Parameter identification in chloride ingress from accelerated test using Bayesian network Tran, Thanh-Binh; Bastidas-Arteaga, Emilio; Bonnet, Stéphanie; Schoefs, Franck
Chloride ingress into concrete is one of the main causes leading to the degradation of reinforced concrete (RC) structures. Important damages due to chloride-attack are reported after 10-20 years and thus, structures should be inspected periodically to ensure optimal levels of serviceability and safety. Modelling chloride ingress into concrete, therefore, becomes an important task to plan and quantify maintenance operations of structures. Relevant material and environmental parameters required for modelling could be determined from inspection data obtained after each inspection campaign. However, its assessment requires significant experimental time for collecting data that allow considering the time-dependency of the deterioration process. Data from accelerated test could be used as information about long-term performance of concrete or mortar under real exposure conditions if the scale factor reflecting the ratio between exposures times for normal and accelerated tests is determined. The main objective of this paper is to develop a method based on Bayesian updating to identify the ‘real’ (equivalent) exposure time from accelerated tests that is used to define the scale factor. The proposed methodology is first tested on simulated data and after implemented to real measurements.
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