Generation of high-fidelity random fields from micro CT images and phase field-based mesoscale fracture modelling of concrete

Abstract

The damage and fracture mechanisms of concrete are complicated due to the material’s inherent meso-scale heterogeneities. In this study, continuous random fields for selected properties, such as the tensile strength, are first generated on the basis of real microscale X-ray Computed Tomography (CT) images of concrete, using statistical reconstruction algorithms and the Karhunen-Loève expansion technique. The random fields are then assigned to finite element models with mesoscale crack initiation and propagation simulated by a phase-field regularized cohesive zone model (PF-CZM) with softening laws. The proposed two-step modelling method is validated by Monte Carlo simulations of a square test-piece under uniaxial tension and an L-shaped panel under mixed-mode fracture. It is found that the method can capture the variability of mesoscale fracture processes, crack paths and load-displacement curves, in good agreement with measured experimental variations. The method holds promise for the efficient evaluation of structural reliability by Monte Carlo simulations taking into account stochastic microstructural variability, thanks to the fast generation of a large number of random samples, through the high-fidelity representation of real materials’ microstructures, and flexible simulation of complicated nonlinear fracture of phase-field models without the need for remeshing or mesh enrichments.