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Prediction of axial crushing response for CFRP thin-walled C-columns based on WOA-BP-LSTM autoencoder
Received date: 2025-05-30
Revised date: 2025-07-03
Accepted date: 2025-07-28
Online published: 2025-07-30
Supported by
National Natural Science Foundation of China(U2433203);Tianjin Applied Basic Research Multi-Input Fund Project(23JCYBJC00070);Fundamental Research Funds for the Central Universities(3122025084);Graduate Scientific Research Innovation Project of Civil Aviation University of China(2024YJSKC09002)
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 network， 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 employed 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 parameters. The results show that the LSTM autoencoder achieved high-precision reconstruction of force-displacement responses， 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 accuracy， 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 absorption 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 computational costs， providing an efficient parameter-performance mapping tool for the study of its axial crushing response.
Haolei MOU , Jia ZHANG , Zhenyu FENG , Chunyu BAI . Prediction of axial crushing response for CFRP thin-walled C-columns based on WOA-BP-LSTM autoencoder[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2026 , 47(4) : 232349 -232349 . DOI: 10.7527/S1000-6893.2025.32349
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