基于WOA-BP-LSTM自编码器的CFRP薄壁C柱轴压响应预测
收稿日期: 2025-05-30
修回日期: 2025-07-03
录用日期: 2025-07-28
网络出版日期: 2025-07-30
基金资助
国家自然科学基金(U2433203);天津市应用基础研究多元投入基金(23JCYBJC00070);中央高校基本科研业务费专项资金(3122025084);中国民航大学研究生科研创新项目(2024YJSKC09002)
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)
针对航空器货舱下部碳纤维增强复合材料（CFRP）薄壁C柱在准静态轴压下的力-位移响应预测问题，提出了一种融合鲸鱼优化算法（WOA）、反向传播（BP）神经网络和长短期记忆（LSTM）自编码器的智能预测模型（WOA-BP-LSTM自编码器模型）。通过CFRP薄壁C柱准静态轴压试验验证了有限元模型可靠性，其轴压响应评价指标误差均小于10%，基于该模型构建了包含700组变截面几何参数的力-位移响应数据集。采用LSTM自编码器实现力-位移响应特征降维与重建，随后采用BP神经网络对力-位移响应进行预测，并采用WOA进行神经网络参数优化。结果表明，LSTM自编码器实现了力-位移响应的高精度重建，测试集初始峰值压溃力和能量吸收的重建误差均小于3%，80%样本误差小于1%；优化后预测模型的力-位移响应预测精度显著提升，测试集平均绝对误差（MAE）降低17.55%，均方误差（MSE）降低31.77%，均方根误差（RMSE）降低17.47%，初始峰值压溃力和能量吸收的预测误差均小于8%，80%样本误差小于5%。该智能预测模型实现了变截面CFRP薄壁C柱轴压响应的快速精准预测并降低了计算成本，为其轴压响应研究提供了一种高效的参数-性能映射工具。
牟浩蕾 , 张贾 , 冯振宇 , 白春玉 . 基于WOA-BP-LSTM自编码器的CFRP薄壁C柱轴压响应预测[J]. 航空学报, 2026 , 47(4) : 232349 -232349 . DOI: 10.7527/S1000-6893.2025.32349
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.
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