Ke Xu (1) (2), Qingxu Jin (3), Jiaqi Li (4), Daniela M. Ushizima (2) (5), Victor C. Li (6), Kimberly E. Kurtis (7), Paulo J.M. Monteiro (1)
Cement and Concrete Research. Volume 169. (July 2023). DOI: https://doi.org/10.1016/j.cemconres.2023.107164

Keywords

Machine learning, computer vision, synchrotron microtomography, image analysis, strain-hardening cementitious composites, fiber behavior, pore structure

Abstract

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.

How Our Software Was Used

Dragonfly analysis and visualization software was used to visualize the 3D fiber orientation and connectivity of pore networks. The calculations of 3D fiber orientation and pore connectivity were also performed in Dragonfly.

Author Affiliation

(1) Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA
(2) Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
(3) Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 30332, USA
(4) Atmospheric, Earth, and Energy Division, Lawrence Livermore National Laboratory, 94550, USA
(5) Berkeley Institute for Data Science, University of California, Berkeley, CA 94720, USA
(6) Department of Civil and Environmental Engineering, University of Michigan, Michigan, MI 48109, USA
(7) School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA