In this talk, we present a stochastic modeling framework for random fracture properties. The approach is cast within a phase-field formulation, and probabilistic representations are specifically developed to model stochastic spatial variability. The model allows pointwise correlation in fracture parameters to be explored, and different covariance structures are discussed to provide modeling flexibility. Forward simulations are then performed to investigate the sensitivity of fracture simulation outcomes, such as mean fragment size, to model parameters. We finally discuss the identification of the stochastic model by considering an experimental database involving drying tests on mining waste. We show that the proposed formulation enables the reproduction of observed crack networks, both qualitatively (sample-wise) and in a statistical sense.

Stochastic modeling and inverse identification of fracture properties based on phase-field simulations and physical experiments

The Virtual Technical Meeting of the Society of Engineering Science 2020 is the first fully virtual meeting of the SES community to envision and connect during the hiatus created by Covid-19, providing a platform for the exchange of ideas, and to showcase the latest research in the field of engineering science.

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