Computers and Concrete

  • 제3권5호
  • /
  • Pages.357-373
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  • 2006
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A mortar mix proportion design algorithm based on artificial neural networks

  • Ji, Tao (College of Civil Engineering, Fuzhou University) ;
  • Lin, Xu Jian (College of Civil Engineering, Fuzhou University)
  • 투고 : 2006.03.22
  • 심사 : 2006.08.28
  • 발행 : 2006.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

The concepts of four parameters of nominal water-cement ratio, equivalent water-cement ratio, average paste thickness, fly ash-binder ratio were introduced. It was verified that the four parameters and the mix proportion of mortar can be transformed each other. The behaviors (strength, workability, et al.) of mortar primarily determined by the mix proportion of mortar now depend on the four parameters. The prediction models of strength and workability of mortar were built based on artificial neural networks (ANNs). The calculation models of average paste thickness and equivalent water-cement ratio of mortar can be obtained by the reversal deduction of the two prediction models, respectively. A mortar mix proportion design algorithm was proposed. The proposed mortar mix proportion design algorithm is expected to reduce the number of trial and error, save cost, laborers and time.

키워드

참고문헌

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피인용 문헌

  1. Prediction of workability of concrete using design of experiments for mixtures vol.5, pp.1, 2008, https://doi.org/10.12989/cac.2008.5.1.001
  2. Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete vol.25, pp.8, 2011, https://doi.org/10.1016/j.conbuildmat.2011.03.040
  3. A comparative modeling study to estimate wear of concrete vol.24, pp.3-4, 2014, https://doi.org/10.1007/s00521-012-1277-7
  4. Modeling of Thermal Conductivity of Concrete with Vermiculite by Using Artificial Neural Networks Approaches vol.26, pp.4, 2013, https://doi.org/10.1080/08916152.2012.669810
  5. Modeling properties of self-compacting concrete: support vector machines approach vol.5, pp.5, 2008, https://doi.org/10.12989/cac.2008.5.5.461