A global expectation-maximization based on memetic swarm optimization for structural damage detection

During the service life of engineering structures, structural management systems attempt to manage all the information derived from regular inspections, evaluations and maintenance activities. However, the structural management systems still rely deeply on qualitative and visual inspections, which may impact the structural evaluation and, consequently, the maintenance decisions as well as the avoidance of collapses. Meanwhile, structural health monitoring arises as an effective discipline to aid the structural management, providing more reliable and quantitative information; herein, the machine learning algorithms have been implemented to expose structural anomalies from monitoring data. In particular, the Gaussian mixture models, supported by the expectation-maximization (EM) algorithm for parameter estimation, have been proposed to model the main clusters that correspond to the normal and stable state conditions of a structure when influenced by several sources of operational and environmental variations. Unfortunately, the optimal parameters determined by the EM algorithm are heavily dependent on the choice of the initial parameters. Therefore, this paper proposes a memetic algorithm based on particle swarm optimization (PSO) to improve the stability and reliability of the EM algorithm, a global EM (GEM-PSO), in searching for the optimal number of components (or data clusters) and their parameters, which enhances the damage classification performance. The superiority of the GEM-PSO approach over the state-of-the-art ones is attested on damage detection strategies implemented through the Mahalanobis and Euclidean distances, which permit one to track the outlier formation in relation to the main clusters, using real-world data sets from the Z-24 Bridge (Switzerland) and Tamar Bridge (United Kingdom).