Predicting the rheology of limestone calcined clay cements (LC3): Linking composition and hydration kinetics to yield stress through Machine Learning

The physicochemical characteristics of calcined clay influence yield stress of limestone calcined clay cements (LC3), but the independent influences the clay's physical and chemical characteristics as well as the effect of other variables on LC3 rheology are less well-understood. Further, a relationship between LC3 hydration kinetics and yield stress – important for informing mixture design – has not yet been established. Here, rheological properties were determined in pastes with varying water-to-solid ratio (w/s), constituent mass ratios (PC:metakaolin:limestone), limestone particle size and gypsum content. From these data, an ML model developed allowed the independent examination of the different mechanisms by which metakaolin fraction influences yield stress of LC3, identifying four predictors – packing index, Al2O3/SO3, total particle density and metakaolin fraction relative to limestone (MK/LS) – most significant for predicting LC3 yield stress. A methodology based on kernel smoothing also identified hydration kinetics parameters best correlated with yield stress.