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Steel corrosion prediction based on support vector machines

Author

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

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.

Suggested Citation

  • Lv, Ya-jun & Wang, Jun-wei & Wang, Julian & Xiong, Cheng & Zou, Liang & Li, Ly & Li, Da-wang, 2020. "Steel corrosion prediction based on support vector machines," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    • Handle: RePEc:eee:chsofr:v:136:y:2020:i:c:s0960077920302083
    DOI: 10.1016/j.chaos.2020.109807
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    References listed on IDEAS

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    1. Stewart, Mark G. & Al-Harthy, Ali, 2008. "Pitting corrosion and structural reliability of corroding RC structures: Experimental data and probabilistic analysis," Reliability Engineering and System Safety, Elsevier, vol. 93(3), pages 373-382.
    2. Marouani, H. & Fouad, Y., 2019. "Particle swarm optimization performance for fitting of Lévy noise data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 708-714.
    3. Cao, Guohua & Wu, Lijuan, 2016. "Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting," Energy, Elsevier, vol. 115(P1), pages 734-745.
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    Cited by:

    1. Zhaoyi Cui & Xiuli Geng, 2021. "Product Service System Configuration Based on a PCA-QPSO-SVM Model," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    2. Yang, Jianfeng & Suo, Guanyu & Chen, Liangchao & Dou, Zhan & Hu, Yuanhao, 2023. "Prediction method of key corrosion state parameters in refining process based on multi-source data," Energy, Elsevier, vol. 263(PA).
    3. Cheng, Min-Yuan & Cao, Minh-Tu & Herianto, Jason Ghorman, 2020. "Symbiotic organisms search-optimized deep learning technique for mapping construction cash flow considering complexity of project," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).

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