Structural Health Monitoring with dependence on non-harmonic periodic hidden covariates

• Published in: Engineering structures Vol. 166; pp. 187 - 194
• Main Authors: Nguyen, L.H., Goulet, J-A.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.07.2018; Elsevier BV
• Subjects: Bayesian; Bayesian analysis; Belief networks; Comparative studies; Dam; Dependence; Dynamic Linear Models; Harmonic analysis; Hidden covariates; Kalman filter; Kalman filters; Mathematical models; Performance prediction; Structural engineering; Structural Health Monitoring; Superposition (mathematics); Dynamic Linear Models; Structural Health Monitoring; Dam; Hidden covariates; Kalman filter; Bayesian
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2018.03.080

•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. 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.