Wind Pressure Reconstruction at Unmeasured Locations on Long-Span Curved Roofs by POD-GAT

Accurate estimation of wind loads is essential for the structural design and performance assessment of long-span curved roofs, which are highly susceptible to wind pressures. However, the number of pressure taps typically used in wind tunnel tests is limited, and conventional interpolation and reconstruction methods may not adequately capture the non-uniform spatial dependencies of wind pressures, particularly in regions affected by flow separation and pronounced roof curvature. To address this issue, this study proposes a hybrid reconstruction framework that integrates Proper Orthogonal Decomposition (POD) with a Graph Attention Network (GAT) to estimate wind pressures at unmeasured locations on long-span curved roofs, including pressure coefficients, pressure time histories, and spectral characteristics. POD is performed independently for each wind direction. For a representative wind direction, the first 50 modes are retained, capturing 96.7% of the fluctuating-pressure energy. Subsequently, a spatial graph is constructed according to the arrangement of the pressure taps, and the GAT is used to adaptively learn the spatial correlations among the taps. This enables the reconstruction of wind-pressure fields under sparse measurement conditions. Across the evaluated cases, the proposed POD–GAT model achieves R2 values exceeding 0.90 for both the mean and fluctuating pressure coefficients. In critical regions characterized by severe flow separation and pronounced surface curvature, the reconstruction based on the POD modal coefficients predicted by the GAT generally yields smaller absolute errors than those obtained using radial basis function (RBF), convolutional neural network (CNN), and multilayer perceptron (MLP) models. In addition, the reconstructed power spectral densities agree well with the experimental results, with only a minor loss of high-frequency energy. The model also maintains stable prediction accuracy for wind directions of 0°, 45°, and 90°. Overall, the proposed method effectively captures complex spatial dependencies while maintaining computational efficiency, thereby providing a reliable approach for refined wind-pressure reconstruction and wind-load assessment of long-span curved roofs.