Structural Health Monitoring with dependence on non-harmonic periodic hidden covariates. (1st July 2018)

• Record Type: Journal Article
• Authors: Nguyen, L.H.; Goulet, J-A.
• Is Part Of: Engineering structures. Volume 166(2018)
• Page Start: 187
• Page End: 194
• Publication Date: 2018-07-01
• Subjects: Bayesian -- Dynamic Linear Models -- Hidden covariates -- Structural Health Monitoring -- Kalman filter -- Dam; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2018.03.080
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A novel method for handling non-harmonic periodic hidden covariates is proposed. The potential of the new method is illustrated on data recorded on a dam in Canada. A comparison with the existing method in Bayesian Dynamic Linear Models (BDLMs). The new method has better predictive performance than existing method in BDLM. Abstract: 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.