Advancing microstructural insights in 3D-Printed cementitious materials via X-ray micro-computed tomography

This work aims to provide a comprehensive review of utilising X-ray micro-computed tomography (μCT) to analyse 3D-printed cementitious composites and porous concrete (foamed concrete and pervious concrete), followed by recent machine learning applications in μCT analysis of cementitious materials. The X-ray μCT analysis on 3D-printed concrete primarily reveals the porosity distribution along the layer height and pore morphology, which correlate with the mechanical and durability properties of 3D-printed concrete. On the other hand, a proper phase segmentation method is considered critical to accurately differentiate the air void phase from the solid phase in porous concrete. Machine learning has been predominantly applied to develop reliable and automated models that can efficiently segment different phases in the X-ray μCT sliced images of concrete, including air voids, aggregates, cement paste and lightweight polymer fibres. Meanwhile, an in-depth understanding of the concrete failure mechanism could be achieved by segmenting crack phases from air void phases with the assistance of machine learning. X-ray μCT should be regarded as a powerful tool for visualising and characterising the pore structures of both 3D-printed and porous concrete. Furthermore, the integration of machine learning shows the promising future of moving the X-ray μCT analysis on cementitious materials towards higher efficiency and reliability.