东南大学学报（自然科学版）2026，Vol.56Issue(2)：268-276,9.DOI:10.3969/j.issn.1001-0505.2026.02.010
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
Time-history prediction of bridge static three-component forces using angle-fused convolutional-transformer based on physical constraints and feature synergy
摘要
Abstract
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.关键词
三分力时程预测/桥梁抗风气动力/物理约束融合/攻角融合卷积-Transformer/训练效率优化Key words
three-component force time-history prediction/bridge aerodynamics/physical constraint fusion/angle-fused convolutional-transformer(AFConv-Transformer)/training efficiency optimization分类
交通工程引用本文复制引用
孙洪鑫,罗臻懿,燕飞,张明,欧阳鹭伟..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[J].东南大学学报（自然科学版）,2026,56(2):268-276,9.基金项目
国家自然科学基金资助项目(52478514). （52478514）
