A real microstructural model for cement concrete modeling
Edward J. Garboczi, Yang Lu, Stephen Thomas
Existing concrete microstructural models of particles embedded in matrix materials are only represented by regular shape particles like spheres, ellipsoids, or multi-faceted polyhedrons. However, the real particle shapes are more complex and sometimes play an essential role of macroscale properties. Spherical Harmonics has been employed to characterize the irregular shape of particles from nano to continuum scale numerically. The preceding research has established procedures to retrieve particle aggregate shape characterizations from X-ray computed tomography (X-ray CT) scanned digital images. A real multiphase microstructure, Anm model, with irregular shape particles has been proposed. The Anm model places multiple irregular shape particles into a pre-defined empty box according to the real parking density to build up particles embedded in matrix material model in all scales from nano to macro. However, the packing accuracy and efficiency need to be improved, since the original Anm models algorithms has limited function and relative low parking efficiency. Furthermore, it cannot control the inter-particle distance. In this paper, we upgraded the initial algorithms by the following innovations: 1) integrated a new contact function, extending overlap box (EOB) to make the contact detection more accurately and efficiently; 2) the particle parking algorithm has been implemented to associate multiple aggregate shape database with each size bin; 3) an uniform thickness shell has been put around each parking particle, which can make the inter-particle distance customizable when we park nano-scale particles considering possible charge interactions between individual nano-particles.
Proceedings of the 5th Nanotechnology in Construction Conference
, Lu, Y.
and Thomas, S.
A real microstructural model for cement concrete modeling, Proceedings of the 5th Nanotechnology in Construction Conference, Chicago, IL, [online], https://doi.org/10.1007/978-3-319-17088-6_39
(Accessed May 30, 2023)