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Reinforced concrete structures are often subjected to corrosion, inducing a decreasing reliability over time. This corrosion often exhibits spatial variation, which can be modelled by random fields. To improve the accuracy of the estimated reliability, measurements can be performed. In this work, focus will be on static strain data. Taking into account these measurements leads to updated distributions of the corrosion parameters and an updated reliability. However, modelling errors will arise due to an inadequate choice of the spatial correlation model. These are present since the perfect model is not known. By assuming an inaccurate correlation model, a bias on the distribution of the reliability index might arise. In this work, the influence of this modelling error is investigated by assuming a different correlation model in the calibration of the model and the generation of the data. Different correlation models are considered and the most robust spatial correlation model is searched for. This problem is considered first for a simply supported beam subjected to corrosion, with the aim of identifying the most robust model. Next, it is investigated whether this model also performs well for a bridge structure.

Influence of the correlation model on the failure probability of a reinforced concrete structure considering spatial variability