Zhiwei QU

Mr. Zhiwei QU

UNSW Sydney, Sydney, New South Wales, Australia
orcid

Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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21 April 2022 in Materials
Verified
Journal Article
Open access
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ZQZhiwei QU
Zhiwei QU
ZLZIHAO LIU
ZIHAO LIU
Ruizhe SiRuizhe Si
Ruizhe Si
YZYingda Zhang
Yingda Zhang
Concrete is known as the most globally used construction material, but it releases a huge amount of greenhouse gases due to cement production. Recently, Supplementary Cementitious Materials (SCMs) such as fly ash and Ground Granulated Blast Furnace Slag (GGBFS) have been widely used in concrete to reduce the cement content. However, SCMs can alter the mechanical properties and time-dependent behaviors of concrete and the early age mechanical properties of concrete significantly affect the concrete cracking in the engineering field. Therefore, evaluation of the development of the mechanical properties of SCMs-based concrete is vital. In this paper, the time development of mechanical properties of concrete mixes with various fly ash and GGBFS was experimentally investigated. Four different cement replacement levels including 0%, 20%, 30%, and 40% by fly ash and GGBFS as well as ternary binders were considered. Compressive strength, splitting tensile strength, flexural strength, and elastic modulus of concrete were measured until 28 days. Three additional concrete mixes with ternary binders were also cast to investigate the early-age autogenous shrinkage development until 28 days. In addition, prediction models in existing standards were used and compared to experimental results. The comparison results showed that the prediction models overestimated the compressive strength but underestimated the splitting tensile strength development and autogenous shrinkage. As a result, a model capturing the effect of fly ash and GGBFS on the development of compressive and splitting tensile strength is proposed to improve the prediction accuracy for current standards and empirical models.