Damage detection of riveted truss bridge using ANN-aided AMS optimization method

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dc.contributor.authorŠplíchal, Bohumilcs
dc.contributor.authorLehký, Davidcs
dc.contributor.authorLamperová, Katarínacs
dc.date.accessioned2025-03-11T09:36:55Z
dc.date.available2025-03-11T09:36:55Z
dc.date.issued2024-07-12cs
dc.description.abstractAging transport infrastructure brings increased economic burden and uncertainties regarding the reliability, durability and safe use of structures. Early damage detection to locate incipient damage provides an opportunity for early structural maintenance and can guarantee structural reliability and continuing serviceability. This paper describes the use of the hybrid identification method, which combines a metaheuristic optimization technique aimed multilevel sampling with an artificial neural network-based surrogate model to approximate the inverse relationship between structural response and structural parameters. The method is applied to identify damage in existing riveted truss bridge. The effect of the damage rate and location on the identification speed and the accuracy of the solution is investigated and discussed.en
dc.formattextcs
dc.format.extent2279-2286cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationBridge Maintenance, Safety, Management, Digitalization and Sustainability. 2024, p. 2279-2286.en
dc.identifier.doi10.1201/9781003483755-270cs
dc.identifier.isbn9781003483755cs
dc.identifier.orcid0009-0000-2906-142Xcs
dc.identifier.orcid0000-0001-8176-4114cs
dc.identifier.other188906cs
dc.identifier.researcheridAAK-9492-2020cs
dc.identifier.scopus57818031000cs
dc.identifier.scopus56389654700cs
dc.identifier.urihttps://hdl.handle.net/11012/250094
dc.language.isoencs
dc.publisherCRC Presscs
dc.relation.ispartofBridge Maintenance, Safety, Management, Digitalization and Sustainabilitycs
dc.relation.urihttps://www.taylorfrancis.com/books/oa-edit/10.1201/9781003483755cs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/cs
dc.subjectDamage identificationen
dc.subjectModel updatingen
dc.subjectArtificial neural networken
dc.subjectOptimization methoden
dc.titleDamage detection of riveted truss bridge using ANN-aided AMS optimization methoden
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.grantNumberinfo:eu-repo/grantAgreement/GA0/GA/GA23-04712Scs
sync.item.dbidVAV-188906en
sync.item.dbtypeVAVen
sync.item.insts2025.03.11 10:36:55en
sync.item.modts2025.03.07 10:31:58en
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav stavební mechanikycs
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