Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam
The deep learning method of the recurrent neural network (RNN) is applied to predict the chaotic vibrations of a laminated composite cantilever beam. The RNN model converts time series data into a multi-step supervised learning format and normalizes it using MinMaxScaler. The cantilever structure is...
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MDPI AG
2025-06-01
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| author | Xudong Li Lin Sun Xiaopei Liu Yili Duo |
| author_facet | Xudong Li Lin Sun Xiaopei Liu Yili Duo |
| author_sort | Xudong Li |
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| description | The deep learning method of the recurrent neural network (RNN) is applied to predict the chaotic vibrations of a laminated composite cantilever beam. The RNN model converts time series data into a multi-step supervised learning format and normalizes it using MinMaxScaler. The cantilever structure is subjected to an evenly distributed load, and a series of chaotic vibrations are observed corresponding to different amplitudes and angular velocities of the load. Then, the RNN data-driven model is applied to predict chaotic vibrations, and the chaotic vibration prediction of RNN is evaluated. The prediction results are primarily evaluated using two metrics: mean absolute error (MAE) and root mean square error (RMSE). The analysis results show that the maximum MAE is 0.041 and the maximum RMSE is 0.067. Even under perturbed initial conditions, the RNN model maintained high prediction accuracy, with a maximum MAE of 0.022 and RMSE of 0.038, highlighting its robustness and reliability in predicting chaotic vibrations. The error analysis indicates that the RNN accurately predicts chaotic vibrations with a high degree of precision. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-db4aa673e3694f8396fedd8699c8d7f52025-08-20T02:24:39ZengMDPI AGApplied Sciences2076-34172025-06-011512640310.3390/app15126403Chaotic Vibration Prediction of a Laminated Composite Cantilever BeamXudong Li0Lin Sun1Xiaopei Liu2Yili Duo3School of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun 113001, ChinaSchool of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun 113001, ChinaSchool of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun 113001, ChinaSchool of Environmental and Safety Engineering, Liaoning Petrochemical University, Fushun 113001, ChinaThe deep learning method of the recurrent neural network (RNN) is applied to predict the chaotic vibrations of a laminated composite cantilever beam. The RNN model converts time series data into a multi-step supervised learning format and normalizes it using MinMaxScaler. The cantilever structure is subjected to an evenly distributed load, and a series of chaotic vibrations are observed corresponding to different amplitudes and angular velocities of the load. Then, the RNN data-driven model is applied to predict chaotic vibrations, and the chaotic vibration prediction of RNN is evaluated. The prediction results are primarily evaluated using two metrics: mean absolute error (MAE) and root mean square error (RMSE). The analysis results show that the maximum MAE is 0.041 and the maximum RMSE is 0.067. Even under perturbed initial conditions, the RNN model maintained high prediction accuracy, with a maximum MAE of 0.022 and RMSE of 0.038, highlighting its robustness and reliability in predicting chaotic vibrations. The error analysis indicates that the RNN accurately predicts chaotic vibrations with a high degree of precision.https://www.mdpi.com/2076-3417/15/12/6403deep learningneural networksRNNchaotic vibration predictionlaminated composite beamcantilever structure |
| spellingShingle | Xudong Li Lin Sun Xiaopei Liu Yili Duo Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam Applied Sciences deep learning neural networks RNN chaotic vibration prediction laminated composite beam cantilever structure |
| title | Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam |
| title_full | Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam |
| title_fullStr | Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam |
| title_full_unstemmed | Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam |
| title_short | Chaotic Vibration Prediction of a Laminated Composite Cantilever Beam |
| title_sort | chaotic vibration prediction of a laminated composite cantilever beam |
| topic | deep learning neural networks RNN chaotic vibration prediction laminated composite beam cantilever structure |
| url | https://www.mdpi.com/2076-3417/15/12/6403 |
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