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Bayesian dynamic linear models for structural health monitoring

James-A. Goulet

Article (2017)

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Cite this document: Goulet, J.-A. (2017). Bayesian dynamic linear models for structural health monitoring. Structural Control and Health Monitoring, 24(12), e2035. doi:10.1002/stc.2035
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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.

Uncontrolled Keywords

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

Open Access document in PolyPublie
Subjects: 1000 Génie civil > 1000 Génie civil
1100 Génie des structures > 1104 Analyse des structures
Department: Département des génies civil, géologique et des mines
Research Center: Non applicable
Funders: Swiss National Science Foundation, FRQNT, Conseil national de recherches Canada
Grant number: RGPIN-2016-06405
Date Deposited: 31 Jul 2017 17:18
Last Modified: 24 Oct 2018 16:12
PolyPublie URL: https://publications.polymtl.ca/2647/
Document issued by the official publisher
Journal Title: Structural Control and Health Monitoring (vol. 24, no. 12)
Publisher: Wiley
Official URL: https://doi.org/10.1002/stc.2035

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