Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/58998

TitleProbabilistic-based assessment of existing steel-concrete composite bridges – Application to Sousa River Bridge
Author(s)Matos, José C.
Moreira, Vicente Novo
Valente, Isabel B.
Cruz, Paulo J. S.
Neves, Luís C.
Galvão, Neryvaldo
KeywordsProbabilistic-based assessment
Uncertainty sources
Steel-concrete composite bridges
Existing structures
Bayesian inference
Model identification
Issue dateFeb-2019
PublisherElsevier
JournalEngineering Structures
CitationC. Matos, José & N. Moreira, Vicente & Valente, Isabel & J.S. Cruz, Paulo & Neves, Luis & Galvão, Neryvaldo. (2019). Probabilistic-based assessment of existing steel-concrete composite bridges – Application to Sousa River Bridge. Engineering Structures. 181. 95-110. 10.1016/j.engstruct.2018.12.006.
Abstract(s)This paper presents a framework to assess the safety of existing structures, combining deterministic model identification and reliability assessment techniques, considering both load-test and complementary laboratory test results. Firstly, the proposed framework, as well as the most significant uncertainty sources are presented. Then, the developed model identification procedure is described. Reliability methods are then used to compute structural safety, considering the updated model from model identification. Data acquisition, such as that collected by monitoring, non-destructive or material characterization tests, is a standard procedure during safety assessment analysis. Hence, Bayesian inference is introduced into the developed framework, in order to update and reduce the statistical uncertainty. Lastly, the application of this framework to a case study is presented. The example analyzed is a steel and concrete composite bridge. The load test, the developed numerical model and the obtained results are discussed in detail. The use of model identification allows the development of more reliable structural models, while Bayesian updating leads to a significant reduction in uncertainty. The combination of both methods allows for a more accurate assessment of structural safety
TypeArticle
URIhttp://hdl.handle.net/1822/58998
DOI10.1016/j.engstruct.2018.12.006
ISSN0141-0296
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S0141029618321345
Peer-Reviewedyes
AccessRestricted access (Author)
Appears in Collections:ISISE - Artigos em Revistas Internacionais

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