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A Bayesian Network framework for probabilistic identification of model parameters from normal and accelerated tests : application to chloride ingress into conrete

Abstract : Chloride ingress into concrete is one of the major causes leading to the degradation of reinforced concrete (RC) structures. RC structures are often designed for a service life from 50 to 100 years. Under chloride attack important damages are generated after 10 to 20 years. Consequently, they should be periodically inspected and repaired to ensure an optimal level of serviceability and safety during its lifecycle. Reliability assessment of structures subjected to chloride-induced corrosion is performed to predict the safety level and avoid unexpected damage consequences. Relevant material and environmental parameters for reliability analysis could be determined from inspection data. In natural conditions, chloride ingress involves a large number of uncertainties related to material properties and exposure conditions. These uncertainties are also affected by temporal and spatial variability of associated deterioration process and their characterisation requires larger amount of inspection data. However, due to the slow process of chloride ingress and the difficulties for implementing the inspection techniques, it is difficult to obtain sufficient inspection data to characterise the mid- and long-term behaviour of this phenomenon. The main objective of this thesis is to develop a framework based on Bayesian Network updating for improving the identification of uncertainties related to material and environmental model parameters in case of limited amount of measurements in time and space. The identification process is based on results coming from in-lab normal and accelerated tests that simulate tidal conditions. Based on these data, several procedures are proposed to: (1) identify input random variables from normal or natural tests; (2) determine an equivalent exposure time (and a scale factor) for accelerated tests; and (3) characterise time-dependent parameters combining information from normal and accelerated tests. The identified parameters are after used to evaluate their impact on the assessment of the probability of corrosion initiation. The results indicate that the proposed framework could be a useful tool to identify model parameters even from limited data.
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Submitted on : Saturday, February 10, 2018 - 6:43:34 PM
Last modification on : Wednesday, December 19, 2018 - 4:58:05 PM

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Thanh Binh Tran. A Bayesian Network framework for probabilistic identification of model parameters from normal and accelerated tests : application to chloride ingress into conrete. Mechanics of the structures [physics.class-ph]. Université de nantes, 2015. English. ⟨tel-01706168⟩

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