Structural dynamic load prediction method based on long short-term memory network
[Objective]To address the limitations of traditional surrogate models in handling time-dependent dynamic processes and heterogeneous data,this paper proposes a dynamic load surrogate model method based on a long short-term memory(LSTM)network.[Methods]The surrogate model is comprised of two mod-ules:the load feature encoder and load response decoder.First,the LSTM in the load feature encoder per-forms feature extraction on the time series of dynamic external loads.Next,the extracted load features are combined with the structural parameter features.The LSTM in the load decoder conducts further feature ex-traction and finally generates output while comprehensively considering the heterogeneous data input of the dynamic external load time series and one-dimensional structural parameter features in order to predict the time history of internal force responses.Finally,the model's accuracy is evaluated using a finite element simu-lation dataset and compared with other surrogate model methods.[Results]The results show that the aver-age accuracy of the dynamic load surrogate model can reach 98%,which is higher than that of other methods,and its calculation speed is faster than that of the finite element method.[Conclusions]The proposed meth-od addresses the issue of heterogeneous data involving both time-series and non-time-series features,and of-fers advantages such as high accuracy and efficiency,making it effective for fast iterative computation tasks.
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