine learning [J]. ACS Mater. Lett., 2021, 3: 1151
|
| 11 |
Dogan A, Birant D. Machine learning and data mining in manufacturing [J]. Expert Syst. Appl., 2021, 166: 114060
doi: 10.1016/j.eswa.2020.114060
|
| 12 |
Miyazato I, Tanaka Y, Takahashi K. Accelerating the discovery of hidden two-dimensional magnets using machine learning and first principle calculations [J]. J. Phys.: Condens. Matter, 2018, 30: 06LT01
|
| 13 |
Fu H X, Zhao H. Application Design of MATLAB Neural Network [M]. Beijing: Machinery Industry Press, 2010: 83
|
| 13 |
傅荟璇, 赵 红. MATLAB神经网络应用设计 [M]. 北京: 机械工业出版社, 2010: 83
|
| 14 |
Donaldson R G, Kamstra M. Forecast combining with neural networks [J]. J. Forecasting, 1996, 15: 49
doi: 10.1002/(ISSN)1099-131X
|
| 15 |
Wei D Z. Research on urban air quality based on a BP neural network prediction model [D]. Wuhan: Huazhong University of Science and Technology, 2009
|
| 15 |
韦德志. 城市区域空气质量的BP神经网络预测研究 [D]. 武汉: 华中科技大学, 2009
|
| 16 |
Yu S H, Deng J. GDP prediction based on principal component analysis and Bayesian regularization BP neural network [J]. J. Hunan Univ. (Soc. Sci.), 2011, 25(6): 42
|
| 16 |
喻胜华, 邓 娟. 基于主成分分析和贝叶斯正则化BP神经网络的GDP预测 [J]. 湖南大学学报 (社会科学版), 2011, 25(6): 42
|
| 17 |
Luo S W, Liu Q B, Huang C Y, et al. Research progress on corrosion of shipboard steel in marine environment [J]. Guangzhou Chem. Ind., 2019, 47(13): 33
|
| 17 |
罗思维, 刘泉兵, 黄承勇 等. 船板钢在海洋环境下腐蚀研究进展 [J]. 广州化工, 2019, 47(13): 33
|
| 18 |
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
|
| 18 |
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
|
| 19 |
Dou X F. Overview of KNN algorithm [J]. Telecom World, 2018, (10): 273
|
| 19 |
窦小凡. KNN算法综述 [J]. 通讯世界, 2018, (10): 273
|
| 20 |
Weinberger K Q, Saul L K. Distance metric learning for large margin nearest neighbor classification [J]. J. Mach. Learn. Res., 2009, 10: 207
|
| 21 |
Li W B, Wang Y, Feng Y T, et al. The non-excavation corrosion prediction model of grounding grid based on particle swarm optimization extreme learning machine [J]. IOP Conf. Ser.: Earth Environ. Sci., 2021, 692: 022118
|
| 22 |
Li H T, Yuan S. Corrosion prediction of marine engineering materials based on genetic algorithm and BP neural network [J]. Mar. Sci., 2020, 44(10): 33
|
| 22 |
李海涛, 袁 森. 基于遗传算法和BP神经网络的海洋工程材料腐蚀预测研究 [J]. 海洋科学, 2020, 44(10): 33
|
| 23 |
Zhang P H, Xiao Y A, Zhao J C, et al. Application of GA-BP artificial neural network in seawater corrosion prediction of copper alloys [J]. Equip. Environ. Eng., 2021, 18(12): 73
|
| 23 |
张彭辉, 肖攸安, 赵建仓 等. GA-BP神经网络在铜合金海洋腐蚀预测中的应用 [J]. 装备环境工程, 2021, 18(12): 73
|
| 24 |
Li X, Yu J X, Miao C S, et al. Prediction of corrosion rate in marine environment based on support vector machine combining with genetic algorithm [J]. China Offshore Platform, 2018, 33(5): 56
|
| 24 |
李 响, 余建星, 苗春生 等. 基于遗传算法SVM的海洋环境腐蚀速率预测 [J]. 中国海洋平台, 2018, 33(5): 56
|
| 25 |
Xiang N R, Yan H, Wang W, et al. Prediction of metal corrosion rate based on GA-BP neural network [J]. J. Electric Power, 2018, 33: 48
|
| 25 |
向乃瑞, 闫 海, 王 炜 等. GA-BP神经网络预测金属腐蚀速率 [J]. 电力学报, 2018, 33: 48
|
| 26 |
Qin X X. Research on soil corrosion and protection of pipeline [D]. Qingdao: China University of Petroleum (East China), 2009
|
| 26 |
秦晓霞. 埋地管道土壤腐蚀性与防护研究 [D]. 青岛: 中国石油大学, 2009
|
| 27 |
