EM - Escola de Minas

URI permanente desta comunidadehttp://www.hml.repositorio.ufop.br/handle/123456789/6

Notícias

A Escola de Minas de Ouro Preto foi fundada pelo cientista Claude Henri Gorceix e inaugurada em 12 de outubro de 1876.

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Filtros

2010 - 20122

Configurações

Tem arquivos: SimAssunto: search.filters.subject.Symbolic data analysis

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
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    Application of symbolic data analysis for structural modification assessment.
    (2010) Cury, Alexandre Abrahão; Crémona, Christian; Diday, Edwin
    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 agglomeratives chemes. Resultsregarding experimental performed onarail way 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 orthe 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
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    Assignment of structural behaviours in long-term monitoring : application to a strengthened railway bridge.
    (2012) Cury, Alexandre Abrahão; Crémona, Christian
    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