Machine learning for high-fidelity prediction of cement hydration kinetics in blended systems

Abstract

The production of ordinary Portland cement (OPC), the most broadly utilized man-made material, has been scrutinized due to its contributions to global anthropogenic CO2 emissions. Thus — to mitigate CO2 emissions — mineral additives have been promulgated as partial replacements for OPC. However, additives — depending on their physiochemical characteristics — can exert varying effects on OPC’s hydration kinetics. Therefore — in regards to more complex systems — it is infeasible for semi-empirical kinetic models to reveal the underlying nonlinear composition-property (i.e., reactivity) relationships. In the past decade or so, machine learning (ML) has arisen as a promising, holistic approach to predict the properties of heterogeneous materials, even without an across-the-board comprehension of the underlying composition-properties correlations. This paper describes the use of a Random Forests (RF) model to enable high-fidelity predictions of time-dependent hydration kinetics of OPC-based systems — more specifically [OPC + mineral additive(s)] systems — using the system’s physiochemical attributes as inputs. Results show that the RF model can also be used to formulate mixture designs that satisfy user-imposed kinetics-related criteria. Lastly, the presented results can be expanded to formulate mixture designs that satisfy target kinetic criteria, even without knowledge of the underlying kinetic mechanisms.

Keywords

• Machine learning
• Random forests
• Portland cement
• Hydration
• Mineral additives