Long-term structural health monitoring of a steel road-rail bridge based on symbolic data analysis
| Autor: | Orcesi, André, Cremona, Christian, Cury, Alexandre, Dieleman, Luc |
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| Přispěvatelé: | Département Structures et Ouvrages d'Art (IFSTTAR/SOA), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Paris-Est, Service d'Etudes Techniques des Routes et Autoroutes (SETRA), Avant création Cerema, Université d'Ouro Preto, SNCF - IGOAR |
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: | |
| Zdroj: | EVACES'11-Experimental Vibration Analysis for Civil Engineering Structures EVACES'11-Experimental Vibration Analysis for Civil Engineering Structures, Oct 2011, Italy. 9p |
| Popis: | Structural health monitoring (SHM) of civil infrastructure systems has emerged as a promising effective integrated management tool. The use of SHM systems for bridges, tunnels and transportation networks, integrating sensors and instrumentation, communication and computation together with data and information management, has not ceased increasing over the last few years. A great challenge is the detection of early damage based on dynamic measurements. One of the major issues lies on the discrimination of abnormal changes from normal changes in the structural dynamic behaviour. Dynamic measurements can easily contain over thousands of values making an analysis process extensive and prohibitive. In this sense, despite the current processing power of computers, the necessary computational effort to manipulate large datasets remains a problem. The concept of symbolic data analysis (SDA) has recently been considered to classify different structural behaviours. This paper proposes the use of SDA methods applied to damage classification. The methods are based on the raw knowledge of the structure’s acceleration (acceleration measurements). Basic concepts of SDA and its application to the Adour Bridge are introduced. The proposed framework should enable to define warning notifications in case of early changes of dynamic measurements. Results are presented in order to show the efficiency of the described methodology. Dynamic measurements, Symbolic data analysis |
| Databáze: | OpenAIRE |
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