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Finding an optimal design for a structural system subject to seismic actions to minimize failure probability, repair costs, and injuries to occupants, significantly contributes to the resilience of buildings in earthquake regions. This research presents a comprehensive framework for the performance-based design optimization of steel structures, incorporating the Performance-Based Earthquake Engineering (PBEE) methodology delineated in FEMA P-58 [1]. A selected set of ground motions, consistent with the seismic hazard intensity of interest, and a nonlinear finite element model, established using OpenSees, enable the assessment of the system's dynamic response. To address the computational complexity related to evaluating the probability of failure of the system during an optimization iteration when using the PBEE methodology for assessing performance, this study introduces metamodeling techniques as a substitute for the original high-fidelity nonlinear finite element model. In particular, Kriging is employed to approximate both the median and standard deviation of the Engineering Demand Parameters (EDPs) in the design domain. The parameters of the Kriging metamodels are derived from nonlinear dynamic analyses performed using the original high-fidelity model and an optimal sampling plan obtained through Latin Hypercube sampling. Under the assumption of a lognormal distribution, the metamodel is then used to generate a large number of simulated demand sets necessary for the Monte Carlo procedure adopted by FEMA P-58 to calculate the distribution of probable losses for any given value of the design variable vector. Additionally, the median and standard deviation of the fragility function modeling collapse are also approximated by a Kriging metamodel, in which the parameters are derived from an Incremental Dynamic Analysis (IDA) for any given value of the design variable vector. The scheme is illustrated in a full-scale case study consisting of the performance-based optimization of the buckling-restrained braces of a steel seismic force-resisting system in terms of expected losses and construction costs. The study demonstrates that the proposed risk-based optimization scheme effectively balances construction costs with expected financial losses from earthquakes, thus enhancing the seismic performance of the system.[1] Applied Technology Council, & National Earthquake Hazards Reduction Program (US). (2012). Seismic performance assessment of buildings. Federal Emergency Management Agency. 

The simulation of stochastic wind loads is necessary for many applications in wind engineering. The proper-orthogonal-decomposition-(POD)-based spectral representation method is a popular approach used for this purpose, due to its computational efficiency. For general wind directions and building configurations, the data-informed POD-based stochastic model is an alternative that uses wind-tunnel-smoothed auto- and cross-spectral density as input, to calibrate the eigenvalues and eigenvectors of the target load process. Even though this method is straightforward and presents advantages, compared to using empirical target auto- and cross-spectral density, the limitations and errors associated with this model have not been investigated. To this end, an extensive experimental study on a rectangular building model considering multiple wind directions and configurations was conducted, to allow the quantification of uncertainty related to the use of short-duration wind tunnel records for calibration and validation of the data-informed POD-based stochastic model. The results demonstrate that the data-informed model can efficiently simulate stochastic wind loads with negligible model errors, while the errors associated with calibration to short-duration wind tunnel data can be important.