Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement Article Swipe
Q. Shen
,
Yuming Wu
,
Limin Wan
,
Qian Chen
,
Yue Li
,
Zhenpeng Liao
,
Wenbo Wang
,
Feng Li
,
Tao Li
,
Jiajun Shu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app142310848
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app142310848
The settlement values of subway tunnels during the construction period exhibit significant nonlinear and spatial–temporal variation characteristics. To overcome the problems of historical data interference and spatiotemporal characteristics in tunnel settlement prediction models, this paper proposes a tunnel settlement prediction method based on data decomposition, reconstruction, and optimization. First, the original data are optimized via the SSA, which has global optimization capability, high noise immunity, and high adaptivity. The original signal is subsequently decomposed into multiple subsignal sequences via a variational modal decomposition (VMD) algorithm combined with a rolling decomposition technique. Finally, the decomposed signals are fed into the machine learning model to construct a high-precision settlement prediction model based on rolling decomposition. The prediction accuracy of different models was analyzed via the measured settlement data during the construction period of the Beijing subway as an example. The results show that the prediction model with the integrated decomposition algorithm reduces the RMSE and MAE by 33% and 37%, respectively, which significantly improves the prediction accuracy and generalization ability of the neural network to meet the demand of practical engineering prediction and simultaneously enhances the risk warning ability of the model.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app142310848
- OA Status
- gold
- Cited By
- 1
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404701491
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- OpenAlex ID
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https://openalex.org/W4404701491Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app142310848Digital Object Identifier
- Title
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Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel SettlementWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-11-23Full publication date if available
- Authors
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Q. Shen, Yuming Wu, Limin Wan, Qian Chen, Yue Li, Zhenpeng Liao, Wenbo Wang, Feng Li, Tao Li, Jiajun ShuList of authors in order
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https://doi.org/10.3390/app142310848Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/app142310848Direct OA link when available
- Concepts
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Modal, Series (stratigraphy), Nonlinear system, Settlement (finance), Algorithm, Computer science, Decomposition, Engineering, Geology, Materials science, Physics, Polymer chemistry, Quantum mechanics, World Wide Web, Payment, Ecology, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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