Damage quantification using transfer component analysis combined with Gaussian process regression

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorLusófona University-
Autor(es): dc.contributorUniversidade de Lisboa-
Autor(es): dc.contributorUniversidade Federal do Espírito Santo (UFES)-
Autor(es): dc.creatorYano, Marcus Omori-
Autor(es): dc.creatorSilva, Samuel da-
Autor(es): dc.creatorFigueiredo, Eloi-
Autor(es): dc.creatorVillani, Luis G Giacon-
Data de aceite: dc.date.accessioned2025-08-21T15:11:48Z-
Data de disponibilização: dc.date.available2025-08-21T15:11:48Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1177/14759217221094500-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240171-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240171-
Descrição: dc.descriptionMachine learning methods used in Structural Health Monitoring applications still have generalization difficulties among structures, even when structures are nominally and topologically similar. The data sets present divergences between their probability distributions that do not allow the model’s generalization for damage detection. This issue is even more complex in situations where one wants to quantify damage levels through data sets collected from different structures. Transfer learning methods offer a solution to overcome those limitations, using relevant information from a labeled structure (source domain) to assist the analysis of another structure (target domain) under unknown conditions. Therefore, this paper proposes the use of transfer component analysis to mitigate divergences between the model/structure’s features, and the label consistency requirement is applied in combination with a Gaussian process regression model for damage quantification. The effectiveness of the estimated model improves when the labels consistency between domains is achieved, indicating the current damage level in the structure when the regression model achieves its best performance (lowest error). The proposed methodology is applied on the benchmark data of a three-story building structure from the Los Alamos National Laboratory using the knowledge from its numerical model under several conditions, where the complete information of its behavior is available. The results compare the analysis in the original space and after applying the proposed methodology, demonstrating an improvement of the performance in the damage detection and quantification steps.-
Descrição: dc.descriptionDepartamento de Engenharia Mecânica UNESP - Universidade Estadual Paulista-
Descrição: dc.descriptionFaculty of Engineering Lusófona University-
Descrição: dc.descriptionCERIS Instituto Superior Técnico Universidade de Lisboa-
Descrição: dc.descriptionDepartamento de Engenharia Mecânica Centro Tecnológico UFES - Universidade Federal do Espírito Santo-
Descrição: dc.descriptionDepartamento de Engenharia Mecânica UNESP - Universidade Estadual Paulista-
Idioma: dc.languageen-
Relação: dc.relationStructural Health Monitoring-
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Palavras-chave: dc.subjectdamage identification-
Palavras-chave: dc.subjectdomain adaptation-
Palavras-chave: dc.subjectGaussian process regression-
Palavras-chave: dc.subjectStructural Health Monitoring-
Palavras-chave: dc.subjecttransfer component analysis-
Palavras-chave: dc.subjectTransfer learning-
Título: dc.titleDamage quantification using transfer component analysis combined with Gaussian process regression-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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