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International audienceChloride 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 that is very limited due to time-consuming and expensive tests. The main objective of this paper is to develop a method based on Bayesian updating for selecting appropriate inspection configuration that can provide an optimal balance between accuracy and cost. The results indicate that Bayesian approach could be a useful tool to identify model parameters even from insufficient inspection data

Optimal configuration for identification of parameters for chloride ingress models using bayesian networks