Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

58.848 UPC E-Prints
You are here:
View Item 
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament de Matemàtiques
  • Capítols de llibre
  • View Item
  •   DSpace Home
  • E-prints
  • Departaments
  • Departament de Matemàtiques
  • Capítols de llibre
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Data-driven methodologies for structural damage detection based on machine learning applications

Thumbnail
View/Open
52838.pdf (4,476Mb) (Restricted access)   Request copy 

Què és aquest botó?

Aquest botó permet demanar una còpia d'un document restringit a l'autor. Es mostra quan:

  • Disposem del correu electrònic de l'autor
  • El document té una mida inferior a 20 Mb
  • Es tracta d'un document d'accés restringit per decisió de l'autor o d'un document d'accés restringit per política de l'editorial
Share:
 
 
10.5772/65867
 
  View Usage Statistics
Cita com:
hdl:2117/101068

Show full item record
Vitola Oyaga, Jaime
Anaya Vejar, Maribel
Tibaduiza Burgos, Diego Alexander
Pozo Montero, FrancescMés informacióMés informacióMés informació
Document typePart of book or chapter of book
Defense date2016-12
Rights accessRestricted access - publisher's policy
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Structural health monitoring (SHM) is an important research area, which interest is the damage identification process. Different information about the state of the structure can be obtained in the process, among them, detection, localization and classification of damages are mainly studied in order to avoid unnecessary maintenance procedures in civilian and military structures in several applications. To carry out SHM in practice, two different approaches are used, the first is based on modelling which requires to build a very detailed model of the structure, while the second is by means of data-driven approaches which use information collected from the structure under different structural states and perform an analysis by means of data analysis . For the latter, statistical analysis and pattern recognition have demonstrated its effectiveness in the damage identification process because real information is obtained from the structure through sensors installed permanently to the observed object allowing a real-time monitoring. This chapter describes a damage detection and classification methodology, which makes use of a piezoelectric active system which works in several actuation phases and that is attached to the structure under evaluation, principal component analysis, and machine learning algorithms working as a pattern recognition methodology. In the chapter, the description of the developed approach and the results when it is tested in one aluminum plate are also included.
CitationVitola, J., Anaya, M., Tibaduiza, D.A., Pozo, F. Data-driven methodologies for structural damage detection based on machine learning applications. A: "Pattern recognition - Analysis and applications". 2016, p. 109-126. 
URIhttp://hdl.handle.net/2117/101068
DOI10.5772/65867
ISBN978-953-51-2804-5
Publisher versionhttp://www.intechopen.com/books/pattern-recognition-analysis-and-applications/data-driven-methodologies-for-structural-damage-detection-based-on-machine-learning-applications
Collections
  • Departament de Matemàtiques - Capítols de llibre [186]
  • CoDAlab - Control, Modelització, Identificació i Aplicacions - Capítols de llibre [43]
Share:
 
  View Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
52838.pdfBlocked4,476MbPDFRestricted access

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Contact Us
  • Send Feedback
  • Inici de la pàgina