Computers and Concrete

  • 제3권4호
  • /
  • Pages.249-260
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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

Time dependent equations for the compressive strength of self-consolidating concrete through statistical optimization

  • Hossain, K.M.A. (Department of Civil Engineering, Ryerson University) ;
  • Lachemi, M. (Department of Civil Engineering, Ryerson University)
  • 투고 : 2006.07.18
  • 심사 : 2006.08.21
  • 발행 : 2006.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

Self-consolidating concrete (SCC) in the fresh state is known for its excellent deformability, high resistance to segregation, and use, without applying vibration, in congested reinforced concrete structures characterized by difficult casting conditions. Such a concrete can be obtained by incorporating either mineral or chemical admixtures. This paper presents the results of an investigation to asses the applicability of Abram's law in predicting the compressive strength of SCC to any given age. Abram's law is based on the assumption that the strength of concrete with a specific type of aggregate at given age cured at a prescribed temperature depends primarily on the water-to-cement ratio (W/C). It is doubtful that such W/C law is applicable to concrete mixes with mineral or chemical admixtures as is the case for SCC where water to binder ratio (W/B) is used instead of W/C as the basis for mix design. Strength data of various types of SCC mixtures is collected from different sources to check the performance of Abram's law. An attempt has been made to generalize Abram's law by using various optimization methodologies on collected strength data of various SCC mixtures. A set of generalized equations is developed for the prediction of SCC strength at various ages. The performance of generalized equations is found better than original Abram's equations.

키워드

참고문헌

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  1. Self compacting reinforced concrete beams strengthened with natural fiber under cyclic loading vol.17, pp.5, 2016, https://doi.org/10.12989/cac.2016.17.5.597
  2. Predicting strength of SCC using artificial neural network and multivariable regression analysis vol.20, pp.1, 2006, https://doi.org/10.12989/cac.2017.20.1.031
  3. Case-based reasoning approach to estimating the strength of sustainable concrete vol.20, pp.6, 2017, https://doi.org/10.12989/cac.2017.20.6.645