Structural health monitoring by combining machine learning and dimensionality reduction techniques

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/165508

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental
dc.contributor	Universitat Politècnica de Catalunya. LACÀN - Mètodes Numèrics en Ciències Aplicades i Enginyeria
dc.contributor.author	Quaranta, Giacomo
dc.contributor.author	López Tomás, Elena
dc.contributor.author	Abisset-Chavanne, Emmanuelle
dc.contributor.author	Duval, Jean Louis
dc.contributor.author	Huerta, Antonio
dc.contributor.author	Chinesta Soria, Francisco
dc.date	2019-01-01
dc.identifier.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.
dc.identifier.citation	0213-1315
dc.identifier.citation	10.23967/j.rimni.2018.12.001
dc.identifier.uri	http://hdl.handle.net/2117/165508
dc.language.iso	eng
dc.relation	https://www.scipedia.com/public/Quaranta_et_al_2018a
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Matemàtiques i estadística::Àlgebra::Teoria de nombres
dc.subject	Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
dc.subject	Number theory
dc.subject	Artificial intelligence
dc.subject	Non Destructive Testing
dc.subject	Machine Learning
dc.subject	Dimensionality Reduction
dc.subject	Nombres, Teoria dels
dc.subject	Intel·ligència artificial
dc.subject	Classificació AMS::11 Number theory::11Y Computational number theory
dc.subject	Classificació AMS::68 Computer science::68T Artificial intelligence
dc.title	Structural health monitoring by combining machine learning and dimensionality reduction techniques
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/article
dc.description.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 dimensionality reduction techniques proceed.
dc.description.abstract	Peer Reviewed

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