Improved Bayesian Network Configurations for Probabilistic Identification of Degradation Mechanisms: Application to Chloride Ingress

摘要: AbstractProbabilistic modelling of deterioration processes is an important task to plan and quantify maintenance operations structures. Relevant material environmental model parameters could be determined from inspection data; but in practice, the number measures required for uncertainty quantification conditioned by time-consuming expensive tests. The main objective this study was propose a method based on Bayesian networks improving identification uncertainties related models when there limited available information. outputs are configurations (in space time) that provide optimal balance between accuracy cost. proposed methodology applied random variables chloride ingress model. It found discretisation identifying each parameter combination these minimises id...