Spatial stochastic direct and inverse analysis for the extent of damage in deteriorated RC structures

The problem of updating the parameters of a probabilistic model, describing spatially large structures, based on uncertain output information is analyzed. An unscented Kalman filter (UKF) variant is successfully used, although the analysis has not been cast in a filtering format. The performance of the UKF-variant is compared with other generic gradient-free inverse solvers. To reduce the computational demand of the stochastic model, sensitivity analysis for functional inputs and probabilistic homogenization techniques are used. Without loss of generality for this type of problems, the whole process is described along a specific application concerning diffusion phenomena and steel damage in RC.