Polymorphic Uncertain Structural Analysis: Challenges in Data‐Driven Inelasticity

This contribution addresses polymorphic uncertainty quantification within structural analysis of reinforced concrete structures composed heterogeneous and reinforcement (e.g. steel bars or carbon fibres). The macroscopic material behaviour is strongly dependent on the mesoscopic heterogeneities, which are considered by multiscale modelling. characterized representative volume elements (RVE) transition scales carried out utilizing numerical homogenization methods. concept data-driven computational mechanics enables model free finite element analyses directly based data sets, overcoming necessity assumptions in approach mainly consists assigning a stress-strain state, leads to minimum an objective function fulfils equilibrium as well compatibility constraints every integration point. Inelastic taken into account through definition local sets containing only set states consistent with respect past history . realistic modelling requires consideration uncertainty. Generalized models utilized order take variability, imprecision, inaccuracy incompleteness combining aleatoric epistemic models. In this contribution, computationally efficient for uncertain information reduction measurements presented. Due generalization, applicable various aleatoric, identification admissible taking inelastic realized energy parametrization extended data. An selection these presented challenges inelasticity, particularly use case structures, pointed out.