Search for: All records

The hydration of the two most reactive phases of ordinary Portland cement (OPC), tricalcium silicate (C₃S), and tricalcium aluminate (C₃A) is successfully halted when the activity of water () falls below critical thresholds of 0.70 and 0.45, respectively. It has been established that the reduction in relative humidity (RH) and suppresses the hydration of all anhydrous phases in OPC, including less explored phases like dicalcium silicate, that is, belite (β‐C₂S). However, the degree of suppression, that is, the critical threshold, for β‐C₂S, standalone has yet to be established. This study utilizes isothermal microcalorimetry and X‐ray diffraction techniques to elucidate the influence ofon the hydration of‐C₂S suspensions via incremental replacements of water with isopropanol (IPA). Experimentally, this study shows that with increasing IPA replacements, hydration is increasingly suppressed until eventually brought to a halt at a critical threshold of approximately 27.7% IPA on a weight basis (wt.%_IPA). From thermodynamic estimations, the exact criticalthreshold and solubility product constant of‐C₂S () are established as 0.913 and 10^−12.68, respectively. This study enables enhanced understanding of β‐C₂S reactivity and provides thermodynamic parameters during the hydration of β‐C₂S‐containing cementitious systems such as OPC‐based and calcium aluminate‐based systems.

Ultrahigh temperature ceramics (UHTCs) have melting points above 3000°C and outstanding strength at high temperatures, thus making them apposite structural materials for high‐temperature applications. Di‐borides, nitride, and carbide compounds—processed via various techniques—have been extensively studied and used in the manufacture of UHTCs. Current analytical models, based on our current but incomplete understanding of the theory, are unable to produce a priori predictions of mechanical properties of UHTCs based on their mixture designs and processing parameters. As a result, researchers have to rely on experiments—which are often costly and time‐consuming—to understand composition–structure–performance links in UHTCs. This study employs machine learning (ML) models (i.e., random forest and artificial neural network models) to predict Young's modulus, flexural strength, and fracture toughness of UHTCs in relation to a wide range of mixture designs, processing parameters, and testing conditions. Outcomes demonstrate that adequately trained ML models can yield reliable predictions, a priori, of the three aforesaid mechanical properties. The prediction performance on Young's modulus is superior to flexural strength and fracture toughness. Next, the ML model with the best prediction performance is utilized to evaluate and rank the impacts of input variables on Young's modulus. Finally, on the basis of such classification of consequential and inconsequential input variables, this study develops an easy‐to‐use, closed‐form analytical model to predict Young's modulus of UHTCs. Overall, this study highlights the ability of data‐driven numerical models to complement, or even replace, time‐consuming experiments, thereby accelerating the development of UHTCs.

Alkali‐activated mortar (AAM) is an emerging eco‐friendly construction material, which can complement ordinary Portland cement (OPC) mortars. Prediction of properties of AAMs—albeit much needed to complement experiments—is difficult, owing to substantive batch‐to‐batch variations in physicochemical properties of their precursors (i.e., aluminosilicate and activator solution). In this study, a machine learning (ML) model is employed; and it is shown that the model—once trained and optimized—can reliably predict compressive strength of AAMs solely from their initial physicochemical attributes. Prediction performance of the model improves when multiple compositional descriptors of the aluminosilicate are combined into a singular, composite chemostructural descriptor (i.e.,network ratioandnumber of constraints); thus, reducing the degrees of freedom. Through interpretation of the ML model's outcomes—specifically the variable importance for the AAMs’ compressive strength—a simple, easy‐to‐use, closed‐form analytical model is developed. Results demonstrate that the analytical model yields predictions of compressive strength of AAMs without scarifying much accuracy compared to the ML model. Overall, this study's outcomes demonstrate a roadmap—incorporates composite chemostructural descriptors in ML models—that can be employed to design AAMs to achieve targeted compressive strength.

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.

