In-situ microtomography image segmentation for characterizing strain-hardening cementitious composites under tension using machine learning

The application of machine learning and computer vision in microtomography provides new opportunities to directly analyze the microstructural evolutions of strain-hardening cementitious composites (SHCC) under tensile load, especially the strain-hardening process. For the first time, a state-of-the-art machine-learning pipeline combined with digital volume correlation for automated microtomography segmentation analysis (MSA) was developed to separate different components and quantify the in-situ 3D morphological properties of the fibers and pore networks imaged with in-situ synchrotron X-ray computed microtomography. Strain localization and crack initiation were observed around the interconnected pores where strain localized instead of the weakest cross-section defined by the fiber distribution and porosity. Fibers reinforced the crack planes through fiber debonding, bridging, bending, stretching, and orientation redistribution, which contributed to the crack width control and ductility of SHCC in the experiment. This work is essential to understand the progressive damage mechanisms of SHCC and help refine the characterization, modeling, and design of the composite using a bottom-up approach.