Over the last few decades, evaluating the performance of existing structures has become increasingly important, particularly as the number of bridges reaching their design life continues to rise. As a result, there is a growing need for effective and accurate procedures to guide the assessment of the current structures' capacity and safety levels to implement appropriate maintenance and rehabilitation strategies. Evaluating a structure's performance involves assessing its ability to carry loads, resist external forces, and maintain its functionality over time. This is a complex process that requires a deep understanding of the structure's behavior, as well as knowledge of the environmental conditions it is subjected to.

In recent years, technological advances and an increased understanding of reliability concepts have allowed for the development of more sophisticated tools and methods for structural evaluation. Thus, engineers and researchers can obtain more accurate and reliable data about a structure's performance, which can inform decision-making processes related to maintenance, repair, and replacement.

This study aims to present a methodology that guides the assessment of existing structures' performance effectively and accurately. Precisely, the performance is measured in terms of redundancy and robustness. Thus, a comparison of existing reliability- and risk-based indicators is performed through an example application presented in one of the appended papers. The comparison allows an overview of the difference between the available measures and the type of information provided by each one of them.

Also, in one of the appended papers a new algorithm for evaluating the failure probability value is proposed. The algorithm is based on metamodel strategies and integrates the advantages of kriging, learning, and copula functions. The proposed algorithm aims to reduce the number of performance function evaluations, so the number of model runs is feasible when using Finite Element Modeling (FEM).

By comparing the available redundancy and robustness indicators, it was possible to observe that each measure provides different insights into these two structural properties. Additionally, direct comparison between them is challenging since their units can differ, and the lack of a target or standard values makes their interpretation difficult. Therefore, when using a specific indicator, it is required to specify the definition adopted clearly.

Furthermore, the proposed algorithm showed through the validation examples and the case study that it can obtain the failure probability accurately and effectively. Its application resulted in a more economical methodology, in terms of computational cost, compared to other existing reliability methods.