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Prediction of the Rheological Properties of Fresh Cementitious Suspensions Considering Microstructural Parameters
Sam Rajadurai Rajagopalan1, Bang-Yeon Lee2, Su-Tae Kang1
1Department of Civil Engineering, Daegu University, 201 Daegudae-ro, Jillyang, Gyeongsan 38453, Korea.
This study investigated the rheological properties of supplementary cementitious materials (SCMs) like fly ash and silica fume. Predictive models accurately estimated yield stress and plastic viscosity, advancing cementitious material science.
Area of Science:
- Materials Science
- Civil Engineering
- Rheology
Background:
- Supplementary cementitious materials (SCMs) are crucial for modifying cement properties.
- Understanding individual SCM rheology is vital for predicting blended mix behavior.
- Limited research exists on SCM rheological properties across various solid fractions and predictive models.
Purpose of the Study:
- To investigate the rheological properties of non-blended cementitious suspensions (Portland cement, fly ash, blast-furnace slag, silica fume).
- To predict yield stress using YODEL and plastic viscosity using Krieger-Dougherty's equation.
- To evaluate the accuracy of these models against experimental data.
Main Methods:
- Experimental investigation of rheological properties for PC, FA, BS, and SF suspensions.
- Systematic variation of solid volume fractions (ϕ) from 0.28 to 0.44.
- Prediction of rheological parameters using YODEL and Krieger-Dougherty's equation.
Main Results:
- Rheological properties increased with solid volume fraction (ϕ), indicating better flowability at lower ϕ.
- YODEL accurately predicted yield stress (R² > 0.96) based on microstructural parameters.
- Krieger-Dougherty's equation accurately predicted plastic viscosity (R² > 0.94) using particle characteristics.
Conclusions:
- Both YODEL and Krieger-Dougherty's equation provide accurate, quantitative predictions without fitting parameters.
- These models are applicable to multimodal powder suspensions, enhancing cementitious material design.
- The study advances the understanding of SCM rheology and its predictive modeling.
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