Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects

dc.contributor.author	Vélez, A. P.
dc.contributor.author	Fernández Muñiz, María Zulima
dc.contributor.author	Fernández Martínez, Juan Luis
dc.date.accessioned	2025-04-30T09:18:45Z
dc.date.available	2025-04-30T09:18:45Z
dc.date.issued	2024
dc.identifier.citation	Aims Mathematics, 9(11), p. 30493-30514 (2024); doi:10.3934/math.20241472
dc.identifier.issn	2473-6988
dc.identifier.uri	https://hdl.handle.net/10651/78657
dc.format.extent	p. 30493-30514
dc.language.iso	eng
dc.relation.ispartof	Aims Mathematics
dc.rights	©,
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209898659&doi=10.3934%2fmath.20241472&partnerID=40&md5=036e1d9ad704934bf19c735d636b81eb
dc.title	Enhancing structural health monitoring with machine learning for accurate prediction of retrofitting effects
dc.type	journal article
dc.identifier.doi	10.3934/math.20241472
dc.relation.publisherversion	http://dx.doi.org/10.3934/math.20241472

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