Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/24534

TitleMethodology for updating nonlinear structural models through experimental data acquisition
Author(s)Matos, José C.
Valente, Isabel
Cruz, Paulo J. S.
Neves, Luís Canhoto
KeywordsNonlinear behaviour
Probabilistic model
Bayesian inference
Experimental data
Model updating
Issue date2013
PublisherInternational Federation for Structural Concrete(FIB)
Abstract(s)Nonlinear numerical models are considered to be more accurate because they furnish a realistic representation of the structure under analysis. Moreover, it is known that different uncertainty sources should be considered when evaluating structure behaviour. Therefore, probabilistic based models, which consider the structural properties as distribution functions, are being implemented. In some situations, experimental data is respectively collected from permanent monitoring systems, nondestructive tests and visual inspection, to control this randomness. Consequently, the developed nonlinear probabilistic model may be updated through the use of a Bayesian inference algorithm. The advantage of this framework is its reduced computational cost and the reduced source of statistical uncertainty. This methodology is validated with a set of reinforced concrete structures, tested up to failure, in a laboratory. These structures are modelled with one nonlinear analysis software. The correspondent probabilistic model is then obtained through the introduction of proper distribution functions. This model is then updated with data collected from complementary characterization tests. Obtained results are used to validate the proposed inference algorithm.
TypeConference paper
URIhttp://hdl.handle.net/1822/24534
ISBN9789659203901
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:ISISE - Comunicações a Conferências Internacionais

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