Identification bayésienne des paramètres des modèles de chloration dans le béton armé

Chloride ingress into concrete is one of the major causes leading to the degradation of reinforced concrete structures. Its modelling is an important task to plan and quantify maintenance operations of structures. Relevant material and environmental parameters for modelling could be determined from inspection data which is very limited due to expensive costs. The main objective of this paper is to develop a method based on Bayesian network updating for improving the identification of model parameters. The results indicate that Bayesian approach could be a useful tool to identify model parameters even from insufficient inspection data.