As inspection data may concern data on specific point locations (e.g. core drilling, cover thickness, chloride content), grid data with a specific mesh size (e.g. potential mapping ) as well as zonal data (visual damage mapping), techniques are needed that allow for the integration of data sets with different spatial density. This will be achieved by modelling the damage by a random field. This results in zones with specific damage levels and related uncertainties. 

A scheme for hierarchical classification of input data will be set up, dependent on their importance in the modelling and decision process, to enable dealing with lack of data and related uncertainties as encountered in real cases.

The identified damage levels will be linked to mechanical effects (rebar section reduction, bond loss, and concrete cracking). In this project, a practical approach is sought with the development of a Bayesian framework, using empirical relations and accounting for uncertainties (material, model, and measurement). In the adopted Bayesian methodology, the prior uncertainty of the damage model is updated to a posterior uncertainty on the basis of inspections. 

Model uncertainties will be derived on the basis of comparisons with a limited number of experimental tests which are performed under controlled conditions (see WP4) and selected on-site case studies which might exhibit combined effects of mechanical and environment loading (cf. WP7). The latter will allow to assess model uncertainties in real conditions, by means of periodic inspection and vibration-based monitoring (WP3). The linking of the corrosion damage to mechanical effects leads to a parametrization of damage which is used as input parameters for performance prediction (WP2).