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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. ABSTRACTA 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.

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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.

Since the construction industry is the one that bears most expenses, both in financial and environmental terms, it is of the upmost importance that these expenses originate a product with a long term exploitation, so as to mitigate them. Having this problem in consideration, in this paper the bridge located in the north of Portugal, near Porto, in the oil tanker terminal at the Leixões port was studied. This structure is located in one of the most aggressive environments for concrete structures, a maritime zone. The most accepted durability models in the country, related to deterioration induced by sea chlorides penetration, were then implemented. Thereby, it was possible to identify which model better reflects reality, since the structure in analysis is now at the end of its lifetime, after 50 years of service and shows advanced degradation due to chloride attack. In the context of this work, the structure and the test methods relevant to the theme being studied were described by inspection reports and in situ test results, made available by the Douro and Viana do Castelo Port Authority. From this data it was possible to study the structure deterioration by introducing them into durability models. These models range from prescriptive to performance based approaches, being possible to identify, from the later ones, a deterministic model, based on the Model Code 2010, a semi-probabilistic based on the E465 specification from the Portuguese National Laboratory for Civil Engineering (LNEC) and two probabilistic models, based on the same standards, for which a computer code was developed during this work. Through these deterioration prediction tools, different project scenarios were established, originating a list of minimal concrete covers to ensure 100 years of lifetime to a structure built in the studied exposure zone.