Reinforced concrete (RC) structures, such as RC bridges, are designed to remain safe and functional for their lifetime, during which the impacts of aging may result in performance degradation. Steel bar corrosion is one of the most common causes of structural performance degradation in RC structures subjected to service loads or natural hazards, e.g., earthquakes in seismic-prone areas. Therefore, to ensure the adequate seismic performance of RC structures over the course of their life, it is necessary to investigate the effect of corrosion on seismic performance prediction.

To this end, this research work develops required tools for seismic performance assessment, including advanced finite element (FE) modeling and Bayesian parameter estimation strategies for corroded RC structures. The newly developed advanced FE modeling strategy in this thesis can capture the corrosion impact on bonding between steel bars and surrounding concrete, as well as the vulnerability of steel bars to buckling in addition to other effects on the steel bar cross-sectional area, cover concrete spalling, and confinement level for core concrete. One of the main novelties of this FE modeling approach is that an enhanced fiber-based frame element with bond-slip is developed and probabilistic models for uncorroded and corroded bonding properties are established in order to account for the corrosion-affected bonding in the nonlinear behavior simulation of corroded RC structures. Additionally, a constitutive model for steel bar with buckling is developed that can simulate the steel bar buckling behavior restrained by flexible transverse reinforcement. This newly developed model is capable to simulate bridge structures under extreme health and loading conditions, specifically, considering the impact of corroded longitudinal steel bar and transverse reinforcement on the steel bar buckling behavior, which reflects the increased vulnerability of corroded RC bridges to steel bar buckling due to corrosion. To estimate the corrosion-related properties of RC bridges, this research work extends the application of the nonlinear FE model updating to corroded RC structures and provides a methodology to estimate the corrosion state from the seismic measurement of RC structures with high nonlinearity.

Using these newly developed strategies, the seismic performance of an RC bridge, impacted by corrosion over the course of its life, is examined in a probabilistic framework. In particular, it has been demonstrated that the conventional FE modeling approach, which neglects the corrosion-affected bond-slip and steel bar buckling, would lead to underestimated seismic risk for corroded RC bridges, specifically the seismic risk associated with the post-peak behavior. In addition, adopting the corrosion state estimation strategy results in more accurate estimations for the corrosion-affected properties, which can be used to update the FE model. The updated FE model of the corroded RC bridge can potentially be utilized for a more reliable seismic performance assessment, which assists in future decision-making for the repair or retrofit of the existing bridge.