Debonding identification in FRP-plated RC structures using PZT sensors

Sevillano Bravo, Enrique; Perera Velamazan, Ricardo y Sun, Rui (2014). Debonding identification in FRP-plated RC structures using PZT sensors. En: "9th International Conference on Structural Dynamics, EURODYN 2014", 30 June - 2 July 2014, Porto, Portugal. ISBN 978-972-752-165-4. pp. 2629-2634.

Descripción

Título: Debonding identification in FRP-plated RC structures using PZT sensors
Autor/es:
  • Sevillano Bravo, Enrique
  • Perera Velamazan, Ricardo
  • Sun, Rui
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 9th International Conference on Structural Dynamics, EURODYN 2014
Fechas del Evento: 30 June - 2 July 2014
Lugar del Evento: Porto, Portugal
Título del Libro: Proceedings of the 9th International Conference on Structural Dynamics, EURODYN 2014
Fecha: Julio 2014
ISBN: 978-972-752-165-4
Materias:
Palabras Clave Informales: strengthening with FRP, damage, PZT, impedance
Escuela: E.T.S.I. Industriales (UPM)
Departamento: Mecánica Estructural y Construcciones Industriales [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The application of the Electro-Mechanical Impedance (EMI) method for damage detection in Structural Health Monitoring has noticeable increased in recent years. EMI method utilizes piezoelectric transducers for directly measuring the mechanical properties of the host structure, obtaining the so called impedance measurement, highly influenced by the variations of dynamic parameters of the structure. These measurements usually contain a large number of frequency points, as well as a high number of dimensions, since each frequency range swept can be considered as an independent variable. That makes this kind of data hard to handle, increasing the computational costs and being substantially time-consuming. In that sense, the Principal Component Analysis (PCA)-based data compression has been employed in this work, in order to enhance the analysis capability of the raw data. Furthermore, a Support Vector Machine (SVM), which has been widespread used in machine learning and pattern recognition fields, has been applied in this study in order to model any possible existing pattern in the PCAcompress data, using for that just the first two Principal Components. Different known non-damaged and damaged measurements of an experimental tested beam were used as training input data for the SVM algorithm, using as test input data the same amount of cases measured in beams with unknown structural health conditions. Thus, the purpose of this work is to demonstrate how, with a few impedance measurements of a beam as raw data, its healthy status can be determined based on pattern recognition procedures.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de Españaproject BIA2010-20234-C03-01Sin especificarSin especificarSin especificar

Más información

ID de Registro: 36693
Identificador DC: http://oa.upm.es/36693/
Identificador OAI: oai:oa.upm.es:36693
URL Oficial: http://paginas.fe.up.pt/~eurodyn2014/CD/program.html
Depositado por: Memoria Investigacion
Depositado el: 06 Abr 2016 09:08
Ultima Modificación: 12 Abr 2016 10:48
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