基于WOA-BP-LSTM自编码器的CFRP薄壁C柱轴压响应预测OA

To predict the force-displacement responses of Carbon Fiber Reinforced Plastic(CFRP)thin-walled C-columns in the aircraft sub-cargo area under quasi-static axial crushing,an intelligent prediction model(WOA-BP-LSTM autoencoder model)integrating the Whale Optimization Algorithm(WOA),Back Propagation(BP)neural net-work,and Long Short-Term Memory(LSTM)autoencoder was proposed.The reliability of the finite element model of CFRP thin-walled C-columns was validated through quasi-static axial crushing tests,with axial crushing response evaluation indicators showing errors within 10%.A dataset comprising 700 force-displacement response samples with variable cross-sectional geometric parameters was constructed based on the model.The LSTM autoencoder was em-ployed for dimensionality reduction and reconstruction of the force-displacement responses.Subsequently,the BP neural network was used for force-displacement responses prediction,with WOA optimizing the neural network param-eters.The results show that the LSTM autoencoder achieved high-precision reconstruction of force-displacement re-sponses,where the errors for initial peak crushing force and energy absorption in the test set were both less than 3%,and 80%of the samples had errors within 1%.The optimized prediction model significantly improved prediction accu-racy,reducing the test set's Mean Absolute Error(MAE)by 17.55%,Mean Squared Error(MSE)by 31.77%,and Root Mean Squared Error(RMSE)by 17.47%.Prediction errors for the initial peak crushing force and energy absorp-tion were both less than 8%,with 80%of samples showing errors within 5%.This model enables rapid and accurate prediction of axial crushing responses for variable cross-section CFRP thin-walled C-columns while reducing computa-tional costs,providing an efficient parameter-performance mapping tool for the study of its axial crushing response.