This paper demonstrates the use of normalized intensities of chemical species obtained from energy-dispersive X-ray spectroscopy (EDS) as inputs to machine learning (ML) models, in order to predict the nanoindentation moduli (M) of different phases in a cementitious matrix. Single and multi-component blends belonging to conventional and ultra-high performance (UHP) pastes are evaluated using a variety of ML models. It is shown that the relative intensities of Ca, Si, and Al can be used to accurately predict the phase moduli in well-hydrated pastes with limited microstructural complexities, using all the ML models investigated. When data sets belonging to multiple binders or those for UHP pastes consisting of multiple materials and low degrees of reaction are considered, the accuracy of ML predictions are found to be significantly lower. This is partly attributable to the presence of mixed phases with widely differing chemistry-property relationships, and the lack of data for higher stiffness phases that exaggerate the skew-sensitivity of ML models like ANN. Potential data augmentation strategies to tide over some of these effects are suggested.

本文展示了使用从能量色散X射线光谱（EDS）获得的化学物种的归一化强度作为机器学习（ML）模型的输入，以预测水泥基中不同相的纳米压痕模量（M） 。使用多种ML模型评估属于常规和超高性能（UHP）浆料的单组分和多组分共混物。结果表明，使用所有研究的ML模型，可以使用Ca，Si和Al的相对强度来准确预测具有有限的微观结构复杂性的水合良好的糊料中的相模。当考虑到属于多个粘合剂的数据集或由多种材料组成且反应程度低的UHP糊剂的数据集时，发现ML预测的准确性明显较低。这部分归因于存在具有广泛不同的化学-性质关系的混合相，以及缺乏较高硬度相的数据，这些相夸大了ML模型（如ANN）的偏斜敏感性。建议使用潜在的数据增强策略来克服其中一些影响。