基于长短期记忆神经网络的方柱表面风压时程预测  

PREDICTION OF WIND PRESSURE TIME SERIES ON SQUARE CYLINDER UPON LONG SHORT-TERM MEMORY NEURAL NETWORK

作　　者：杜晓庆[1] 鲁羿 董浩天 胡采瑶 DU Xiao-qing;LU Yi;DONG Hao-tian;HU Cai-yao(Department of Civil Engineering,Shanghai University,Shanghai 200444,China;Department of Bridge Engineering,Tongji University,Shanghai 200092,China)

机构地区：[1]上海大学土木工程系,上海200444 [2]同济大学桥梁系,上海200092

出　　处：《工程力学》2025年第4期130-138,186,共10页Engineering Mechanics

基　　金：国家自然科学基金项目(51978392,52008239)。

摘　　要：该文提出了一种基于长短期记忆神经网络的风压时程预测模型,可通过少量测点的风压时间序列预测结构周向未知位置的风压时程。基于不同风向角下均匀来流方柱测压风洞试验数据,模型有效预测了方柱表面未知位置的风压时程。模型数据集需考虑合理序列长度范围内的关联性,以提高预测精度;多层网络结构能够提高模型的数据特征捕捉能力;训练测点数量的增加可以改善预测效果,但需考虑预测精度和测点布置经济性之间的平衡。平均风压分布、脉动风压分布和典型测点风压时程的预测值与试验值较为吻合,但方柱角点附近风压极值的预测误差相对较高,可能与该区域风压非高斯特征较强有关。Based on Long Short-Term Memory(LSTM)neural network,this paper predicts the wind pressure time series on unknown circumferential locations of the structure using measured data from only a few pressure taps.Using the wind pressure data measured from the wind tunnel test of uniform flow past a square cylinder at various incidences,the LSTM model has effectively predicted the time series of pressure on the cylinder.When choosing the correlation length of model data,the temporal correlation of wind pressure should be considered to achieve better prediction results.The multiple layers of neural network can improve the precision.More training taps produce better results,but a balance between precision and efficiency should be considered.The predicted results of mean and fluctuating pressure coefficients,as well as the pressure time series of typical taps,meet well with those of experiments.However,the prediction error near cylinder’s corners is relatively large,which might be related to the strong non-Gaussian characteristic of pressure.

关 键 词：风压预测 方柱 风洞试验 机器学习 长短期记忆神经网络 

分 类 号：TU312.1[建筑科学—结构工程]