Structural Engineering and Mechanics

  • 제86권2호
  • /
  • Pages.181-195
  • /
  • 2023
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  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Development of an integrated machine learning model for rheological behaviours and compressive strength prediction of self-compacting concrete incorporating environmental-friendly materials

  • Pouryan Hadi (Department of Civil Engineering, Urmia Branch, Islamic Azad University) ;
  • KhodaBandehLou Ashkan (Department of Civil Engineering, Urmia Branch, Islamic Azad University) ;
  • Hamidi Peyman (Department of Civil Engineering, Urmia Branch, Islamic Azad University) ;
  • Ashrafzadeh Fedra (Department of Civil Engineering, Urmia Branch, Islamic Azad University)
  • 투고 : 2022.09.25
  • 심사 : 2023.03.10
  • 발행 : 2023.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

To predict the rheological behaviours along with the compressive strength of self-compacting concrete that incorporates environmentally friendly ingredients as cement substitutes, a comparative evaluation of machine learning methods is conducted. To model four parameters, slump flow diameter, L-box ratio, V-funnel time, as well as compressive strength at 28 days-a complete mix design dataset from available pieces of literature is gathered and used to construct the suggested machine learning standards, SVM, MARS, and Mp5-MT. Six input variables-the amount of binder, the percentage of SCMs, the proportion of water to the binder, the amount of fine and coarse aggregates, and the amount of superplasticizer are grouped in a particular pattern. For optimizing the hyper-parameters of the MARS model with the lowest possible prediction error, a gravitational search algorithm (GSA) is required. In terms of the correlation coefficient for modelling slump flow diameter, L-box ratio, V-funnel duration, and compressive strength, the prediction results showed that MARS combined with GSA could improve the accuracy of the solo MARS model with 1.35%, 11.1%, 2.3%, as well as 1.07%. By contrast, Mp5-MT often demonstrates greater identification capability and more accurate prediction in comparison to MARS-GSA, and it may be regarded as an efficient approach to forecasting the rheological behaviors and compressive strength of SCC in infrastructure practice.

키워드

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