Bridges are essential structures in the logistic chain of countries, making it critical to design them to be as resilient as possible. One way to achieve this is through performance-based seismic design (PBSD), which involves using nonlinear Finite Element (FE) models to predict the response and potential damage of different structural components under earthquake excitations. Nonlinear FE models need accurate constitutive models of material and components. Among them, seismic bars and laminated elastomeric bearings play an important role in a bridge’s response to earthquakes; therefore, properly validated and calibrated models should be proposed. Only default parameter values from the early development of the constitutive models widely used by researchers and practitioners for these components tend to be used, and low identifiability of its governing parameters and the high cost of generating reliable experimental data have prevented a thorough probabilistic characterization of their model parameters. To address this issue, this study implements a Bayesian probabilistic framework using Sequential Monte Carlo (SMC) for updating the parameters of constitutive models of seismic bars and elastomeric bearings and proposes joint probability density functions (PDF) for the most influential parameters. The framework is based on actual data from comprehensive experimental campaigns. The PDFs are obtained from independent tests conducted on different seismic bars and elastomeric bearings, to then consolidate all the information in a single PDF for each modeling parameter by means of the conflation methodology, where the mean, coefficient of variation, and correlation between calibrated parameters are obtained for each bridge component. Finally, findings show that the incorporation of model parameter uncertainty through a probabilistic framework will allow for a more accurate prediction of the response of bridges under strong earthquakes.

Monopile‐supported offshore wind turbines (MS‐OWTs) are increasingly deployed in seismic coastal regions, where they face compound risks from earthquake loading and seabed scour. While past studies have addressed these hazards separately, seismic fragility under evolving scour conditions remains insufficiently understood. This study introduces a probabilistic framework to evaluate the seismic fragility of MS‐OWTs across a range of scour depths, accounting for evolutionary changes in foundation stiffness, dynamic behavior, and structural vulnerability. Nonlinear dynamic analyses are performed using real ground motions within a cloud‐based approach, and scour is modeled as a loss of lateral soil support. Structural and radiation damping mechanisms are treated separately to better reflect MS‐OWT working conditions. A central contribution of this study is a comprehensive assessment of seismic intensity measures (IMs), including detailed analyses of efficiency and sufficiency. The results reveal that broadband spectral IMs provide more reliable and informative seismic demand predictions than traditional metrics such as peak ground acceleration (PGA) or peak ground velocity (PGV), particularly under scour‐modified conditions. Fragility curves show how exceedance probabilities at both serviceability and ultimate limit states change with scour depth. These findings underscore the importance of integrating scour‐induced uncertainties and IM selection into seismic design frameworks of MS‐OWTs. The proposed approach contributes to more accurate risk‐informed assessment and improved resilience of offshore wind infrastructure in multi‐hazard coastal environments.