Computers and Concrete

  • 제24권1호
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  • Pages.7-17
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  • 2019
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Applied linear and nonlinear statistical models for evaluating strength of Geopolymer concrete

  • Prem, Prabhat Ranjan (CSIR-Structural Engineering Research Centre) ;
  • Thirumalaiselvi, A. (CSIR-Structural Engineering Research Centre) ;
  • Verma, Mohit (CSIR-Structural Engineering Research Centre)
  • 투고 : 2018.11.05
  • 심사 : 2019.04.30
  • 발행 : 2019.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.

키워드

참고문헌

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