Materials Data Science for Microstructural Characterization of Archaeological Concrete

Abstract

ABSTRACTAncient Roman concrete presents exceptional durability, low-carbon footprint, and interlocking minerals that add cohesion to the final composition. Understanding of the structural characteristics of these materials using X-ray tomography (XRT) is of paramount importance in the process of designing future materials with similar complex heterogeneous structures. We introduce Materials Data Science algorithms centered on image analysis of XRT that support inspection and quantification of microstructure from ancient Roman concrete samples. By using XRT imaging, we access properties of two concrete samples in terms of three different material phases as well as estimation of materials fraction, visualization of the porous network and density gradients. These samples present remarkable durability in comparison with the concrete using Portland cement and nonreactive aggregates. Internal structures and respective organization might be the key to construction durability as these samples come from ocean-submersed archeological findings dated from about two thousand years ago. These are preliminary results that highlight the advantages of using non-destructive 3D XRT combined with computer vision and machine learning methods for systematic characterization of complex and irreproducible materials such as archeological samples. One significant impact of this work is the ability to reduce the amount of data for several computations to be held at minimalistic computational infrastructure, near real-time, and potentially during beamtime while materials scientists are still at the imaging facilities.