PREDICTION OF REMAINING STRENGTH OF CORRODED PIPELINES BASED ON IMPROVED BP ALGORITHM

J Chin Soc Corr Pro

2011, Vol. 31

Issue (5): 404-408 DOI:

Research Articles

Current Issue | Archive | Adv Search

PREDICTION OF REMAINING STRENGTH OF CORRODED PIPELINES BASED ON IMPROVED BP ALGORITHM

SUN Baocai, LI Shuxin, YU Shurong, ZENG Hailong

School of PetroChemical Engineering, Lanzhou University of Technology, Lanzhou 730050

Download:

PDF(539KB)
Export: BibTeX | EndNote (RIS)

Abstract The failure pressure of long-distance gas pipeline was predicted based on nonlinear mapping function of artificial neural network. The effects of pipe diameter, pipe wall thickness, material yield strength, radial corrosion rate, longitudinal corrosion rate, defect length and pit depth on the pipeline failure were analyzed comprehensively. In order to illustrate the generality of neural network, the network was trained using sample training set from six corroded pipelines with different diameters. The result showed that the neural network can be a more accurate and convenient method to predict pipeline failure.

Key words: corroded pipeline neural network failure pressure nonlinear mapping residual strength

Received: 08 July 2010

ZTFLH:	TE 988.2
	O211.9

	Service

	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	（）
	Articles by authors
	XUN Bao-Cai
	LI Shu-Xin
	ZENG Hai-Long
	YU Shu-Rong

Cite this article:

SUN Baocai, LI Shuxin, YU Shurong, ZENG Hailong. PREDICTION OF REMAINING STRENGTH OF CORRODED PIPELINES BASED ON IMPROVED BP ALGORITHM. J Chin Soc Corr Pro, 2011, 31(5): 404-408.

URL:

https://www.jcscp.org/EN/ OR https://www.jcscp.org/EN/Y2011/V31/I5/404

[1] Shuai J, Zhang C E, Chen F L. Comparison study on assessment methods for remaining strength of corroded pipeline [J]. Natur. Gas Ind.,2006, 11: 122-155

    (帅健, 张春娥, 陈福来．腐蚀管道剩余强度评价方法的对比研究 [J]. 天然气工业, 2006, 11: 122-155)

[2] Li S X, Zeng H L, Yu S R, et al. A method of probabilistic analysis for steel pipeline with correlated corrosion defects [J]. Corros.Sci.,2009, 51(12): 3050-3056

[3] Ahammed M, Melchers R E. Probabilistic analysis of pipelines subjected to pitting corrosion leak [J]. Eng.Struct., 1995, 17(2): 74-80

[4] Lian L X, Liu Y, Ye J W, et al. Prediction model for magnetic properties of Nd-Fe-Co-Zr-B alloys based on artificial neural network [J]. Acta. Metall. Sin., 2005, 41(5): 529-533

    (连利仙, 刘颖, 叶金文等. 基于人工神经网络的 Nd-Fe-Co-Zr-B系永磁合金磁性能的预测模型 [J]. 金属学报, 2005, 41(5): 529-533)

[5] Sinha S K, Pandey M D. Probabilistic neural network for reliability assessment of oil and gas pipelines [J]. Computer-Aid.Civil Infrastruct. Eng., 2002, 17(5): 320-329

[6] Silva R C C, Guerreiro J N C, Loula A F D. A study of pipe interacting corrosion defects using the FEM and neural networks [J]. Adv. Eng. Software. 2007, 38(11-12): 868-875

[7] Li T J, An W G. Corrodent conduit reliability analysis based on artificial neural network [J].Mech. Eng., 2005, 6: 106-107

    (李铁钧, 安伟光. 基于神经网络的腐蚀管道的可靠性分析 [J]. 机械工程师, 2005, 6: 106-107)

[8] ASME-B31G. A supplement of ASME B31G code for pressure piping, 1995, Manual for determining the remaining strength of corroded pipelines [S]. New York: American Society for Mechanical Engineers, 1995

