| Abstract Scope |
There is a growing need to quantify and characterize the micro-to-nano scale processes within cements and concretes in multiple dimensions and scales. Evolving areas of research include recycled concretes, self-healing concretes, nuclear waste containment, and lowering the CO2 footprint in cement/concrete production. They are prone to degradation and change over time, making processes such as hydration, cracking, corrosion, and mineralogical/phase transformations under differing conditions (temperature, stress/strain, humidity) important to characterize for these fundamental building materials. Here, we have used non-destructive 3D imaging via X-ray Microscopy (XRM), combined with a novel 3D automated quantitative mineralogy technique, to spatially characterise the mineralogical phases in a variety of cements. Additionally, we have applied a deep learning-based reconstruction method to improve the quantification of pores and cracks. This combined workflow applies advanced denoising and vastly improves artefact detection in 10 mm+ diameter cores to enhance quantification, as well as accelerating throughput in tomographic investigations. |