Mostrar el registro sencillo del ítem

dc.contributor.authorChiachío Ruano, Manuel 
dc.contributor.authorMegía Cardeñoso, María 
dc.contributor.authorChiachío Ruano, Juan 
dc.contributor.authorFernández Salas, Juan
dc.contributor.authorJalón Ramírez, María Lourdes 
dc.date.accessioned2022-06-28T07:59:54Z
dc.date.available2022-06-28T07:59:54Z
dc.date.issued2022-05-19
dc.identifier.citationManuel Chiachío... [et al.]. Structural digital twin framework: Formulation and technology integration, Automation in Construction, Volume 140, 2022, 104333, ISSN 0926-5805, [https://doi.org/10.1016/j.autcon.2022.104333]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/75690
dc.description.abstractThis work presents a digital twin framework for structural engineering. The digital twin is conceptualised and mathematically idealised within the context of structural integrity, and includes the main attributes to behave as a functional digital twin, namely simulation, learning, and management. The manuscript makes special emphasis on the autonomous interactions between the physical and digital counterparts along with on the workflow modelling of the digital twin, which are both missing aspects in the majority of use cases found in the literature, specially within the civil and structural engineering domain. The proposed framework is demonstrated in a proof of concept using a laboratory scale test structure monitored using internet-of-things-based sensors and actuators. The results reveal that the virtual counterpart can respond in real-time with self-adaptability in liaison to the performance of the physical counterpart. Moreover, the tests show that the digital twin is able to provide automated decision making for structural integrity.es_ES
dc.description.sponsorshipEuropean Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie 859957es_ES
dc.description.sponsorshipEuropean Union's Horizon 2020 research and innovation programme 821054es_ES
dc.description.sponsorshipUniversidad de Granada/CBUAes_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectDigital twines_ES
dc.subjectPetri netses_ES
dc.subjectBayesian learninges_ES
dc.subjectInternet of thingses_ES
dc.subjectStructural health monitoringes_ES
dc.titleStructural digital twin framework: Formulation and technology integrationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/859957es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/821054es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1016/j.autcon.2022.104333
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


Ficheros en el ítem

[PDF]

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Atribución-NoComercial-SinDerivadas 3.0 España
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución-NoComercial-SinDerivadas 3.0 España