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To promote the sustainable development of eco-efficient calcium sulfoaluminate (CSA) cements through the partial replacement of the CSA clinker with supplementary cementitious waste products, the effects of coal fly ashes on the early-age and mature-age properties of a calcium sulfoaluminate (CSA)-based cement paste were investigated. The impacts of both Class C and Class F fly ashes on the rheological properties, hydration kinetics, and compressive strength development of CSA cement paste were studied. Rheology-based workability parameters, representing the rate of loss of flowability, the rate of hardening, and the placement limit, were characterized for the pastes prepared with fixed water-to-cement (w/c) and fixed water-to-binder (w/b) ratios. The results indicate a slight improvement in the workability of the CSA paste by fly ash addition at a fixed w/b ratio. The isothermal calorimetry studies show a higher heat of hydration for the Class C fly ash-modified systems compared to the Class F-modified systems. The results show that fly ash accelerates the hydration of the calcium sulfoaluminate cement pastes, chiefly due to the filler effects, rather than the pozzolanic effects. In general, ettringite is stabilized more by the addition of Class F fly ash than Class C fly ash. Both fly ashes reduced the 1-day compressive strength, but increased the 28-day strength of the CSA cement paste; meanwhile, the Class C modified pastes show a higher strength than Class F, which is attributed to the higher degree of reaction and potentially more cohesive binding C-S-H-based gels formed in the Class C fly ash modified systems. The results provide insights that support that fly ash can be employed to improve the performance of calcium sulfoaluminate cement pastes, while also enhancing cost effectiveness and sustainability. 

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.