Multiscale Characterization of Cementitious Systems: Recent Advances in Imaging, Scattering, Spectroscopy and Machine Learning-based Approaches
Recent progress in methods used in multiscale characterization of cementitious systems is reviewed. The review focuses on advances in imaging, scattering, and spectroscopy for characterization of cementitious materials, and also includes relevant applications of and development in machine learning and other data analytics approaches to enhance characterization. Developments in imaging via light and electron microscopy as well as x-ray (or synchrotron) methods are summarized, and include updates on scanning electron microscopy (SEM), transmission electron microscopy (TEM), as well as holography and tomography. A critical overview of spectroscopy (e.g., MAS NMR, Raman) and scattering (e.g., neutron, lab and synchrotron x-ray) methods is provided, and the intersection of these with imaging is developed (e.g., Raman imaging). Additionally, the paper summarizes recent developments in and implementations of state-of-the-art of machine-learning algorithms and data analytics methods for automated, systematic, and/or quantitative analyses of image data sets. The review considers but is not limited to the application of these methods for the investigation for the hydration and microstructure development of cement phases, low-energy cements (e.g., limestone calcined clay cements, LC3), environmental interactions (e.g., ASR) and model systems. Thus, the present work provides a critical presentation of advances in characterization methods that link together composition and multiscale structure of cementitious materials.