Journal
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
Volume 11, Issue 4, Pages 422-441Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1475921711434858
Keywords
novelty detection; clustering; assignment; symbolic data analysis; dynamic clouds; hierarchy-divisive; dynamic testing; modal properties; probabilities of assignment
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Novelty detection, the identification of data that is unusual or different, is relevant in a wide number of real-world scenarios, ranging from identifying unusual weather conditions to detecting evidence of damage in mechanical systems. Using novelty detection approaches for structural health monitoring presents significant challenges to the non-expert user. In this article, symbolic data analysis is introduced to model variability in tests. Hierarchy-divisive methods and dynamic clouds procedures are then used to discriminate structural changes used as novelty detection approaches for classifying structural behaviours. This article reports the study of experimental tests performed on a railway bridge in France. This bridge has undergone reinforcement works during the summer of 2003. Through the years of 2004-2006, new sets of dynamic tests were recorded. The main objective was to analyse the evolution of the bridge's dynamic behaviour over time. To this end, the symbolic data analysis-based clustering methods are used for assigning new tests to clusters identified before and after strengthening or to highlight a totally different structural behaviour.
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