1. Un modèle décisionnel de maintenance pour les ouvrages d'art d'acier
- Author
-
Attema, Thomas, Kosgodagan Acharige, Alex, Morales-Nápoles, Oswaldo, Maljaars, Johan, The Netherlands Organisation for Applied Scientific Research (TNO), Systèmes Logistiques et de Production (SLP ), Laboratoire des Sciences du Numérique de Nantes (LS2N), 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)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Département Automatique, Productique et Informatique (IMT Atlantique - DAPI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Eindhoven University of Technology [Eindhoven] (TU/e)
- Subjects
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR] ,[SPI.GCIV]Engineering Sciences [physics]/Civil Engineering ,[STAT.AP]Statistics [stat]/Applications [stat.AP] ,Bridge deck ,Monitoring ,Linear elastic fracture mechanics ,[SPI.GCIV.RISQ]Engineering Sciences [physics]/Civil Engineering/Risques ,Non-parametric Bayesian networks ,[SPI.GCIV.STRUCT]Engineering Sciences [physics]/Civil Engineering/Structures ,Fatigue - Abstract
International audience; A probabilistic model is developed to investigate the crack growth development in welded details of orthotropic bridge decks. Bridge decks may contain many of these vulnerable details and bridge reliability cannot always be guaranteed upon the attainment of a critical crack. Therefore insight into the crack growth development is crucial in guaranteeing bridge reliability and scheduling efficient maintenance schemes. The probabilistic nature of the crack growth development model and the dependence of this model on many interdependent random variables results in significant uncertainties regarding model outcome. To reduce some of these uncertainties the probabilistic model is combined with a monitoring system installed on a part of the bridge. In addition, a Bayesian network is used to determine the dependence structure between the different details (monitored and non-monitored) of the bridge. This dependence structure enables us to make more accurate crack growth predictions for all details of the bridge while monitoring only a limited number of those details and updating the remaining uncertainties.
- Published
- 2017