Probabilistic-based assessment of existing steel-concrete composite bridges : application to Sousa River Bridge

Abstract

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.

Citation

Matos, J. C., Moreira, V. N., Valente, I. B., Cruz, P. J., Neves, L. C., & Galvão, N. (2019). Probabilistic-based assessment of existing steel-concrete composite bridges : application to Sousa River Bridge. Engineering Structures, 181, 95-110. https://doi.org/10.1016/j.engstruct.2018.12.006

• Journal Article Type: Article
• Acceptance Date: Dec 3, 2018
• Online Publication Date: Dec 11, 2018
• Publication Date: Feb 15, 2019
• Deposit Date: Jan 22, 2019
• Publicly Available Date: Jan 22, 2019
• Journal: Engineering Structures
• Print ISSN: 0141-0296
• Electronic ISSN: 1873-7323
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 181
• Pages: 95-110
• DOI: https://doi.org/10.1016/j.engstruct.2018.12.006
• Keywords: Civil and Structural Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/1486800
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0141029618321345