基于物理约束与特征协同的攻角融合卷积-Transformer桥梁静力三分力时程预测OA

To address the issue of insufficient accuracy in the current time-history prediction of static three-component forces on bridges under wind loads,an angle-fused convolutional-transformer(AFConv-Transformer)model is proposed.A one-dimensional convolutional network is used to extract local high-frequency features and a transformer encoder is utilized to capture global time-series dependencies.Multi-modal fusion is conducted by taking the angle of attack as a physical constraint to resolve the phase de-viation problem of traditional models.Then,860 sample sets are generated based on wind tunnel test data from a large-span steel box girder to validate the model.The results of the ablation experiments show that the fusion of the angle of attack is benefical for eliminating phase deviation in the prediction,while the synergistic effect between the convolutional and transformer encoder modules forms the foundation of the model's effec-tiveness.On the test set,the mean absolute error,the root mean square error,and the coefficient of determi-nation of the proposed model are 0.354 7,0.654 3,and 0.976 8,respectively.Compared with the classic angle-fused convolutional-long short-term memory(AFConv-LSTM)model,the training time decreases from 147.50 to 65.60 s,marking a significant efficiency improvement of 55.5%.This research provides an effi-cient and reliable new method for intelligent prediction of aerodynamic forces in bridge wind-resistant design.