A Joint Robust Sparse Bayesian Learning Model with the Mixture of Gaussians for the Structural Damage Identification
Damage identification is one of the core components in structural health monitoring.The robust sparse Bayesian learning model with the mixture of Gaussians(RSBLM-MoG)has been illustrated to be effective in the field of damage identification because of the accurate quantification of the mixture of Gaussians for the uncertainties of damage identification.However,RSBLM-MoG ignores the similarity information among the multiple groups of measurements in damage identification.In order to further improve the accuracy of damage identification,this paper employs joint learning technology to utilize the similarity information among these measurements.As a result,a joint robust sparse Bayesian learning model with the mixture of Gaussians(JRSBLM-MoG)and its corresponding expectation-maximization algorithm based on the Laplace approximation technique are proposed for damage identification.A numerical example on a truss structure and an experimental validation on a fixed-end beam structure have verified that JRSBLM-MoG improves the accuracy of damage identification by 7.61％and 3.54％than RSBLM-MoG,respectively.This study shows that JRSBLM-MoG can effectively employ the similarity information among multiple measurements to improve the accuracy of damage identification.
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