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

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Autoren

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

Organisationseinheiten

Externe Organisationen

  • Chongqing University
  • Shanghai Tunnel Engineering Company Ltd.
  • Wuhan University of Technology
  • China West Construction Academy of Building Materials
  • Tongji University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Aufsatznummer2141023
Seitenumfang23
FachzeitschriftInternational Journal of Computational Methods
Jahrgang20
Ausgabenummer8
Frühes Online-Datum8 März 2022
PublikationsstatusVeröffentlicht - 1 Okt. 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.

ASJC Scopus Sachgebiete

Zitieren

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, Jahrgang 20, Nr. 8, 2141023, 01.10.2023.

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › 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 Okt 1;20(8):2141023. Epub 2022 Mär 8. doi: 10.1142/S0219876221410231
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AU - Fang, Zheng

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