Early‐age hydration of cement is enhanced by slightly soluble mineral additives (ie, fillers, such as quartz and limestone). However, few studies have attempted to systematically compare the effects of different fillers on cementitious hydration rates, and none have quantified such effects using fillers with comparable, size‐classified particle size distributions (PSDs). This study examines the influence of size‐classified fillers [ie, limestone (CaCO3), quartz (SiO2), corundum (Al2O3), and rutile (TiO2)] on early‐age hydration kinetics of tricalcium silicate (C3S) using a combination of experimental methods, while also employing a modified phase boundary and nucleation and growth model. In prior studies, wherein fillers with broad PSDs were used, it has been reported that between quartz and limestone, the latter is a superior filler due to its ability to partake in anion‐exchange reactions with C‐S‐H. Contrary to prior investigations, this study shows that when size‐classified and
Carbonaceous (e.g., limestone) and aluminosilicate (e.g., calcined clay) mineral additives are routinely used to partially replace ordinary portland cement in concrete to alleviate its energy impact and carbon footprint. These mineral additives—depending on their physicochemical characteristics—alter the hydration behavior of cement; which, in turn, affects the evolution of microstructure of concrete, as well as the development of its properties (e.g., compressive strength). Numerical, reaction-kinetics models—e.g., phase boundary nucleation-and-growth models; which are based partly on theoretically-derived kinetic mechanisms, and partly on assumptions—are unable to produce a priori prediction of hydration kinetics of cement; especially in multicomponent systems, wherein chemical interactions among cement, water, and mineral additives occur concurrently. This paper introduces a machine learning-based methodology to enable prompt and high-fidelity prediction of time-dependent hydration kinetics of cement, both in plain and multicomponent (e.g., binary; and ternary) systems, using the system’s physicochemical characteristics as inputs. Based on a database comprising hydration kinetics profiles of 235 unique systems—encompassing 7 synthetic cements and three mineral additives with disparate physicochemical attributes—a random forests (RF) model was rigorously trained to establish the underlying composition-reactivity correlations. This training was subsequently leveraged by the RF model: to predict time-dependent hydration kinetics of cement in new, multicomponent systems; and to formulate optimal mixture designs that satisfy user-imposed kinetics criteria.
more » « less- NSF-PAR ID:
- 10214257
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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