Application of symbolic data analysis for structural modification assessment

Structural health monitoring is a problem which can be addressed at many levels. One of the more promising approaches used in damage assessment problems is based on pattern recognition. The idea is to extract features from the data that characterize only the normal condition and to use them as a template or reference. During structural monitoring, data are measured and the appropriate features are extracted as well as compared (in some sense) to the reference. Any significant deviations from the reference are considered as signal novelty or damage. In this paper, the corpus of symbolic data analysis (SDA) is applied on the one hand for classifying different structural behaviors and on the other hand for comparing any structural behavior to the previous classification when new data become available. For this purpose, raw information (acceleration measurements) and also processed information (modal data) are used for feature extraction. Some SDA techniques are applied for data classification: hierarchy-divisive methods, dynamic clustering and hierarchy-agglomerative schemes. Results regarding experimental tests performed on a railway bridge in France are presented in order to show the efficiency of the described methodology. The results show that the SDA methods are efficient to classify and to discriminate structural modifications either considering the vibration data or the modal parameters. In general, both hierarchy-divisive and dynamic cloud methods produce better results compared to those obtained by using the hierarchy-agglomerative method. More robust results are given by modal data than by measurement data. (C) 2009 Elsevier Ltd. All rights reserved.