Froth flotation process is extensively used for selective separation of base metal sulfides from uneconomic mineral resources. Reliable prediction of process outcomes (metal recovery and grade) is vital to ensure peak performance. This work employs an innovative hybrid machine learning (ML) model—constructed by combining the random forest model and the firefly algorithm—to predict froth flotation efficiency of galena and chalcopyrite in relation to various experimental process parameters. The hybrid model's prediction performance was rigorously evaluated, and compared against four different standalone ML models. The outcomes of this study illustrate that the hybrid ML model has the prediction ability to process outcomes with high‐fidelity, while consistently outperforming the standalone ML models.

Microcrystalline zeolites of the gismondine family are often reported in alkali‐activated and blended cement systems. However, little is known about gismondine's compatibility with other cementitious phases to determine stability in long‐term phase assemblage. Experimental studies were conducted to investigate the compositional field of gismondine stability in the lime‐alumina‐silica‐hydrate systems, with a particular focus on understanding the compatibility of gismondine with other cement phases such as C‐S‐H, ettringite, monosulfate, strätlingite, katoite, gypsum, calcite, portlandite, alkali, silica, and aluminosilicate phases. Results show that gismondine‐Ca forms readily at ~85°C in high aluminosilicate compositions; and persists in the presence of calcite, gypsum, ettringite, katoite solid solution, low Ca tobermorite‐like C‐S‐H, silica and aluminosilicate phases, at 20‐85°C. However, gismondine‐Ca reacts with: (a) monosulfate, producing ettringite‐thaumasite solid solution; (b) portlandite, forming tobermorite‐like C‐A‐S‐H gel and siliceous katoite at >55°C; (c) aqueous NaOH, generating gismondine‐(Na,Ca), a garronite‐like zeolite P solid solution; and (d) strätlingite leading to the conversion of strätlingite to gismondine indicating the metastability of strätlingite with respect to gismondine at 55°C. The outcomes are discussed to provide insights into the long‐term phase assemblage of relevant cement systems such as lime‐calcined clay, alkali‐activated materials, and potentially ancient Roman concrete.

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 andarea matchedfillers are used—which, essentially, eliminate degrees of freedom associated with surface area and agglomeration of filler particulates—the filler effect of quartz is broadly similar to that of limestone as well as rutile. Results also show that unlike quartz, limestone, and rutile—which enhance C₃S hydration kinetics—corundum suppresses hydration of C₃S during the first several hours after mixing. Such deceleration in C₃S hydration kinetics is attributed to the adsorption of aluminate anions—released from corundum's dissolution—onto anhydrous particulates’ surfaces, which impedes both the dissolution of C₃S and heterogeneous nucleation of C‐S‐H.

The hydration of tricalcium silicate (C₃S)—the major phase in cement—is effectively arrested when the activity of water (a_H) decreases below the critical value of 0.70. While it is implicitly understood that the reduction ina_Hsuppresses the hydration of tricalcium aluminate (C₃A: the most reactive phase in cement), the dependence of kinetics of C₃A hydration ona_Hand the criticala_Hat which hydration of C₃A is arrested are not known. This study employs isothermal microcalorimetry and complementary material characterization techniques to elucidate the influence ofa_Hon the hydration of C₃A in [C₃A + calcium sulfate (C$) + water] pastes. Reductions in water activity are achieved by partially replacing the water in the pastes with isopropanol. The results show that with decreasinga_H, the kinetics of all reactions associated with C₃A (eg, with C$, resulting in ettringite formation; and with ettringite, resulting in monosulfoaluminate formation) are proportionately suppressed. Whena_H ≤0.45, the hydration of C₃A and the precipitation of all resultant hydrates are arrested; even in liquid saturated systems. In addition to—and separate from—the experiments, a thermodynamic analysis also indicates that the hydration of C₃A does not commence or advance whena_H ≤0.45. On the basis of this criticala_H, the solubility product of C₃A (K_C3A) was estimated as 10^−20.65. The outcomes of this work articulate the dependency of C₃A hydration and its kinetics on water activity, and establish—for the first time—significant thermodynamic parameters (ie, criticala_HandK_C3A) that are prerequisites for numerical modeling of C₃A hydration.