AI 中文总结
研究分析多生产商水泥数据集，结合多种描述符与机器学习方法，探讨常规水泥表征用于性能推断的情况。发现细度对强度等级和需水量最关键，能跨生产商进行性能推断，但存在生产商特定变化，恢复需额外分析。
AI 中文摘要
常规水泥性能表征提供连续质量控制数据，但其用于性能推断及跨独立生产商的可转移性的信息内容仍不确定。本研究分析了来自23家欧洲生产商的476条水泥记录，这些记录在一个实验室27年里收集。分析聚焦于CEM I，结合氧化物化学、勃氏比表面积、粒度分布描述符、物理性能以及派生的博格和等效碱描述符，并进行机器学习归因和生产商转移测试。结果表明，细度是强度等级和需水量最强的描述符家族，联合评估时氧化物化学贡献相当信号。勃氏比表面积和紧凑粒度分布表示在描述符空间内基本可互换。等效碱与28天强度呈持续负相关。强度等级和需水量可从常规水泥表征数据中恢复，早期强度指定只能作为总体趋势恢复。生产商保留测试表明，该数据集中跨保留生产商的绝对预测误差仍相当，而生产商内部强度变化的恢复则取决于生产商。常规CEM I表征支持跨生产商进行有用的性能推断，同时揭示特定于生产商的变化，其恢复可能需要额外的物种形成。
英文摘要
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.
Comments17 pages, 6 figures, 13 tables
