(untitled) (REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World, 2026)
Global sustainability goals require the reduction of waste and CO₂ emissions, motivating the reuse of reinforced concrete elements on a structural level. Assessing the structural reliability of such reused elements is particularly challenging, as their performance varies due to aging processes and exposure to environmental influences. In order to take these variabilities into account when performing structural analyses, the appropriate representation of uncertainty is crucial, see e.g. (Schoen, 2025). This contribution focuses on the generation of random fields for uncertainty quantification of spatially variable material properties of reused concrete elements, see (Botte et al., 2025). Correlation properties, such as correlation functions and lengths, are determined from measurement data (see e.g. (Fina et al., 2020)) of removed concrete elements obtained through destructive and non-destructive testing. The generated random fields are integrated into a nonlinear computational model of a reinforced concrete structure. Comprehensive probabilistic analyses are performed to show the influence of different random field approaches on the stochastic load-bearing capacity.