Structural health monitoring with dependence on non-harmonic periodic hidden covariates

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.

Uncontrolled Keywords

Bayesian; Dynamic Linear Models; Hidden covariates; Structural Health Monitoring; Kalman filter; Dam