期刊
FRONTIERS IN BUILT ENVIRONMENT
卷 5, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fbuil.2019.00008
关键词
Bayesian; dynamic linear models; kernel regression; structural health monitoring; kalman filter; dam; bridge
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Hydro Quebec (HQ)
- Hydro Quebec's Research Institute (IREQ)
- Institute For Data Valorization (IVADO)
Modeling periodic phenomena with accuracy is a key aspect to detect abnormal behavior in time series for the context of Structural Health Monitoring. Modeling complex non-harmonic periodic pattern currently requires sophisticated techniques and significant computational resources. To overcome these limitations, this paper proposes a novel approach that combines the existing Bayesian Dynamic Linear Models with a kernel-based method for handling periodic patterns in time series. The approach is applied to model the traffic load on the Tamar Bridge and the piezometric pressure under a dam. The results show that the proposed method succeeds in modeling the stationary and non-stationary periodic patterns for both case studies. Also, it is computationally efficient, versatile, self-adaptive to changing conditions, and capable of handling observations collected at irregular time intervals.
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