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Prediction of Chloride Diffusion Coefficient in Concrete Based on Machine Learning and Virtual Sample Algorithm

Author

Listed:
  • Fei-Yu Zhou

    (College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Ning-Jing Tao

    (College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yu-Rong Zhang

    (College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China
    Zhejiang Key Laboratory of Civil Engineering Structures and Disaster Prevention and Mitigation Technology, Hangzhou 310023, China)

  • Wei-Bin Yuan

    (College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310023, China
    Zhejiang Key Laboratory of Civil Engineering Structures and Disaster Prevention and Mitigation Technology, Hangzhou 310023, China)

Abstract

The durability degradation of reinforced concrete was mainly caused by chloride ingress. Former studies have used component parameters of concrete to predict chloride diffusion by machine learning (ML), but the relationship between microstructure and macroparameter of concrete need to be further clarified. In this study, multi-layer perceptron (MLP) and support vector machine (SVM) were used to establish the prediction model for chloride diffusion coefficient in concrete, especially for the solid waste concrete. A database of concrete pore parameters and chloride diffusion coefficients was generated by the algorithm based on the Gaussian mixture model (GMM-VSG). It is shown that both MLP and SVM could make good predictions, in which the data using the normalization preprocessing method was more suitable for the MLP model, and the data using the standardization preprocessing method was more adapted to the SVM model.

Suggested Citation

  • Fei-Yu Zhou & Ning-Jing Tao & Yu-Rong Zhang & Wei-Bin Yuan, 2023. "Prediction of Chloride Diffusion Coefficient in Concrete Based on Machine Learning and Virtual Sample Algorithm," Sustainability, MDPI, vol. 15(24), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16896-:d:1301267
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    References listed on IDEAS

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    1. Gong, Hong-Fei & Chen, Zhong-Sheng & Zhu, Qun-Xiong & He, Yan-Lin, 2017. "A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries," Applied Energy, Elsevier, vol. 197(C), pages 405-415.
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