Probabilistic-based assessment of existing steel-concrete composite bridges – Application to Sousa River Bridge. (15th February 2019)

• Record Type: Journal Article
• Authors: Matos, José C.; Moreira, Vicente N.; Valente, Isabel B.; Cruz, Paulo J.S.; Neves, Luís C.; Galvão, Neryvaldo
• Is Part Of: Engineering structures. Volume 181(2019)
• Page Start: 95
• Page End: 110
• Publication Date: 2019-02-15
• Subjects: Probabilistic-based assessment -- Uncertainty sources -- Steel-concrete composite bridges -- Existing structures -- Bayesian inference -- Model identification; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2018.12.006
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Assessment of existing in-service Portuguese roadway composite steel-concrete bridge. Both numerical and experimental uncertainty sources are taken into consideration. Experimental data is included through model identification and Bayesian inference. Applied framework allows an accurate and whole life reliability assessment. An additional reserve strength was identified through the presented analysis. 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.