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Limestone calcined clay cement (LC3) is a sustainable alternative to ordinary Portland cement, capable of reducing the binder’s carbon footprint by 40% while satisfying all key performance metrics. The inherent compositional heterogeneity in select components of LC3, combined with their convoluted chemical interactions, poses challenges to conventional analytical models when predicting mechanical properties. Although some studies have employed machine learning (ML) to predict the mechanical properties of LC3, many have overlooked the pivotal role of feature selection. Proper feature selection not only refines and simplifies the structure of ML models but also enhances these models’ prediction performance and interpretability. This research harnesses the power of the random forest (RF) model to predict the compressive strength of LC3. Three feature reduction methods—Pearson correlation, SHapley Additive exPlanations, and variable importance—are employed to analyze the influence of LC3 components and mixture design on compressive strength. Practical guidelines for utilizing these methods on cementitious materials are elucidated. Through the rigorous screening of insignificant variables from the database, the RF model conserves computational resources while also producing high-fidelity predictions. Additionally, a feature enhancement method is utilized, consolidating numerous input variables into a singular feature while feeding the RF model with richer information, resulting in a substantial improvement in prediction accuracy. Overall, this study provides a novel pathway to apply ML to LC3, emphasizing the need to tailor ML models to cement chemistry rather than employing them generically.

 

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. 

Calcium aluminate cement (CAC) has been explored as a sustainable alternative to Portland cement, the most widely used type of cement. However, the hydration reaction and mechanical properties of CAC can be influenced by various factors such as water content, Li2CO3 content, and age. Due to the complex interactions between the precursors in CAC, traditional analytical models have struggled to predict CAC binders’ compressive strength and porosity accurately. To overcome this limitation, this study utilizes machine learning (ML) to predict the properties of CAC. The study begins by using thermodynamic simulations to determine the phase assemblages of CAC at different ages. The XGBoost model is then used to predict the compressive strength, porosity, and hydration products of CAC based on the mixture design and age. The XGBoost model is also used to evaluate the influence of input parameters on the compressive strength and porosity of CAC. Based on the results of this analysis, a closed-form analytical model is developed to predict the compressive strength and porosity of CAC accurately. Overall, the study demonstrates that ML can be effectively used to predict the properties of CAC binders, providing a valuable tool for researchers and practitioners in the field of cement science.