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Bayesian dynamic linear models for structural health monitoring

James Alexandre Goulet

Article (2017)

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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)

Subjects: 1000 Civil engineering > 1000 Civil engineering
1100 Structural engineering > 1104 Structural analysis
Department: Department of Civil, Geological and Mining Engineering
Funders: Swiss National Science Foundation, FRQNT, Conseil national de recherches Canada
Grant number: RGPIN-2016-06405
PolyPublie URL: https://publications.polymtl.ca/2647/
Journal Title: Structural Control and Health Monitoring (vol. 24, no. 12)
Publisher: Wiley
DOI: 10.1002/stc.2035
Official URL: https://doi.org/10.1002/stc.2035
Date Deposited: 31 Jul 2017 17:18
Last Modified: 06 Apr 2024 20:37
Cite in 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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