[9] Cong S. Neural network theory and applications with MATLAB toolboxes (3rd Ed.) [M]. Hefei: University of Science and Technology of China Press, 2009

    (丛爽. 面向MATLAB工具箱的神经网络理论与应用(第3版) [M]. 合肥: 中国科学技术大学出版社, 2009)

[10] Yan P F, Zhang C S. Artificial neural networks and evolutionary computing(2nd Ed) [M]. Beijing: Tsinghua University Press, 2005

     (阎平凡, 张长水. 人工神经网络与模拟进化计算(第2版) [M]. 北京: 清华大学出版社, 2005)

[11] Zhou Y, Xu B L. Orthogonal method for training neural networks [J]. J. Nanjing Univ.(Nat. Sci.), 2001, 137(1): 72-78

     (周毅, 徐柏龄. 神经网络中的正交设计法研究 [J]. 南京大学学报(自然科学), 2001, 137(1): 72-78)

[1]	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.
[2]	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.
[3]	YANG Zhuanzhao,LIU Daoxin,ZHANG Xiaohua. Finite Elements Analysis and Experimental Verification of Residual Strength of Steel Pipe with Flat Bottom Corrosion Defect[J]. 中国腐蚀与防护学报, 2013, 33(4): 339-346.
[4]	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.
[5]	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.
[6]	;. 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 .
[7]	Haitao WANG; Enhou Han. PREDICTIVE MODEL FOR ATMOSPHERIC CORROSION OFALUMINIUM ALLOY BY ARTIFICIAL NEURAL NETWORK[J]. 中国腐蚀与防护学报, 2006, 26(5): 272-274 .
[8]	;. PREDICTIVE MODEL FOR ATMOSPHERIC CORROSION OFALUMINIUM ALLOY BY ARTIFICIAL NEURAL NETWORK[J]. 中国腐蚀与防护学报, 2006, 26(5): 275-281 .
[9]	DaZhao Yu; Yueliang Chen. Statistical Study on Corrosion Damage Distribution of Aircraft Structure Based on Neural Network[J]. 中国腐蚀与防护学报, 2006, 26(1): 19-21 .
[10]	Shouyan Wang; Shizhe Song. PREDICTIVE MODEL BASED ON WAVELET TRANSFORMATION AND ARTIFICIAL NEURAL NETWORK FOR ZINC ATMOSPHERIC CORROSION[J]. 中国腐蚀与防护学报, 2005, 25(5): 257-261 .
[11]	Zhiming Gao; Shizhe Song; Yunhai Xv. ELECTROCHEMICAL IMPEDANCE SPECTROSCOPY ANALYSIS OF COATING DETERIORATION PROCESS WITH KOHONEN NEURAL NETWORKS[J]. 中国腐蚀与防护学报, 2005, 25(2): 106-109 .
[12]	Xueqing Liu; Xiao Tang; Jia Wang. CORRELATION BETWEEN SEAWATER ENVIRONMENTAL FACTORS AND MARINE CORROSION RATE USING ARTIFICIAL NEURAL NETWORK ANALYSIS[J]. 中国腐蚀与防护学报, 2005, 25(1): 11-14 .
[13]	Xiaoming Tan. Prediction Model for Corrosion of Aluminum Alloys Based on Artificial Neural network and Analysis of the Precision[J]. 中国腐蚀与防护学报, 2004, 24(4): 218-221 .
[14]	Shouyan Wang. ON THE SPOT DETECTION AND EVALUATION OF UNDERGROUND PIPELINE COATINGS DEFECTS[J]. 中国腐蚀与防护学报, 2004, 24(2): 100-104 .
[15]	Dawei Cui; Leyun Lin; Yuehong Zhao. Prediction of Corrosion Behavior of Nonferrous Metals in Seawater with ANN[J]. 中国腐蚀与防护学报, 2004, 24(1): 29-32 .

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed