Bileşenlerin özel uyumluluğuna sahip düşük karbonlu yüksek performanslı katkılı portland çimentoları

This thesis aims to develop low-carbon, high-performance blended cements by holistically investigating the multidimensional interactions of Portland cement (PC), supplementary cementitious materials (SCMs), and superplasticizers (SP). A comprehensive dataset consisting of isothermal calorimetry, thermal analysis, and compressive strength data was generated, and different PC types, replacement rates, SCM fineness, water/binder (w/b) ratio, SP type, and dosage were systematically varied. These data were analyzed with regression-based machine learning models to obtain high-accuracy predictions. While linear models performed well for outputs focused on physical parameters such as early-age compressive strength, nonlinear models (especially Gaussian process regression and artificial neural networks) were found to perform significantly better (R2 ≈ 0.90–0.97) and with lower error for complex and high-variance outputs such as hydration kinetics, CH content, and late-age compressive strength. The Multi-Criteria Compatibility Index, developed by normalizing and weighting different criteria, was applied to a synthetic dataset consisting of 1602 mixtures. The results showed that highly compatible systems had low PC-high SCM content, low w/b ratios, and high SP dosages. Furthermore, SCM with low to medium CaO and high SiO2+Al2O3 content increased compatibility. In conclusion, machine learning methods modeled the multivariable interactions in superplasticized blended cements with high accuracy, and the developed compatibility index emerged as an innovative and flexible tool for the preliminary design of low-carbon, high-performance mixtures. This approach contributes to an environmentally, economically, and socially sustainable cement industry.