Influence of aggregate characteristics on workability and rheology of self-compacting concrete

The workability and rheological behavior of self-compacting concrete (SCC) are significantly influenced by aggregate characteristics, yet existing studies shows a need of a unified quantitative framework for predicting these effects. This study introduces Coarse Aggregate Angularity Texture (CAAT) index, which integrates aggregate angularity and texture to provide a comprehensive metric for assessing rheological behavior. Four types of coarse aggregates with distinct morphological characteristics were analyzed using the Aggregate Image Measurement System (AIMS), and experiments were conducted to investigate their influence on rheology of SCC. Results demonstrated that individual aggregate properties alone could not reliably predict SCC rheology; however, the CAAT index has demonstrated strong linear correlations (R² > 0.92) with slump flow, T500, static yield stress, and plastic viscosity. Additionally, the influence of excess mortar layer thickness was quantified in this study based on the change of sand ratio: the impact of sand ratio on SCC rheology followed a nonlinear trend, with an optimal ratio improving flowability but excessive ratio leading to increased viscosity, demonstrating the role of sand ratio in determining SCC flowability. Furthermore, a robust time-dependent mathematical relationship (R² > 0.95) was developed between SCC rheological parameters and time. Based on the findings from experiments, predictive models integrating CAAT, excess mortar layer thickness, and time was proposed, with accuracy falling within a 95 % prediction interval. This study offers a new quantitative approach to predict the SCC rheology and workability, which is able to provide recommendations on improved mix design and performance prediction of SCC in engineering applications. ● CAAT index is closely related with rheological parameters of SCC. ● Increasing excess mortar layer thickness improves rheology of SCC. ● Developed SCC rheology model has predictions within 95 % prediction interval.