Symbolic data analysis and supervised/ non supervised learning algorithms for bridge health monitoring.

• Autor(a) principal: Crémona, Christian
• Data de Publicação: 2011
• Outros Autores: Cury, Alexandre Abrahão, Orcesi, André, Dieleman, Luc
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UFOP
• Texto Completo: http://www.repositorio.ufop.br/handle/123456789/1537

In the past few years, numerous methods for damage assessment in connection with structural health monitoring were proposed in the literature. Several problems are raised for making these approaches practical for the engineer. The first concern is to determine whether a structure presents an abnormal behavior or not. Statistical inference is concerned with the implementation of algorithms that analyze the distribution of extracted features in an effort to make decisions on damage diagnosis. Learning algorithms have extensively been applied to classification and pattern recognition problems in the past years and deserve to be used for structural health monitoring. Two approaches are nevertheless available depending on the ability to perform supervised or unsupervised learning. The first group of methods forms the family of classification methods whereas the second group is referred to clustering techniques. In addition, data acquisition campaigns of civil engineering structures can last from several minutes to years. Dealing with large amounts of data is not an easy task and suitable tools are required to correctly extract important features from them. To deal with this issue, symbolic data analysis (SDA) is introduced for managing complex, aggregated, relational, and higher-level data. SDA is then coupled with supervised and non supervised learning algorithms to form a new family of hybrid techniques. From the non supervised learning side, dynamic clouds and hierarchy-divisive method have been used. From the supervised learning side, neural networks and support vector machines have been introduced. All these techniques have been developed within the concept of symbolic data analysis in order to compress data without losing its inherent variability. To highlight the different features of these techniques for structural health monitoring, this paper focuses attention on the monitoring of a railway bridge belonging to the high speed track between Paris and Lyon. During the month of June 2003, a strengthening procedure was carried out in this bridge. In so doing, vibration measurements were recorded under three different structural conditions: before, during and strengthening. In the following years (2004, 2005 and 2006), new tests were performed to observe how the dynamic behavior of the bridge evolved, especially for the case of frequency changes. The objective was to verify whether the strengthening procedure was still effective or not, in order terms if the new data could be still assigned to the condition “after strengthening”. This paper reports the major results obtained and shows how the techniques can be applied to cluster structural behaviors and classify new data.