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Structural health monitoring by combining machine learning and dimensionality reduction techniques

Author
Quaranta, G.; López, E.; Abisset-Chavanne, E.; Duval, J.; Huerta, A.; Chinesta Soria, Francisco
Type of activity
Journal article
Journal
Revista internacional de métodos numéricos para cálculo y diseño en ingeniería
Date of publication
2019-01-01
Volume
35
Number
1
First page
1
Last page
13
DOI
10.23967/j.rimni.2018.12.001
Project funding
Empowered decision-making in simulation-based engineering: advanced model reduction for real-time, inverse and optimization in industrial problems
Repository
http://hdl.handle.net/2117/165508 Open in new window
URL
https://www.scipedia.com/public/Quaranta_et_al_2018a Open in new window
Abstract
Structural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate damaged and undamaged regions in a given structure. Indeed, existence, severity (size) and location of damage can be efficiently estimated from collected data at some locations from which the fields of interest are completed before the analysis based on machine learning and...
Citation
Quaranta, G. [et al.]. Structural health monitoring by combining machine learning and dimensionality reduction techniques. "Revista internacional de métodos numéricos para cálculo y diseño en ingeniería", 1 Gener 2019, vol. 35, núm. 1, p. 1-13.
Keywords
Dimensionality Reduction, Machine Learning, Non Destructive Testing
Group of research
LACÀN - Numerical Methods for Applied Sciences and Engineering

Participants

  • Quaranta, Giacomo  (author)
  • López Tomás, Elena  (author)
  • Abisset-Chavanne, Emmanuelle  (author)
  • Duval, Jean Louis  (author)
  • Huerta, Antonio  (author)
  • Chinesta Soria, Francisco  (author)

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