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Bayesian dynamic linear models for structural health monitoring

James Alexandre Goulet

Article de revue (2017)

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Abstract

In several countries, infrastructure is in poor condition, and this situation is bound to remain prevalent for the years to come. A promising solution for mitigating the risks posed by ageing infrastructure is to have arrays of sensors for performing, in real time, structural health monitoring across populations of structures. This paper presents a Bayesian dynamic linear model framework for modeling the time-dependent responses of structures and external effects by breaking it into components. The specific contributions of this paper are to provide (a) a formulation for simultaneously estimating the hidden states of structural responses as well as the external effects it depends on, for example, temperature and loading, (b) a state estimation formulation that is robust toward the errors caused by numerical inaccuracies, (c) an efficient way for learning the model parameters, and (d) a formulation for handling nonuniform time steps.

Mots clés

Bayesian models; bridge; dynamic linear models; infrastructure; Kalman filter; structural health monitoring (SHM)

Sujet(s): 1000 Génie civil > 1000 Génie civil
1100 Génie des structures > 1104 Analyse des structures
Département: Département des génies civil, géologique et des mines
Organismes subventionnaires: Swiss National Science Foundation, FRQNT, Conseil national de recherches Canada
Numéro de subvention: RGPIN-2016-06405
URL de PolyPublie: https://publications.polymtl.ca/2647/
Titre de la revue: Structural Control and Health Monitoring (vol. 24, no 12)
Maison d'édition: Wiley
DOI: 10.1002/stc.2035
URL officielle: https://doi.org/10.1002/stc.2035
Date du dépôt: 31 juil. 2017 17:18
Dernière modification: 27 sept. 2024 22:50
Citer en APA 7: Goulet, J. A. (2017). Bayesian dynamic linear models for structural health monitoring. Structural Control and Health Monitoring, 24(12), e2035. https://doi.org/10.1002/stc.2035

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