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.
- Hydration,
- Machine Learning,
- Mineral Additives,
- Portland Cement,
- Random Forests
Available at: http://works.bepress.com/hongyan-ma/103/
The authors acknowledge financial support for this research provided by the UM system; the Federal Highway Administration (Award no: 693JJ31950021); the Leonard Wood Institute (LWI:W911NF-07-2-0062) and the National Science Foundation (NSFCMMI:1661609 and 1932690; and NSF-DMR: 2034856).