期刊
ENGINEERING STRUCTURES
卷 166, 期 -, 页码 187-194出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.engstruct.2018.03.080
关键词
Bayesian; Dynamic Linear Models; Hidden covariates; Structural Health Monitoring; Kalman filter; Dam
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2016-06405]
In Structural Health Monitoring, non-harmonic periodic hidden covariate typically arises when an observed structural response depends on unobserved external effects such as temperature or loading. This paper addresses this challenge by proposing a new extension to Bayesian Dynamic Linear Models (BDLMs) for handling situations where non-harmonic periodic hidden covariates may influence the observed responses of structures. The potential of the new approach is illustrated on the data recorded on a dam in Canada. A model employing the proposed approach is compared to another that only uses a superposition of harmonic hidden components available from the existing BDLMs. The comparative study shows that the proposed approach succeeds in estimating hidden covariates and has a better predictive performance than the existing method using a superposition of harmonic hidden components.
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