Prediction Model for Time-varying Safety Factor for Gravity Dam Stability Based on CNN-BiLSTM-Attention
Under complex working conditions such as high water pressure and high seepage pressure, accurately grasping the time-varying law of the safety factor of gravity dams and effectively predicting it are crucial for the scientific control of the dam's operation status. To this end, a coupled predic...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2025-04-01
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| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2025.04.001 |
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| Summary: | Under complex working conditions such as high water pressure and high seepage pressure, accurately grasping the time-varying law of the safety factor of gravity dams and effectively predicting it are crucial for the scientific control of the dam's operation status. To this end, a coupled prediction model is proposed based on the CNN-BiLSTM-Attention method of deep learning theory, with the upstream water level, the riverward displacement at the dam crest, and time-dependent effects as independent variables, and the anti-sliding stability coefficient as the dependent variable. Through the analysis of a gravity dam project with a height of 148.0 meters, the model demonstrates a mean absolute error (MAE) and root mean square error (RMSE) of 1.12×10<sup>-3</sup> and 1.66×10<sup>-3</sup>, respectively, prediction errors MAE and RMSE<italic> </italic>of 3.08×10<sup>-3</sup> and 3.53×10<sup>-3</sup>, respectively. Compared to traditional statistical regression methods, this model has increased the prediction accuracy by 51.80% and 45.44%, and when compared to the SVM algorithm, the prediction accuracy has increased by 16.08% and 10.18%, respectively. This indicates that the proposed model has a better alignment with the finite element calculation result curves and a more remarkable advantage in prediction accuracy. |
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| ISSN: | 1001-9235 |