Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Computational Generation of Virtual Concrete Mesostructures

Version 1 : Received: 27 June 2021 / Approved: 28 June 2021 / Online: 28 June 2021 (14:49:38 CEST)

A peer-reviewed article of this Preprint also exists.

Holla, V.; Vu, G.; Timothy, J.J.; Diewald, F.; Gehlen, C.; Meschke, G. Computational Generation of Virtual Concrete Mesostructures. Materials 2021, 14, 3782. Holla, V.; Vu, G.; Timothy, J.J.; Diewald, F.; Gehlen, C.; Meschke, G. Computational Generation of Virtual Concrete Mesostructures. Materials 2021, 14, 3782.

Journal reference: Materials 2021, 14, 3782
DOI: 10.3390/ma14143782

Abstract

Concrete is a heterogeneous material with a disordered material morphology that strongly governs the behavior 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 modeling and 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.

Subject Areas

Concrete, Mesoscale, Modeling, Virtual Mesostructure, Machine Learning

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