Bayesian inference of mesoscale mechanical properties of mortar using experimental data from a double shear test

In this work, we propose Bayesian parameter estimation of a nonlinear mechanics based model describing the behaviour of mortar subjected to double shear test with externally bonded carbon fibre reinforced polymer (CFRP) plates. With the Bayesian approach, it is possible to identify mechanical material parameters of different phases of the mortar mesostructure, i.e. hardened cement paste, aggregates and interface transition zone (ITZ). Due to nonlinearity of the concerned problem, we use a novel sequential approach for the parameter inference, which does not require coupling between the finite element solver and software for the stochastic analysis. The model geometry and material mesostructure are learned based on micro-computed tomography (μCT) scans of the real specimen, whereas the unknown boundary conditions are assumed to be uncertain and are also identified from experimental data. Mortar is modelled through a discrete lattice model consisting of spatial Timoshenko beams with embedded discontinuities. The latter allows the description of the distinct stages in material degradation, from the appearance of microscopic material damage to its evolution into macroscopic cracks leading to localised failure.