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2019.00164.CBM"]}]},{"name":"FAPESP","award":["309467\/2023-3"],"award-info":[{"award-number":["309467\/2023-3"]}]},{"name":"FAPESP","award":["24\/00720-8"],"award-info":[{"award-number":["24\/00720-8"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Infrastructures"],"abstract":"<jats:p>Bridges are crucial transportation infrastructures with significant socioeconomic impacts, necessitating continuous assessment to ensure safe operation. However, the vast number of bridges and the technical and financial challenges of maintaining permanent monitoring systems in every single bridge make the implementation of structural health monitoring (SHM) difficult for authorities. Unsupervised transfer learning, which reuses experimental or numerical data from well-known bridges to detect damage on other bridges with limited monitoring response data, has emerged as a promising solution. This solution can reduce SHM costs while ensuring the safety of bridges with similar characteristics. This paper investigates the limitations, challenges, and opportunities of unsupervised transfer learning via domain adaptation across datasets from various prestressed concrete bridges under distinct operational and environmental conditions. A feature-based transfer learning approach is proposed, where the joint distribution adaptation method is used for domain adaptation. As the main advantage, this study leverages the generalization of SHM for damage detection in prestressed concrete bridges with limited long-term monitoring data.<\/jats:p>","DOI":"10.3390\/infrastructures9080131","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T12:13:14Z","timestamp":1723119194000},"page":"131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Comprehensive Study on Unsupervised Transfer Learning for Structural Health Monitoring of Bridges Using Joint Distribution Adaptation"],"prefix":"10.3390","volume":"9","author":[{"given":"Laura","family":"Souza","sequence":"first","affiliation":[{"name":"Applied Electromagnetism Laboratory, Universidade Federal do Par\u00e1, R. Augusto Corr\u00eaa, Guam\u00e1 01, Bel\u00e9m 66075-110, PA, Brazil"}]},{"given":"Marcus Omori","family":"Yano","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia Mec\u00e2nica, UNESP\u2014Universidade Estadual Paulista, Ilha Solteira 15385-000, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6430-3746","authenticated-orcid":false,"given":"Samuel","family":"da Silva","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia Mec\u00e2nica, UNESP\u2014Universidade Estadual Paulista, Ilha Solteira 15385-000, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9168-6903","authenticated-orcid":false,"given":"Eloi","family":"Figueiredo","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Lus\u00f3fona University, Campo Grande 376, 1749-024 Lisboa, Portugal"},{"name":"CERIS, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3018","DOI":"10.1177\/14759217221075241","article-title":"Three decades of statistical pattern recognition paradigm for SHM of bridges","volume":"21","author":"Figueiredo","year":"2022","journal-title":"Struct. Health Monit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1177\/1475921710388971","article-title":"Machine Learning Algorithms for Damage Detection under Operational and Environmental Variability","volume":"10","author":"Figueiredo","year":"2011","journal-title":"Struct. Health Monit."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Farrar, C., and Worden, K. (2013). 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