1. Optimal configuration for identification of parameters for chloride ingress models using bayesian networks
- Author
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Franck Schoefs, Stéphanie Bonnet, Thanh-Binh Tran, Emilio Bastidas-Arteaga, Contrôle de santé fiabilité et calcul des structures (TRUST), Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), and Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Engineering ,Parameter identification ,[SDE.MCG]Environmental Sciences/Global Changes ,Bayesian probability ,0211 other engineering and technologies ,020101 civil engineering ,Model parameters ,02 engineering and technology ,Bayesian inference ,Chloride ,[SPI.MAT]Engineering Sciences [physics]/Materials ,0201 civil engineering ,Task (project management) ,Chloride ingress ,021105 building & construction ,medicine ,business.industry ,Bayesian network ,Structural engineering ,Reinforced concrete ,Reliability engineering ,Corrosion ,[SPI.GCIV]Engineering Sciences [physics]/Civil Engineering ,Identification (information) ,business ,medicine.drug - Abstract
International audience; Chloride 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.
- Published
- 2014
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