Pattern recognition of structural behaviors based on learning algorithms and symbolic data concepts

Learning algorithms have extensively been applied to classification and pattern recognition problems in the past years. Some papers have addressed special attention to applications regarding damage assessment, especially how these algorithms could be used to classify different structural conditions. Nevertheless, few works present techniques in which vibration signatures can be directly used to provide insights about possible modification processes. This paper proposes a novel approach in which the concept of Symbolic Data Analysis (SDA) is introduced to manipulate not only vibration data (signals) but also modal properties (natural frequencies and mode shapes). These quantities (transformed into symbolic data) are combined to three well-known classification techniques: Bayesian Decision Trees, Neural Networks and Support Vector Machines. The objective is to explore the efficiency of this combined methodology. For this purpose, several numerical simulations are first performed for evaluating the probabilities of true detection (or true classification) in the presence of different damage conditions. Several noise levels are also applied to the data to attest the sensibility of each technique. Second, a set of experimental tests performed on a railway bridge in France is used to emphasize advantages and drawbacks of the proposed approach. Results show that the analysis combining the cited learning algorithms with the symbolic data concepts is efficient enough to classify and discriminate structural modifications with a high probability of true detection, either considering vibration data or modal parameters. Copyright (c) 2010 John Wiley & Sons, Ltd.