Machine Learning Inference Limits of Routine Cement Characterization for CEM I Performance: Evidence From a Multi-Producer Dataset
Routine cement performance characterization provides continuous quality control data, but its information content for performance inference and transferability across independent producers remains uncertain. This study analyzes 476 cement records from 23 European producers, collected in one laboratory over 27 years, to determine what can be inferred from routine measurements. The analysis focuses on CEM I and combines oxide chemistry, Blaine fineness, particle-size distribution descriptors, physical properties, and derived Bogue and equivalent-alkali descriptors with machine learning attribution and producer-transfer tests. For CEM I, fineness is the strongest descriptor family for strength class and water demand, but oxide chemistry contributes a comparable signal when evaluated jointly. Blaine and compact particle-size distribution representations are largely interchangeable within the descriptor space, indicating that the dominant recoverable fineness information is captured by routine measurements. Equivalent alkali shows a consistent negative association with 28-day strength, through K$_2$O in this dataset. Strength class and water demand can be recovered from routine cement characterization data. The early-strength designation is recovered only as a population-level tendency, not a physically separable class, because early-strength development can arise from combinations of fineness, sulfate--alkali chemistry, phase assemblage, and plant practice. Producer-holdout tests show that absolute prediction errors remain comparable across the held-out producers in this dataset, whereas recovery of within-producer strength variation is producer-dependent. Routine CEM I characterization therefore supports useful performance inference across producers, while exposing producer-specific variation whose recovery may require additional speciation.