In the past few decades, vibration-based structural damage detection (SDD) has attracted widespread attention. Using the response data of engineering structures, the researchers have developed many methods for damage localization and quantification. Adopting meta-heuristic algorithms, in which particle swarm optimization (PSO) is the most widely used, is a popular approach. Various PSO variants have also been proposed for improving its performance in SDD, and they are generally based on the Global topology. However, in addition to the Global topology, other topologies are also developed in the related literature to enhance the performance of the PSO algorithm. The effects of PSO topologies depend significantly on the studied problems. Therefore, in this article, we conduct a performance investigation of eight PSO topologies in SDD. The success rate and mean iterations that are obtained from the numerical simulations are considered as the evaluation indexes. Furthermore, the average rank and Bonferroni-Dunn’s test are further utilized to perform the statistic analysis. From these analysis results, the Four Clusters are shown to be the more favorable PSO topologies in SDD.

A two-stage approach based on the finite element model is proposed for structural damage detection. In the first stage, the damage identification problem is transformed into an $l_1$-minimization issue related to the natural-frequencies-based sensitivity matrix, which can be quickly solved using the alternating directional multiplication method. Based on this, a damage localization indicator vector (DLIV) is proposed for dimensionality reduction. In the second stage, a particle swarm optimization (PSO) algorithm with four-cluster topology, namely Cluster PSO, is employed to identify the location and severity of the actual damage after dimensionality reduction. Therefore, the proposed two-stage approach can be denoted as the DLIV-based Cluster PSO. In the numerical simulations, the performance of the proposed DLIV-based Cluster PSO is evaluated for single and multiple damage detection of a cantilever beam. The simulation results demonstrate that the proposed DLIV-based Cluster PSO approach provides faster convergence, less computational time and better noise tolerance.