Genetic-based EM algorithm to improve the robustness of Gaussian mixture models for damage detection in bridges

During the service life of bridges, the bridge management systems (BMSs) seek to handle all performed assessment activities by controlling regular inspections, evaluations, and maintenance of these structures. However, the BMSs still rely heavily on qualitative and visual bridge inspections, which compromise the structural evaluation and, consequently, the maintenance decisions as well as the avoidance of bridge collapses. The structural health monitoring appears as a natural field to aid the bridge management, providing more reliable and quantitative information. Herein, the machine learning algorithms have been used to unveil structural anomalies from monitoring data. In particular, the Gaussian mixture models (GMMs), supported by the expectation-maximization (EM) on the parameter estimation, have been proposed to model the main clusters that correspond to the normal and stable state conditions of a bridge, even when it is affected by unknown sources of operational and environmental variations. Unfortunately, the performance of the EM algorithm is strongly dependent on the choice of the initial parameters. This paper proposes a hybrid approach based on a standard genetic algorithm (GA) to improve the stability of the EM algorithm on the searching of the optimal number of clusters and their parameters, strengthening the damage classification performance. The superiority of the GA-EM-GMM approach, over the classic EMGMM one, is tested on a damage detection strategy implemented through the Mahalanobis-squared distance, which permits one to track the outlier formation in relation to the chosen main group of states, using real-world data sets from the Z-24 Bridge, in Switzerland. Copyright (C) 2016 John Wiley & Sons, Ltd.