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.

翻译：随机风荷载的模拟对于风工程中的许多应用是必要的。基于本征正交分解（POD）的谱表示法因其计算效率高而成为常用方法。对于一般风向来向和建筑构型，基于数据驱动的POD随机模型是一种替代方案，它利用风洞平滑自谱与互谱密度作为输入，以校准目标荷载过程的特征值和特征向量。尽管该方法简单直观，且相比使用经验目标自谱与互谱密度具有优势，但该模型的相关局限性和误差此前尚未得到研究。为此，针对一个矩形建筑模型，在考虑多个风向来向和构型的条件下开展了广泛的实验研究，以量化使用风洞数据进行数据驱动POD随机模型校准与验证所引入的不确定性。分别量化了使用典型风洞记录进行模型校准、模型本身以及模态截断所关联的误差。结果表明，数据驱动模型能够高效模拟随机风荷载且模型误差可忽略，而与典型风洞数据校准相关的误差可能较为显著。