  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-driven 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 wind tunnel data for
calibration and validation of the data-driven POD-based stochastic model.
Errors associated with the use of typical wind tunnel records for model
calibration, the model itself, and the truncation of modes were quantified.
Results demonstrate that the data-driven model can efficiently simulate
stochastic wind loads with negligible model errors, while the errors associated
with calibration to typical wind tunnel data can be important.
