Abstract An accurate assessment of corrosion in reinforced concrete structures is crucial for predicting their remaining capacity. Hence, this research develops a Bayesian updating approach, utilizing spatially distributed inspection data, to enhance corrosion parameter predictions. The study focuses on two corrosion parameters, which represent both steel and concrete degradation: the corrosion level (CL) and reduced apparent concrete stiffness. Initial prior estimations of both corrosion parameters are based on corrosion initiation time and current density, as often employed in the literature and in practice. Thereafter, inspection data, including detailed crack measurements and acoustic emission sensing, are used to enhance the reliability of the parameter estimations, as quantified through Bayesian updating. The prior and posterior estimated corrosion parameters are incorporated in finite element model predictions of the structural capacity, which are compared with experimental test results to validate the updating approach and quantify the improved model accuracy. The results show that integrating inspection data improves corrosion parameter and structural capacity predictions, especially for structural elements with non‐uniform CLs. Moreover, the Bayesian framework allows for incorporation of uncertainties, ensuring robust corrosion assessments.
Vandecruys et al. (Sat,) studied this question.