In line with sustainable construction goals, this study investigates the synergistic use of amine-based grinding aids (GAs), triethanolamine (TEA), and triisopropanolamine (TIPA) to enhance grinding performance and cement properties. GAs were physically blended at varying TEA/TIPA ratios, and their effects on grinding efficiency, CO₂ emissions, and environmental footprint were assessed based on energy consumption per target Blaine fineness. The interaction of blended GAs with Ca²⁺ ions was modeled to understand adsorption behavior. Cement particle size distribution (PSD), Hausner ratio, Carr index, and angle of repose were analyzed to evaluate powder flowability. Scanning electron microscopy (SEM) was employed to examine microstructural changes. Finally, the Taguchi method statistically analyzed the effective parameters influencing system performance. Results demonstrated that the optimized blend containing 25% TEA and 75% TIPA improved grinding performance, enhanced polymer–ion interactions, refined PSD, and significantly increased powder flowability. Overall, the study underscores the potential of amine-based polymeric GAs in producing environmentally friendly, high-performance cement composites. Using a Taguchi design with the larger-is-better S/N criterion, the optimal formulation was determined to be 25% TEA and 75% TIPA at a dosage of 0.10%. ANOVA results indicated that the TEA content was the most significant factor, while the dosage had no statistically significant effect.

Substituting cement with mineral additives like fly ash is increasingly essential for sustainable production. While replacement rates largely depend on fresh-state properties, the interaction between fly ash and polycarboxylate ether (PCE) molecular structures remains underexplored. In this regard, this study investigates the effect of PCE molecular structures and weight on the rheology, setting, and strength of cementitious systems containing up to 45% fly ash additions. Seven distinct PCE possessing different molecular weights (27,000–78,000 g/mol) as well as backbone and side chain lengths are synthesized. The interaction between PCE and solid particles was explored through total organic carbon, dynamic light scattering, and gel permeation chromatography. Test results showed that the adsorption rates of the cement and fly ash particles within the cementitious composites improved by up to 90% with fly ash replacement and upon using PCE with a medium molecular weight of 56,000 g/mol, backbone length of 21 k, and short side chain length of 1000 g/mol. This has resulted in a 75% reduction in the material’s apparent viscosity, delayed setting times of up to 38%, and improved early- and late-age compressive strengths of up to 123%. Such data can interest cement and admixture producers in proposing suitable PCEs for superior fly ash concrete performance.

Today, various strategies are being adopted to produce more environmentally friendly cementitious systems. A commonly adopted strategy is the enhancement of energy efficiency in the clinker grinding process through the use of grinding aids (GAs). Another approach is to reduce cement consumption by partially replacing cement with mineral additives such as fly ash. The literature has highlighted that the use of GAs during clinker grinding can narrow the particle size distribution, thereby promoting higher rates of mineral additive replacement. Nevertheless, the literature still lacks comprehensive insight into how the combined application of commonly used GAs influences the substitution levels of mineral additives. In this regard, this study thoroughly examined the influence of varying proportions and dosages of Triethanolamine (TEA) and Triisopropanolamine (TIPA)—two commonly employed grinding aids—on the hydration kinetics, compressive strength development, and life cycle performance of fly ash (FA)-blended cementitious systems. The mixtures prepared with the cements produced were analyzed through XRD, TGA, and SEM techniques, and the compressive strength results were evaluated using the Taguchi method. The results demonstrated that, irrespective of the type of additive used, the use of GAs enhanced pozzolanic activity and compressive strength. In particular, the GA combination containing 75% TIPA and 25% TEA proved the most superior results in terms of hydration kinetics, mechanical strength, and environmental performance. It was demonstrated that the combined use of TEA and TIPA in specific proportions creates a synergistic effect, enabling the development of more efficient binder systems.

This study investigates the interaction between polycarboxylate-based water-reducing admixtures (WRAs) and various types of CEM I 42.5R Portland cements, focusing on optimizing input parameters in cementitious systems. Despite the widespread use of WRAs to enhance concrete’s workability, strength, and durability, their compatibility with cement remains a critical challenge, often leading to performance issues such as low initial flow, bleeding, and rapid slump loss. This research addresses two significant gaps in the literature: the unexplored use of input parameter reduction in cementitious systems and the application of novel metaheuristic algorithms in optimizing these systems. In this study, 25 WRA were first synthesized to enrich the inputs of machine learning (ML) models. Then, a dataset of 750 entries was generated, and advanced prediction models were developed. To ensure scientific rigor and eliminate data leakage, a triple-split dataset strategy (Training–Validation–Test) and 5-fold cross-validation were implemented. Among the machine learning techniques analyzed, the Optimized Artificial Neural Networks (opANN) architecture decisively demonstrated the highest prediction performance on the isolated test dataset. In the opANN process, 10 different metaheuristics were tested to evaluate their effectiveness in hyperparameter optimization. As a result, the Kepler Optimization (KOA) algorithm was determined as the algorithm with the highest performance in ANN hyperparameter optimization. Furthermore, Shapley Additive Explanations (SHAP) analysis was utilized to bridge the gap between empirical observations and algorithmic predictions, quantitatively corroborating the rheological roles of phosphate and sulfonate groups. The results offer new insights into WRA–cement compatibility and present advanced, interpretable modeling approaches that enhance predictive accuracy, contributing to more reliable and sustainable concrete practices.

Grinding aids (GAs) are commonly utilized to enhance energy efficiency during clinker grinding and improve cement properties. However, alongside their benefits, GAs may lead to challenges such as reduced flow performance, loss of consistency retention, and increased demand for water or water-reducing admixtures in cementitious systems. Therefore, it is crucial to assess the flow properties and consistency retention behavior (time-dependent flow) in mixtures prepared with GAs. Given that such experiments are labor-intensive and time-consuming, predicting these performance metrics through machine learning offers substantial advantages. In this study, 29 different cements, incorporating various types and dosages of GAs, were produced. Time-dependent flow and compressive-strength tests were conducted on mixtures made with these cements. Additionally, the experimental results were compared with predictions using four machine learning models: random forest, adaptive boosting (AdaBoost), gradient boosting and multilayer perceptron. The computational results indicated that random forest outperformed the other machine learning algorithms in predicting compressive strength, whereas multilayer perceptron achieved the best overall performance for flow value prediction. Furthermore, it was concluded that random forest, adaptive boosting and gradient boosting provided acceptable performance in compressive strength prediction, while random forest and multilayer perceptron demonstrated comparatively better performance in predicting the flow value. However, the study is limited to specific types and dosages of GAs and does not include the influence of temperature, humidity, or long-term durability metrics, which may affect the generalizability of the machine learning models.