Steel corrosion prediction based on support vector machines

Ya jun Lv, Jun wei Wang, Julian Jia liang Wang, Cheng Xiong, Liang Zou, Ly Li, Da wang Li

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number109807
JournalChaos, Solitons and Fractals
Volume136
DOIs
StatePublished - Jul 2020

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Mathematics(all)
  • Physics and Astronomy(all)
  • Applied Mathematics

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