Liang P, Du C W, Yu J, et al. Analysis of factors on soil corrosion for buried Q235 steel in Ku'erle region [J]. Corros. Sci. Prot. Technol., 2010, 22: 146
|
| 27 |
梁 平, 杜翠薇, 余 杰 等. Q235钢在库尔勒地区土壤腐蚀性的影响因素分析 [J]. 腐蚀科学与防护技术, 2010, 22: 146
|
| 28 |
Li L, Li X G, Xing S B, et al. Research on soil corrosion rate prediction of carbon steel in typical Chinese cities based on BP artificial neural network [J]. Corros. Sci. Prot. Technol., 2013, 25: 372
|
| 28 |
李 丽, 李晓刚, 邢士波 等. BP人工神经网络对国内典型地区碳钢土壤腐蚀的预测研究 [J]. 腐蚀科学与防护技术, 2013, 25: 372
|
| 29 |
Zha D G. Research on machine learning framework based on AMLF optimization algorithm [J]. J. Jiamusi Univ. (Nat. Sci. Ed.), 2022, 40(1): 56
|
| 29 |
查道贵. 基于AMLF优化算法的机器学习框架的技术研究 [J]. 佳木斯大学学报 (自然科学版), 2022, 40(1): 56
|
| 30 |
Ling X, Xu L S, Yu J P, et al. Prediction of corrosion rate in oil pipeline based on improved BP neural network [J]. Transd. Microsyst. Technol., 2021, 40(2): 124
|
| 30 |
凌 晓, 徐鲁帅, 余建平 等. 基于改进的BP神经网络的输油管道内腐蚀速率预测 [J]. 传感器与微系统, 2021, 40(2): 124
|
| 31 |
Liu J. Research on soil corrosion of 20# steel buried gas pipelines in Chongqing [D]. Chongqing: Chongqing University, 2013
|
| 31 |
刘 静. 重庆市20#钢质燃气埋地管道的土壤腐蚀研究 [D]. 重庆: 重庆大学, 2013
|
| 32 |
Wang W H, Shen S M, Yu X C. Review on research methods of soil corrosion for buried pipeline steels [J]. J. Nanjing Univ. Technol. (Nat. Sci. Ed.), 2008, 30(4): 105
|
| 32 |
王文和, 沈士明, 於孝春. 埋地管道钢土壤腐蚀研究方法进展 [J]. 南京工业大学学报 (自然科学版), 2008, 30(4): 105
|
| 33 |
Luo X H. Evaluation of soil corrosion grade based on IAHP and fuzzy grey theory [J]. Mater. Prot., 2019, 52(7): 54
|
| 33 |
罗小虎. 基于IAHP和模糊灰色理论的土壤腐蚀等级评价 [J]. 材料保护, 2019, 52(7): 54
|
| 34 |
Luo Z S, Wang W H, Wang X W, et al. Soil corrosion prediction of buried pipeline based on the model of RS-PSO-GRNN [J]. Mater. Prot., 2018, 51(8): 47
|
| 34 |
骆正山, 王文辉, 王小完 等. 基于RS-PSO-GRNN的埋地管道土壤腐蚀预测 [J]. 材料保护, 2018, 51(8): 47
|
| 35 |
Liang C J, Xie B, Liu Y Q, et al. Prediction of soil corrosion rate of buried pipeline based on KPCA-GWO-SVM [J]. Oil Gas Storage Transport., 2021, 40: 938
|
| 35 |
梁昌晶, 谢 波, 刘延庆 等. 基于KPCA-GWO-SVM的埋地管道土壤腐蚀速率预测 [J]. 油气储运, 2021, 40: 938
|
| 36 |
Guo Y Y. Study on corrosion prediction of Q235 steel from soil in Hainan substation based on artificial neural network [D]. Baoding: North China Electric Power University, 2016
|
| 36 |
郭阳阳. 基于神经网络的海南变电站土壤对Q235钢的腐蚀预测研究 [D]. 保定: 华北电力大学, 2016
|
| 37 |
Ding L, Rangaraju P, Poursaee A. Application of generalized regression neural network method for corrosion modeling of steel embedded in soil [J]. Soils Found., 2019, 59: 474
doi: 10.1016/j.sandf.2018.12.016
|
| 38 |
Qu L S, Li X G, Du C W, et al. Corrosion rate prediction model of carbon steel in regional soil based on BP artificial neural network [J]. J. Univ. Sci. Technol. Beijing, 2009, 31: 1569
|
| 38 |
曲良山, 李晓刚, 杜翠薇 等. 运用BP人工神经网络方法构建碳钢区域土壤腐蚀预测模型 [J]. 北京科技大学学报, 2009, 31: 1569
|
| 39 |
Hou B R. Theory of Marine Corrosion Environment and its Application [M]. Beijing: Science Press, 1999
|
| 39 |
侯保荣. 海洋腐蚀环境理论及其应用 [M]. 北京: 科学出版社, 1999
|
| 40 |
Hu G. Study on submarine pipeline corrosion detection and corrosion prediction [D]. Chongqing: Chongqing University, 2007
|
| 40 |
胡 舸. 海底管线腐蚀检测与腐蚀预测的研究 [D]. 重庆: 重庆大学, 2007
|
| 41 |
Liu X Q. Investigation on the corrosion behavior and corrosion prediction model of engineering steels used in marine environment [D]. Qingdao: Institute of Oceanography, Chinese Academy of Sciences, 2004
