Damage Detection of an Asymmetrical Frame via Sparse Bayesian Learning with a PSO Algorithm

For civil engineering structures, structural damage usually occurs at limited positions in the preliminary stage of the structural failure. Compared with the numerous elements of the entire structure, the damaged elements are sparsely distributed in space. Based on this important prior information, this paper proposed to utilize a sparse Bayesian learning method to identify the damage to structures while considering the measurement noise and modeling error. The particle Swarm Optimization (PSO) algorithm was first introduced to address the associated computational efficiency issue, and the optimization performances of PSO and Sequential Quadratic Programming (SQP) algorithm in the process of model updating were compared, positive outcomes revealed that the PSO algorithm has the stronger searching ability and better robustness. To investigate the effectiveness and practicality of the sparse Bayesian learning with a PSO algorithm in structural damage detection, an asymmetrical frame in different scenarios (e.g. with single and multiple damages) was constructed in the laboratory. The encouraging results of the experimental case studies compellingly demonstrate that the presented methodology not only can detect the location and extent of structural damage with high precision and efficiency, but also can proficiently assess the posterior uncertainties associated with the damage detection results.