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Structural digital twin framework: Formulation and technology integration
dc.contributor.author | Chiachío Ruano, Manuel | |
dc.contributor.author | Megía Cardeñoso, María | |
dc.contributor.author | Chiachío Ruano, Juan | |
dc.contributor.author | Fernández Salas, Juan | |
dc.contributor.author | Jalón Ramírez, María Lourdes | |
dc.date.accessioned | 2022-06-28T07:59:54Z | |
dc.date.available | 2022-06-28T07:59:54Z | |
dc.date.issued | 2022-05-19 | |
dc.identifier.citation | Manuel 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.uri | http://hdl.handle.net/10481/75690 | |
dc.description.abstract | This 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.sponsorship | European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie 859957 | es_ES |
dc.description.sponsorship | European Union's Horizon 2020 research and innovation programme 821054 | es_ES |
dc.description.sponsorship | Universidad de Granada/CBUA | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Digital twin | es_ES |
dc.subject | Petri nets | es_ES |
dc.subject | Bayesian learning | es_ES |
dc.subject | Internet of things | es_ES |
dc.subject | Structural health monitoring | es_ES |
dc.title | Structural digital twin framework: Formulation and technology integration | es_ES |
dc.type | journal article | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/859957 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/821054 | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.1016/j.autcon.2022.104333 | |
dc.type.hasVersion | VoR | es_ES |
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