|
| 41 |
刘学庆. 海洋环境工程钢材腐蚀行为与预测模型的研究 [D]. 青岛: 中国科学院海洋研究所, 2004
|
| 42 |
Ma L T, Dong H F, Zhu G, et al. Application and research of neural network in predicting metal corrosion rate in marine environment [J]. Mater. Prot., 2018, 51(9): 35
|
| 42 |
马良涛, 董海防, 朱 刚 等. 神经网络预测海洋环境下金属腐蚀速率的应用及研究 [J]. 材料保护, 2018, 51(9): 35
|
| 43 |
Wen Y F, Cai C Z, Liu X H, et al. Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression [J]. Corros. Sci., 2009, 51: 349
doi: 10.1016/j.corsci.2008.10.038
|
| 44 |
Luo Z S, Qin Y, Zhang X S, et al. Prediction of external corrosion rate of marine pipelines based on LASSO-WOA-LSSVM [J]. Surf. Technol., 2021, 50(5): 245
|
| 44 |
骆正山, 秦 越, 张新生 等. 基于LASSO-WOA-LSSVM的海洋管线外腐蚀速率预测 [J]. 表面技术, 2021, 50(5): 245
|
| 45 |
Zhang X S, Chang Y G. Prediction of external corrosion rate of offshore oil and gas pipelines based on FA-BAS-ELM [J]. China Saf. Sci. J., 2022, 32(2): 99
|
| 45 |
张新生, 常潆戈. 基于FA-BAS-ELM的海洋油气管道外腐蚀速率预测 [J]. 中国安全科学学报, 2022, 32(2): 99
doi: 10.16265/j.cnki.issn1003-3033.2022.02.014
|
| 46 |
Wei X, Fu D M, Chen M D, et al. Data mining to effect of key alloying elements on corrosion resistance of low alloy steels in Sanya seawater environment Alloying Elements [J]. J. Mater. Sci. Technol., 2021, 64: 222
doi: 10.1016/j.jmst.2020.01.040
|
| 47 |
Diao Y P, Yan L C, Gao K W. Improvement of the machine learning-based corrosion rate prediction model through the optimization of input features [J]. Mater. Des., 2021, 198: 109326
doi: 10.1016/j.matdes.2020.109326
|
| 48 |
Zeng J J, Zhou X J, Wu J, et al. Research status of correlation and life prediction of atmospheric corrosion test of metal materials [J]. Corros. Sci. Prot. Technol., 2015, 27: 90
|
| 48 |
曾佳俊, 周学杰, 吴 军 等. 金属材料大气腐蚀试验相关性与寿命预测研究现状 [J]. 腐蚀科学与防护技术, 2015, 27: 90
|
| 49 |
Chen R. The evaluation of atmospheric corrosivity category and corrosion behavior among China Southern Power Grid region-Guangxi Zhuang Autonomous region and Guizhou Province [D]. Guangzhou: South China University of Technology, 2018
|
| 49 |
陈 日. 南方电网地区输电杆塔大气腐蚀等级评估及腐蚀行为研究-广西壮族自治区和贵州省 [D]. 广州: 华南理工大学, 2018
|
| 50 |
Ma X Y, Qu Z Y, Li C R. Application of BP artifical nural network for prediction of atmosphere corrosion of carbonsteel and low alloy steel [J]. Corros. Sci. Prot. Technol., 2002, 14: 52
|
| 50 |
马小彦, 屈祖玉, 李长荣. BP神经网络在碳钢及低合金钢大气腐蚀预测中的应用 [J]. 腐蚀科学与防护技术, 2002, 14: 52
|
| 51 |
Wang H T, Han E-H, Ke W. Gray model and gray relation analysis for atmospheric corrosion of carbon steel and low alloy steel [J]. Corros. Sci. Prot. Technol., 2006, 18: 278
|
| 51 |
王海涛, 韩恩厚, 柯 伟. 碳钢、低合金钢大气腐蚀的灰色模型预测及灰色关联分析 [J]. 腐蚀科学与防护技术, 2006, 18: 278
|
| 52 |
Ji Z G, Ma X B, Zhou K, et al. An improved atmospheric corrosion prediction model considering various environmental factors [J]. Corrosion, 2021, 77: 1178
doi: 10.5006/3879
|
| 53 |
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
doi: 10.1016/j.corsci.2020.109084
|
| 54 |
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
doi: 10.1038/s41529-021-00211-3
|
| 55 |
Han D S, Li D. Forecast of atmosphere corrosion for aluminum alloys by RBF neural network [J]. Corros. Sci. Prot. Technol., 2009, 21: 245
|
| 55 |
韩德盛, 李 荻. 用RBF人工神经网络构建铝合金大气腐蚀预测模型 [J]. 腐蚀科学与防护技术, 2009, 21: 245
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