Debonding identification in FRP-plated RC structures using PZT sensors

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

Description

Title: Debonding identification in FRP-plated RC structures using PZT sensors
Author/s:
  • Sevillano Bravo, Enrique
  • Perera Velamazan, Ricardo
  • Sun, Rui
Item Type: Presentation at Congress or Conference (Article)
Event Title: 9th International Conference on Structural Dynamics, EURODYN 2014
Event Dates: 30 June - 2 July 2014
Event Location: Porto, Portugal
Title of Book: Proceedings of the 9th International Conference on Structural Dynamics, EURODYN 2014
Date: July 2014
ISBN: 978-972-752-165-4
Subjects:
Freetext Keywords: strengthening with FRP, damage, PZT, impedance
Faculty: E.T.S.I. Industriales (UPM)
Department: Mecánica Estructural y Construcciones Industriales [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of Spainproject BIA2010-20234-C03-01UnspecifiedUnspecifiedUnspecified

More information

Item ID: 36693
DC Identifier: http://oa.upm.es/36693/
OAI Identifier: oai:oa.upm.es:36693
Official URL: http://paginas.fe.up.pt/~eurodyn2014/CD/program.html
Deposited by: Memoria Investigacion
Deposited on: 06 Apr 2016 09:08
Last Modified: 12 Apr 2016 10:48
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