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Bayesian System Identification based on Hierarchical Sparse Bayesian Learning and Gibbs Sampling with Application to Structural Damage Assessment
The focus in this paper is Bayesian system identification based on noisy incomplete modal data where we can impose spatially-sparse stiffness changes when updating a structural model.
Robust Bayesian compressive sensing with data loss recovery for structural health monitoring signals
The application of compressive sensing (CS) to structural health monitoring is an emerging research topic.
Hierarchical sparse Bayesian learning: theory and application for inferring structural damage from incomplete modal data
In this paper, we improve the theory of our previously proposed sparse Bayesian learning approach by eliminating an approximation and, more importantly, incorporating a constraint on stiffness increases.
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