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| DOI：10.13522/j.cnki.ggps.2024213 |
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| Prediction of dam deformation using adaptive noise CEEMDAN and BiGRU time series modeling |
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WANG Zixuan, OU Bin, CHEN Dehui, YANG Shiyong, ZHAO Dingzhu, FU Shuyan
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1. College of Water Conservancy, Yunnan Agricultural University, Kunming 650201, China;
2. The National Key Laboratory of Water Disaster Prevention, Nanjing 210098, China;
3. Research Center for Smart Management and Maintenance of Small and
Medium-sized Water Conservancy Projects in Yunnan Province, Kunming 650201, China
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| Abstract: |
| 【Background and Objective】Accurate prediction of dam deformation is crucial for ensuring the safety of dam structures in engineering monitoring. Dam deformation is influenced by multiple factors, including water pressure, temperature, and material aging, which often exhibit nonlinear and dynamic relationships. During monitoring, system noise and observation errors frequently interfere with data quality, posing additional challenges for analysis. To address the challenges posed by system noise and strong nonlinear effects in dam deformation, this paper proposes a dam deformation monitoring model based on multi-layer integrated signal processing technology.【Method】The model uses sample entropy reconstruction and the K-means clustering algorithm to optimize the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) process, generating multiple intrinsic mode functions (IMF). High-frequency modal components undergo secondary decomposition using variational mode decomposition (VMD) to extract the optimal intrinsic mode function. Finally, an improved symbiotic biological search algorithm combined with a Bidirectional Gated Recurrent Unit (BiGRU) is used to accurately predict dam deformation.【Result】Case analysis demonstrates that, compared to traditional prediction models, the proposed model achieves a root mean square error (RMSE) of 0.031 9 mm, mean absolute error (MAE) of 0.015 3 mm, mean absolute percentage error (MAPE) of 2.51%, and determination coefficient (R2) of 0.971 2.【Conclusion】 The results verify that the proposed model captures and simulates the dam deformation process more accurately, exhibiting higher prediction accuracy and stronger generalization ability. |
| Key words: dam deformation; complete ensemble empirical mode decomposition of adaptive noise; sample entropy reconstruction; K-means clustering algorithm; symbiotic search algorithm; variational mode decomposition |
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