Computers and Concrete

  • 제30권1호
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  • Pages.33-42
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  • 2022
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Machine learning models for predicting the compressive strength of concrete containing nano silica

  • Garg, Aman (Department of Aerospace Engineering, Indian Institute of Technology Kanpur) ;
  • Aggarwal, Paratibha (Department of Civil Engineering, National Institute of Technology Kurukshetra) ;
  • Aggarwal, Yogesh (Department of Civil Engineering, National Institute of Technology Kurukshetra) ;
  • Belarbi, M.O. (Laboratoire de Recherche en Genie Civil, LRGC. Universite de Biskra) ;
  • Chalak, H.D. (Department of Civil Engineering, National Institute of Technology Kurukshetra) ;
  • Tounsi, Abdelouahed (YFL (Yonsei Frontier Lab), Yonsei University) ;
  • Gulia, Reeta (Department of Civil Engineering, DPG Institute of Technology and Management)
  • 투고 : 2022.01.20
  • 심사 : 2022.06.07
  • 발행 : 2022.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a time-consuming and laborious process. The present study aims to propose surrogate models based on Support Vector Machine (SVM) and Gaussian Process Regression (GPR) machine learning techniques, which can predict the CS of concrete containing nano-silica. Content of cement, aggregates, nano-silica and its fineness, water-binder ratio, and the days at which strength has to be predicted are the input variables. The efficiency of the models is compared in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE), Variance Account For (VAF), Nash-Sutcliffe Efficiency (NSE), and RMSE to observation's standard deviation ratio (RSR). It has been observed that the SVM outperforms GPR in predicting the CS of the concrete containing nano-silica.

키워드

과제정보

The research work presented in the present article receives no funding or grant in any form.

참고문헌

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