Prediction of Early Compressive Strength of Ultrahigh-Performance Concrete Using Machine Learning Methods

Research output: Contribution to journal › Article › Research › peer review

Authors

  • Hailiang Zhu
  • Xiong Wu
  • Yaoling Luo
  • Yue Jia
  • Chong Wang
  • Zheng Fang
  • Xiaoying Zhuang
  • Shuai Zhou

Research Organisations

External Research Organisations

  • Chongqing University
  • Shanghai Tunnel Engineering Company Ltd.
  • Wuhan University of Technology
  • China West Construction Academy of Building Materials
  • Tongji University
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Details

Original languageEnglish
Article number2141023
Number of pages23
JournalInternational Journal of Computational Methods
Volume20
Issue number8
Early online date8 Mar 2022
Publication statusPublished - 1 Oct 2023

Abstract

In this study, a new prediction model is proposed to predict the 7-day compressive strength of ultrahigh-performance concrete (UHPC) with different mix proportions using artificial neural network (ANN) and support vector machine (SVM). The predicted results are compared with the experimental results to verify the proposed model. Then, the importance of each component and the sensitivity of parameters are investigated. The research proves that the proposed model can estimate the 7-day compressive strength of UHPC based on the mix proportions.

Keywords

    7-day compressive strength, ANN, prediction, SVM, UHPC

ASJC Scopus subject areas

Cite this

Prediction of Early Compressive Strength of Ultrahigh-Performance Concrete Using Machine Learning Methods. / Zhu, Hailiang; Wu, Xiong; Luo, Yaoling et al.
In: International Journal of Computational Methods, Vol. 20, No. 8, 2141023, 01.10.2023.

Research output: Contribution to journal › Article › Research › peer review

Zhu H, Wu X, Luo Y, Jia Y, Wang C, Fang Z et al. Prediction of Early Compressive Strength of Ultrahigh-Performance Concrete Using Machine Learning Methods. International Journal of Computational Methods. 2023 Oct 1;20(8):2141023. Epub 2022 Mar 8. doi: 10.1142/S0219876221410231
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