Bayesian damage identification of an unsymmetrical frame structure with an improved PSO algorithm

Abstract

With the development of artificial intelligence technology, intelligent algorithms are widely utilized in the field of probabilistic model updating to solve the optimization problems. In this study, the opposition-based learning based on population centroid and boundary rebound strategy was incorporated into PSO to improve the optimization performance on structural damage detection, and the damage identification of an unsymmetrical frame based on Bayesian model updating with the improved PSO algorithm is proposed for the first time. Six benchmark functions were firstly employed to test the optimization performance of the improved PSO algorithm, which overcomes the premature convergence of the original PSO algorithm in which trapped particles often fall into local optimum. Then three damage scenarios of a laboratory unsymmetrical frame were utilized to validate the effectiveness of the proposed method in structural damage identification. The encouraging experimental results exhibit that the proposed method not only can successfully identify the damage locations and extents, but the associated uncertainties of the identified results can be quantified by calculating the posterior probability density functions of the uncertain model parameters. In addition, the effects of the quantities of measured information on the posterior uncertainties of the model parameters were investigated, and the uncertainty analysis results reveal that the posterior uncertainties associated with the identified model parameters are sensitive to the amount of measurement used in model updating. The proposed method is expected to help in damage identification and condition assessment of the practical engineering structures.