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. 

This thesis aims to assess the remaining useful life of two representative bridge types in Sweden by combining full-scale experimental tests, Finite Element Analysis (FEA), and time-dependent reliability analysis. The combination of these methods seeks to enhance solution accuracy and reduce uncertainties. As the bridge population approaches its intended design life, concerns regarding their current condition start to rise. Recent bridge failures are proof of the need for experts to increase their efforts to accurately assess existing bridges' remaining useful life. However, structural remaining lifetime prediction is a challenging task given the complexity of structural behavior and the various environmental threats the structure faces. Additionally, inherent uncertainties are part of any engineering problem, making an exact solution difficult to achieve. Therefore, introducing probabilistic-based concepts to determine the structure capacity helps account for those uncertainties typically addressed in structural reliability analysis. Time-dependent reliability analysis offers a tool to assess the remaining useful life of a structure, expressed in terms of its time to failure. 

The first case study corresponds to a road existing bridge in north Sweden that has been already demolished. The structure is a prestressed box-girder concrete bridge, and it was 66 years old at the time of experimental data collection. The second case is a reinforced concrete (RC) trough railway bridge, which is a representative bridge type in Sweden. The trough bridge was cast at LTU in 2021 as a replica of the design of a decommissioned trough bridge from the Iron Ore Line. The experiments performed in both case studies are used for two main purposes: the calibration of the Finite Element (FE) models and the update of the probability distributions of the parameters involved in the time-dependent reliability analysis. This will help a better FE model to represent structural behavior and more accurate probabilistic models. Different sensors were implemented during the experimental data collection, such as fiber optic sensors (FOS), traditional strain gauges, and linear variable differential transformers (LVDTs).

Given that the communication between FE and reliability analyses can be computational overwhelming, this thesis proposes an improved metamodel-based reliability algorithm which 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 FEA. 

The results of this research provide a practical understanding of the stochastic process for resistance deterioration in both cases.  This includes degradation due to prestressing losses for the Kalix bridge and fatigue for the trough bridge. By considering the stochastic process over the years and the random nature of the loads over time, we were able to calculate the remaining useful life for the cases of no experimental information and updated using experimental data. These findings have direct implications for the maintenance and management of similar structures, providing valuable insights for the field of civil engineering and structural reliability analysis.