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Predicting the rheology of limestone calcined clay cements (LC3): Linking composition and hydration kinetics to yield stress through Machine Learning
Cement and Concrete Research ( IF 10.9 ) Pub Date : 2022-07-31 , DOI: 10.1016/j.cemconres.2022.106925
Oğulcan Canbek , Qunzhi Xu , Yajun Mei , N.R. Washburn , Kimberly E. Kurtis

The physicochemical characteristics of calcined clay influence yield stress of limestone calcined clay cements (LC3), but the independent influences the clay's physical and chemical characteristics as well as the effect of other variables on LC3 rheology are less well-understood. Further, a relationship between LC3 hydration kinetics and yield stress – important for informing mixture design – has not yet been established. Here, rheological properties were determined in pastes with varying water-to-solid ratio (w/s), constituent mass ratios (PC:metakaolin:limestone), limestone particle size and gypsum content. From these data, an ML model developed allowed the independent examination of the different mechanisms by which metakaolin fraction influences yield stress of LC3, identifying four predictors – packing index, Al2O3/SO3, total particle density and metakaolin fraction relative to limestone (MK/LS) – most significant for predicting LC3 yield stress. A methodology based on kernel smoothing also identified hydration kinetics parameters best correlated with yield stress.



中文翻译:

预测石灰石煅烧粘土水泥 (LC3) 的流变性:通过机器学习将成分和水化动力学与屈服应力联系起来

煅烧粘土的物理化学特性影响石灰石煅烧粘土胶结物(LC 3)的屈服应力,但对粘土物理和化学特性的独立影响以及其他变量对LC 3流变性的影响尚不清楚。此外,LC 3之间的关系水化动力学和屈服应力——对于告知混合物设计很重要——尚未确定。在这里,流变特性在具有不同水固比 (w/s)、组成质量比 (PC:偏高岭土:石灰石)、石灰石粒度和石膏含量的浆料中进行测定。根据这些数据,开发的 ML 模型允许独立检查偏高岭土部分影响 LC 3屈服应力的不同机制,确定了四个预测因子——堆积指数、Al 2 O 3 /SO 3、总颗粒密度和偏高岭土部分相对于石灰石 (MK/LS) – 对预测 LC 3最重要屈服应力。基于内核平滑的方法还确定了与屈服应力最相关的水合动力学参数。

更新日期:2022-07-31
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