Journal of the Korea institute for structural maintenance and inspection (한국구조물진단유지관리공학회 논문집)

Korea Institute for Structural Maintenance Inspection (한국구조물진단유지관리공학회)
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Prediction of Rheological Properties of Cement-Based Pastes Considering the Particle Properties of Binders

결합재의 입자특성을 고려한 시멘트 기반 2성분계 페이스트의 유변특성 예측

  • 최은석 (대구대학교 토목공학과) ;
  • 이준우 (대구대학교 토목공학과) ;
  • 강수태 (대구대학교 건축공학과)
  • Received : 2023.10.30
  • Accepted : 2023.11.28
  • Published : 2023.12.31
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Abstract

Recently, a variety of new cement-based materials have been developed, and attempts to predict the properties of these new materials are increasing. In this study, we aimed to predict the rheological properties of binary blended pastes. The cementitious materials used in the study included Portland cement (PC), fly ash (FA), blast furnace slag (BS), and silica fume (SF). The three binder components, fly ash, blast furnace slag, and silica fume, were blended with cement as the foundational composition. We predicted the yield stress and plastic viscosity of the pastes using the YODEL (Yield stress mODEL) and Krieger-Dougherty's equation. The predictive model's performance was validated by comparing it with experimental results obtained using a rheometer. When the rheological properties of the binary blended paste were predicted by reconstructing the properties and parameters used to predict the individual materials, it was evident that the predictions made using the proposed method closely matched the experimental results.

최근 다양한 시멘트 기반 재료들이 새롭게 개발되고 있으며, 이에 새롭게 개발된 재료들의 물성을 예측하려는 시도도 증가하고 있다. 본 연구에서는 시멘트계 재료로 구성된 2성분계 페이스트의 유변특성을 정량적으로 예측하고자 하였다. 사용된 시멘트계 재료는 포틀랜드 시멘트(PC), 플라이애시(FA), 고로슬래그(BS), 실리카퓸(SF)이 사용되었으며, 시멘트를 기반으로 나머지 3가지의 결합재를 혼합하였다. 페이스트의 항복응력과 소성점도는 각각 YODEL(Yield stress mODEL)과 Krieger-Dougherty's equation을 사용하여 예측하였으며, Rheometer를 사용한 실험 결과를 통해 예측 모델의 성능을 확인하였다. 개별 재료의 예측에 사용된 물성과 매개변수를 재구성하여 2성분계 페이스트의 유변특성을 예측하였을 때, 제안된 방법에 따른 예측의 경향이 실험 결과와 잘 일치하는 것으로 나타났다.

Keywords

Acknowledgement

이 논문은 2023년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구이며(No.RS-2023-00251506), 또한 2020년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No.2020R1F1A1049695).

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