Probabilistic modelling of deterioration processes is an important task to plan and quantify maintenance operations of structures. Relevant material and environmental model parameters could be determined from inspection data; but in practice the number of measures required for uncertainty quantification is conditioned by time-consuming and expensive tests. The main objective of this paper is to propose a method based on Bayesian networks for improving the identification of uncertainties related to material and environmental parameters of deterioration models when there is limited available information. The outputs of the study are inspection configurations (in space and time) that could provide an optimal balance between accuracy and cost. The proposed methodology was applied to the identification of random variables for a chloride ingress model. It was found that there is an optimal discretisation for identifying each model parameter and that the combination of these configurations minimises identification errors. An illustration to the assessment of the probability of corrosion initiation showed that the approach is useful even if inspection data is limited.
Cracking initiation and propagation are frequently recognized as main causes leading to failure of timber structures. Since the kinematics of both processes is largely influenced by environmental conditions, a comprehensive reliability assessment of notched structures should take into account such environmental factors. The main purpose of this paper is to propose a methodology for reliability assessment and updating of notched timber components based on mechanical (A-integral formulation) and reliability (simulation and Bayesian networks) methods, and experimental data. The A-integral formulation is used to estimate energy release rates in modes I and II by taking into account thermal effects; but its numerical implementation is time-consuming for uncertainty propagation. In order to deal with this problem, Bayesian networks were used for reliability assessment and updating. The experimental data used for updating purposes were obtained from measurements of deflection, temperature and relative humidity on a notched beam (Douglas Fir specie) exposed to outdoor environment and constant loading. The whole proposed methodology was illustrated with the reliability assessment and updating of the studied notched beam. The results indicated that the proposed approach is able to integrate measurements of temperature and deflection for reliability updating.
International audienceRelevant material and environmental parameters are required in modelling chloride ingress into concrete. They could be determined from experimental data (concrete cores taken during inspection) but in practice data availability is limited by time-consuming and expensive tests. Consequently, the main objective of this paper is to develop an approach based on Bayesian networks (BN) to improve the parameter identification when inspection data is limited. We aim at proposing appropriate inspection configurations that reduce inspection costs and identification errors for different exposure conditions and materials. It was found that it is possible to define an optimal number of inspection points in depth for allowed identification errors defined by decision makers. The optimal number of inspection points depends on both exposure and material properties. The random variables identified with the improved BN configurations are used to assess the probability of corrosion initiation. The results indicate that the improved BN configurations are useful to identify model parameters even from scarce inspection data
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