Computers and Concrete

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A model to characterize the effect of particle size of fly ash on the mechanical properties of concrete by the grey multiple linear regression

  • Cui, Yunpeng (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Liu, Jun (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Wang, Licheng (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Liu, Runqing (School of Materials Science and Engineering, Shenyang Ligong University) ;
  • Pang, Bo (Faculty of Infrastructure Engineering, Dalian University of Technology)
  • Received : 2020.02.10
  • Accepted : 2020.07.28
  • Published : 2020.08.25
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Abstract

Fly ash has become an important component of concrete as supplementary cementitious material with the development of concrete technology. To make use of fly ash efficiently, four types of fly ash with particle size distributions that are in conformity with four functions, namely, S.Tsivilis, Andersen, Normal and F distribution, respectively, were prepared. The four particle size distributions as functions of the strength and pore structure of concrete were thereafter constructed and investigated. The results showed that the compressive and flexural strength of concrete with the fly ash that conforming to S.Tsivilis, Normal, F distribution increased by 5-10 MPa and 1-2 MPa, respectively, compared to the reference sample at 28 d. The pore structure of the concrete was improved, in which the total porosity of concrete decreased by 2-5% at 28 d. With regarding to the fly ash with Andersen distribution, it was however not conducive to the strength development of concrete. Regression model based on the grey multiple linear regression theory was proved to be efficient to predict the strength of concrete, according to the characteristic parameters of particle size and pore structure of the fly ash.

Keywords

Acknowledgement

The authors would like to acknowledge for the financial support by the Key Research and Development Projects of the National 13th Five-Year, (No. 2018YFD1101001)"; National Natural Science Foundation of China, (No. 51972214); Youth Program of National Natural Science Foundation of China, (No. 51902212); Innovation Talents Support Program for Young and Middle-aged People in Shenyang(No. RC190374); Liaoning innovation team support (No. LT2019012); Young Top Talents of Liaoning Province (No. XLYC 1807096).

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