Computational Generation of Virtual Concrete Mesostructures.

Autor: Holla V; Institute for Structural Mechanics, Ruhr University Bochum, Universitätsstrasse 150, 44791 Bochum, Germany., Vu G; Institute for Structural Mechanics, Ruhr University Bochum, Universitätsstrasse 150, 44791 Bochum, Germany., Timothy JJ; Institute for Structural Mechanics, Ruhr University Bochum, Universitätsstrasse 150, 44791 Bochum, Germany., Diewald F; Centre for Building Materials, Technical University of Munich, Franz-Langinger-Strasse 10, 81245 Munich, Germany., Gehlen C; Centre for Building Materials, Technical University of Munich, Franz-Langinger-Strasse 10, 81245 Munich, Germany., Meschke G; Institute for Structural Mechanics, Ruhr University Bochum, Universitätsstrasse 150, 44791 Bochum, Germany.
Jazyk: angličtina
Zdroj: Materials (Basel, Switzerland) [Materials (Basel)] 2021 Jul 06; Vol. 14 (14). Date of Electronic Publication: 2021 Jul 06.
DOI: 10.3390/ma14143782
Abstrakt: Concrete is a heterogeneous material with a disordered material morphology that strongly governs the behaviour of the material. In this contribution, we present a computational tool called the Concrete Mesostructure Generator (CMG) for the generation of ultra-realistic virtual concrete morphologies for mesoscale and multiscale computational modelling and the simulation of concrete. Given an aggregate size distribution, realistic generic concrete aggregates are generated by a sequential reduction of a cuboid to generate a polyhedron with multiple faces. Thereafter, concave depressions are introduced in the polyhedron using Gaussian surfaces. The generated aggregates are assembled into the mesostructure using a hierarchic random sequential adsorption algorithm. The virtual mesostructures are first calibrated using laboratory measurements of aggregate distributions. The model is validated by comparing the elastic properties obtained from laboratory testing of concrete specimens with the elastic properties obtained using computational homogenisation of virtual concrete mesostructures. Finally, a 3D-convolutional neural network is trained to directly generate elastic properties from voxel data.
Databáze: MEDLINE
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