Semantic segmentation of the micro-structure of strain-hardening cement-based composites (SHCC) by applying deep learning on micro-computed tomography scans

Considering the multi-phase constitutive nature of strain-hardening cement-based composites (SHCC) and the decided influence of their micromechanics on overall material behavior, appropriate analytical methods are necessary for the representation of their microstructure and micro-kinematics. In this respect, micro-computed tomography (microCT) is an efficient, nondestructive technique, which can couple experimental testing with scale-linking numerical simulations. However, for a detailed analysis of microstructure, appropriate segmentation techniques must be applied which can accurately differentiate and represent the individual material phases and other features of interest. Given the small scale of analysis, the typical resolution of common computed tomography, and the small differences among the material constituents in terms of density and x ray absorption as well, the application of common segmentation techniques to SHCC is ineffective. In this work, a Deep Learning technique was applied to the microCT images of two different SHCC. The Deep Learning network parameters were analyzed and optimized on a high-strength SHCC and applied to the automatic segmentation of a typical normal-strength SHCC. The results obtained are highly promising and quantitatively in accordance with the composition of the samples analyzed. It was possible to segment the polymer fibers and the air voids from the cementitious matrices accurately, while the accuracy of the quartz-sand particles' segmentation imposed additional challenges and proved dependent on the properties of the surrounding hydrated phase.