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A novel internal conditioning (InCon) technique based on saturated sodium montmorillonite (sMT) functionalized with two non-ionic surfactants, polyoxyethylene (9) nonylphenylether and t-octyl phenoxy poly ethoxyethanol, is investigated in this study. With the integration of water for internal curing and pozzolanic reactivity in a single system, the role of InCon in modifying cement hydration kinetics is comprehensively elucidated. The results indicate that, in the presence of InCon, both silicate reaction and secondary aluminate reaction rates are enhanced, and the apparent activation energy (Ea) of cement hydration was decreased from 34.3 KJ/mol to 28.7 KJ/mol indicating a lower temperature sensitivity and threshold of the cement hydration reactions. In addition, decreased CH contents, improved degree of hydration, increased chemical shrinkage, and the formation of additional Csingle bondSsingle bondH and aluminum-containing phases were obtained from the cement with InCon. The autogenous shrinkage of cement and the negative impact of dry sMT on the early age strength of cement can be offset by InCon paving a new path to improve the overall properties of concrete.
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Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems
Abstract 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.
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- 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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