Steel corrosion prediction based on support vector machines

被引：37

作者：

Lv, Ya-jun ^{[1
,2
]}

Wang, Jun-wei ^{[3
]}

Wang, Julian ^{[4
]}

Xiong, Cheng ^{[1
,2
]}

Zou, Liang ^{[1
,2
]}

Li, Ly ^{[1
,2
]}

Li, Da-wang ^{[1
,2
]}

机构：

[1] Shenzhen Univ, Dept Civil & Transportat Engn, Shenzhen 518060, Peoples R China

[2] Guangdong Prov Key Lab Durabil Marine Civil Engn, Shenzhen 518060, Peoples R China

[3] Henan Univ Technol, Dept Civil & Architectural Engn, Zhengzhou 450001, Peoples R China

[4] Penn State Univ, Dept Architectural Engn, University Pk, PA 16802 USA

来源：

CHAOS SOLITONS & FRACTALS | 2020年 / 136卷

基金：

中国国家自然科学基金;

关键词：

PARTICLE SWARM OPTIMIZATION; PITTING CORROSION; ALGORITHM; CONCRETE; REINFORCEMENT; PERFORMANCE; BEHAVIOR; REBARS; BARS; LOCALIZATION;

D O I：

10.1016/j.chaos.2020.109807

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, the 3D coordinate data of the corrosion condition of rebar are obtained by a 3D scanning method. Seven numerical parameters, such as the roundness, the section roughness, the inscribed circle radius/circumscribed circle radius and the eccentricity, are obtained by the numerical calculation method. These seven parameters are used to characterize the cross-section morphology of rusted steel bars. The particle swarm optimization support vector machine (PSO-SVM) and the grid search support vector machine (GS-SVM) are used to calculate these seven cross-section digitization parameters to predict the sectional corrosion rate of steel. This work concluded that these two optimization support vector machine (SVM) methods can accurately predict the sectional corrosion rate of steel. Compared with the GS-SVM model, the PSO-SVM steel corrosion prediction model is more accurate. © 2020

引用

页数：10

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