PREDICTING CORROSION RATE OF MILD STEEL IN SOIL BASED ON ARTIFICIAL NEURAL NETWORK

J Chin Soc Corr Pro

1996, Vol. 16

Issue (4): 307-310 DOI:

Current Issue | Archive | Adv Search

PREDICTING CORROSION RATE OF MILD STEEL IN SOIL BASED ON ARTIFICIAL NEURAL NETWORK

GUO Zhihu; XING Zhengliang; JIN Minghui; MENG Xialan (Huazhong University of Science and Technology; Wuhan 430074)

Download:

PDF(264KB)
Export: BibTeX | EndNote (RIS)

Abstract Artificial neural network, possessing learning and non-linear character, was applied to study corrosion of mild steel in soil. A neural network typically consists of many simple neurons like processing elements called "cell" or "nodes" that interact with other cells using numerical weighted connection. In this study,a neural network with 6-10-1 structure, namely 6 input nodes, 10 hidden layer nodes, 1 output node, was used. The learning algorithm was BP (Back-Propagation) algorithm. Corrosion tests of mild steel in soil were carried out with orthogonal test method, in which five corrosion factors, namely pH value,Cl-, H2O,SO_4~2- and Fe2+ content, were considered and test data of sixteen groups were obtained. These data were used as sample set to train neural network. The inputs of neural network were the five corrosion factors and test duration, the output of neural network was corrosion rate of steel in soil.The research results showed that soil corrosion rates of mild steel in 24 months could be predicted by the trained artificial neural network, and were basically in agreement with experimental data.

Key words: Neural network Weight Learning sample Soil corrosion

Received: 25 August 1996

	Service

	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	（）
	Articles by authors

Cite this article:

GUO Zhihu; XING Zhengliang; JIN Minghui; MENG Xialan (Huazhong University of Science and Technology; Wuhan 430074). PREDICTING CORROSION RATE OF MILD STEEL IN SOIL BASED ON ARTIFICIAL NEURAL NETWORK. J Chin Soc Corr Pro, 1996, 16(4): 307-310.

URL:

https://www.jcscp.org/EN/ OR https://www.jcscp.org/EN/Y1996/V16/I4/307

1郭稚孤,金名惠,桂修文,孟厦兰,邢政良,罗逸．腐蚀科学与防护技术,1995;7(3):2582尹红风,戴汝为．模式识别与人工智能,1990,3(1):13王强编著．地下金属管道的腐蚀与阴极保护,西宁:青海人民出版社,1984．404KingRA,MillerJDA,WakerleyDS．Br．Corros．J,1973,8(3):895郭稚弧,唐和清,张君华,张维宇,佐佐本和夫．广岛大学工学部研究报告,1992;40(1):516周建华．介质条件对碳钢在上壤中腐蚀速度的影响,华中理工大学化学系学士论文,1993

[1]	TANG Rongmao, ZHU Yichen, LIU Guangming, LIU Yongqiang, LIU Xin, PEI Feng. Gray Correlative Degree Analysis of Q235 Steel/conductive Concrete Corrosion in Three Typical Soil Environments[J]. 中国腐蚀与防护学报, 2021, 41(1): 110-116.
[2]	ZHU Yichen,LIU Guangming,LIU Xin,PEI Feng,TIAN Xu,SHI Chao. Investigation on Interrelation of Field Corrosion Test and Accelerated Corrosion Test of Grounding Materials in Red Soil Environment[J]. 中国腐蚀与防护学报, 2019, 39(6): 550-556.
[3]	Libao YU, Maocheng YAN, Binbin WANG, Yun SHU, Jin XU, Cheng SUN. Microbial Corrosion of Q235 Steel in Acidic Red Soil Environment[J]. 中国腐蚀与防护学报, 2018, 38(1): 10-17.
[4]	Shuangchen MA, Kunling JIAO, Linan ZHANG, Yao SUN, Wenlong WU, Xiaoni ZHANG. Corrosion Characteristics of Carbon Steel in High Temperature Gas Containing Ammonium Bisulfate and Ammonium Sulfate[J]. 中国腐蚀与防护学报, 2017, 37(6): 605-612.
[5]	Baohui GUO, Youxu QIU, Hailong LI. Application of Artificial Neural Network for Preparation Process of Ni-SiC Composite Coatings on Ti-Alloy TA15[J]. 中国腐蚀与防护学报, 2017, 37(4): 389-394.
[6]	Shuangchen MA,Yue DENG,Wenlong WU,Yu TAN,Linan ZHANG,Feng CHAI,Panpan SUN,Xiaoni ZHANG. Corrosion Characteristics of Downstream Metal Material of Boiler System in Solution of By-product Ammonium Bisulfate from SCR Denitrification[J]. 中国腐蚀与防护学报, 2016, 36(4): 335-342.
[7]	Tao HUANG,Xiaoping CHEN,Xiangdong WANG,Fengyi MI,Rui LIU,Xiangyang LI. Effect of pH Value on Corrosion Behavior of Q235 Steel in an Artificial Soil[J]. 中国腐蚀与防护学报, 2016, 36(1): 31-38.
[8]	Zhian DENG,Shuyi LI,Xiaokun LI,Shan WANG,Xiaojun WANG. A Prediction Method Based on Fuzzy Neural Network for Corrosion Rate of Marine Pipelines[J]. 中国腐蚀与防护学报, 2015, 35(6): 571-576.
[9]	YAO Huiping, YAN Maocheng, YANG Xu, SUN Cheng. Electrochemical Behavior of X80 Pipeline Steel in the Initial Stage of Corrosion in an Acidic Red Soil[J]. 中国腐蚀与防护学报, 2014, 34(5): 472-476.
[10]	SUN Baocai, LI Shuxin, YU Shurong, ZENG Hailong. PREDICTION OF REMAINING STRENGTH OF CORRODED PIPELINES BASED ON IMPROVED BP ALGORITHM[J]. 中国腐蚀与防护学报, 2011, 31(5): 404-408.
[11]	NIE Xianghui, LI Xiaogang, LI Yunlong, LI Jike, ZHANG Hongbo. ACCELERATION RATIOS AND DYNAMIC CORRELATION EXPERIMENTS ON THE CORROSION LOSS OF Q235 STEEL IN SEASHORE SOIL[J]. 中国腐蚀与防护学报, 2011, 31(3): 208-213.
[12]	WANG Chuan, WANG Zhenyao, WEI Wei, XIE Chenping, KE Wei. STATISTICAL ANALYSIS AND PREDICTIVE MODEL IN CORROSION RESEARCH[J]. 中国腐蚀与防护学报, 2010, 30(4): 306-312.
[13]	LUAN Ruipeng，BEN Kerong，XIAO Yuxing，TIAN Liye. A CAUSE-FACTORS EVALUATION FOR ATMOSPHERIC CORROSION OF STEELS BASED ON IMPROVED SIGMOID FUNCTION BP NEURAL NETWORK[J]. 中国腐蚀与防护学报, 2010, 30(3): 227-230.
[14]	HUANG Jiayi QIU Yubing GUO Xingpeng. CLUSTER ANALYSIS OF ELECTROCHEMICAL NOISE FOR X70 STEEL IN KU'ERLE SOIL[J]. 中国腐蚀与防护学报, 2009, 29(6): 453-458.
[15]	;. Gray Neural Network and Its Application to Forecasting Ocean-water Corrosion[J]. 中国腐蚀与防护学报, 2008, 28(4): 201-204 .

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed