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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. [ABSTRACT FROM AUTHOR]

Considering the safety assessment requirements of masonry arch bridges, different levels of reliability, based on uncertainty, may be distinguished, whose core objectives are to accurately analyse the ultimate load-carrying capacity and the serviceability structural response. Within this framework, a simplified full-probabilistic methodology for the safety assessment of existing masonry arch bridges is proposed, which combines both structural analysis and Bayesian inference procedures. The proposed framework aims to determine the ultimate load-carrying capacity (Ultimate Limit State) of masonry arch bridges, by using probabilistic procedures and Limit States principles. Geometric, material and load characterization, as well as inherent uncertainties will be also considered. In order to determine the ultimate load-carrying capacity, a limit analysis approach, based on the mechanism method, will be employed. Due to the high computational costs required by a probabilistic safety assessment framework, a sensitivity analysis will then be introduced. The incorporation of new information from monitoring and/or testing will be performed by the application of Bayesian inference methodologies. Based on the information collected, two reliability indexes will be computed and compared, one with data collected from design documentation and literature and the other with data collected from testing, emphasizing the importance of testing and the advantages of Bayesian inference procedures. The probabilistic framework developed is tested and validated in a Portuguese railway masonry arch bridge from the 19th century. [ABSTRACT FROM AUTHOR]

A novel framework for probabilistic-based structural assessment of existing structures, which combines model identification and reliability assessment procedures, considering in an objective way different sources of uncertainty, is presented in this paper. A short description of structural assessment applications, provided in literature, is initially given. Then, the developed model identification procedure, supported in a robust optimization algorithm, is presented. Special attention is given to both experimental and numerical errors, to be considered in this algorithm convergence criterion. An updated numerical model is obtained from this process. The reliability assessment procedure, which considers a probabilistic model for the structure in analysis, is then introduced, incorporating the results of the model identification procedure. The developed model is then updated, as new data is acquired, through a Bayesian inference algorithm, explicitly addressing statistical uncertainty. Finally, the developed framework is validated with a set of reinforced concrete beams, which were loaded up to failure in laboratory. [ABSTRACT FROM AUTHOR]