Steel corrosion prediction based on support vector machines
| Autor: | Cheng Xiong, Da wang Li, Ya jun Lv, Julian Jia liang Wang, Liang Zou, Junwei Wang, Ly Li |
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| Rok vydání: | 2020 |
| Předmět: |
business.industry
General Mathematics Applied Mathematics Rebar General Physics and Astronomy Particle swarm optimization Statistical and Nonlinear Physics Surface finish Structural engineering 01 natural sciences Roundness (object) 010305 fluids & plasmas Corrosion law.invention Support vector machine law 0103 physical sciences Hyperparameter optimization Circumscribed circle business 010301 acoustics Mathematics |
| Zdroj: | Chaos, Solitons & Fractals. 136:109807 |
| ISSN: | 0960-0779 |
| DOI: | 10.1016/j.chaos.2020.109807 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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