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Presented paper concerns the difficulty of objective characterization of spatial variability due to advancing degradation as a result of environmental exposure. The main issue is to enhance realism in the prediction of remaining service life of existing concrete infrastructure and thus effectively cover the relatively large sample space of possible future deteriorating states. This is done by introducing a special sampling strategy where artificial realizations of damage scenarios are generated. Here, not only material and geometry of a particular structural member is randomized in a nonlinear 3D finite element context, but advanced evolutionary spatial degradation models are applied to account for the variability of future damage states. These are computed using a non-traditional evolutionary scheme based on cellular automata (CA), which is used here to solve the transport equations in time and space and generate the irregular and heterogeneous structure of concrete. Within the presented example of an ageing bridge, the CA simulation also accounts to complex boundary conditions, e.g. the non-stationary seasonal de-icing salt application, irregular turbulent feed or washout effects. The selected case study of an existing deteriorated bridge serves as an application example with historical evidence and well documented damage profiles. It is further discussed with respect to Monte Carlo based structural reliability assessment, how to infer likelihoods associated to the set of implicit statements on damage, as this concept still offers open questions for research, yet is critical to successful and objective uncertainty quantification.

SPATIAL DEGRADATION IN RELIABILITY ASSESSMENT OF AGEING CONCRETE STRUCTURES