Prediction of river embankment deformation during pipe jacking based on machine learning

Xu Hao; Liao Mingxin; Bian Shihai; Jiang Jianliang; Li Aodian; Xu Binfeng; Luo Weijin

doi:10.20265/j.cnki.issn.1007-2993.2024-0287

Volume 39 Issue 5

Oct. 2025

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Article Navigation > GEOTECHNICAL ENGINEERING TECHNIQUE > 2025 > 39(5): 639-647

Xu Hao, Liao Mingxin, Bian Shihai, Jiang Jianliang, Li Aodian, Xu Binfeng, Luo Weijin. Prediction of river embankment deformation during pipe jacking based on machine learning[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2025, 39(5): 639-647. doi: 10.20265/j.cnki.issn.1007-2993.2024-0287

Citation:

Xu Hao, Liao Mingxin, Bian Shihai, Jiang Jianliang, Li Aodian, Xu Binfeng, Luo Weijin. Prediction of river embankment deformation during pipe jacking based on machine learning[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2025, 39(5): 639-647. doi: 10.20265/j.cnki.issn.1007-2993.2024-0287

Citation:

Xu Hao, Liao Mingxin, Bian Shihai, Jiang Jianliang, Li Aodian, Xu Binfeng, Luo Weijin. Prediction of river embankment deformation during pipe jacking based on machine learning[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2025, 39(5): 639-647. doi: 10.20265/j.cnki.issn.1007-2993.2024-0287

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Prediction of river embankment deformation during pipe jacking based on machine learning

doi: 10.20265/j.cnki.issn.1007-2993.2024-0287

1.
Ningbo River Management Centre, Ningbo 315100, Zhejiang, China
2.
Zhejiang Engineering Survey and Design Institute Group Co. Ltd., Ningbo 315012, Zhejiang, China
3.
Key Laboratory of Safe Construction and Intelligent Maintenance for Urban Shield Tunnels of Zhejiang Province, Hangzhou 310015, Zhejiang, China

Received Date: 2024-06-25
Accepted Date: 2025-03-06
Rev Recd Date: 2024-12-31
Publish Date: 2025-10-10

Abstract

Abstract

Pipe jacking has a significant impact on the stability of river embankments due to its small working surface and high stacking load. The early prediction of river embankment deformation is of great significance for safe construction. The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method was employed to decompose the nonlinear and unstable monitoring data into trend and periodic terms, and an intelligent prediction model (CTG) suitable for river embankment deformation was proposed. The model used a polynomial function to predict the trend item, and a time convolution network-gated recurrent unit hybrid model combined with a particle swarm optimization algorithm (i.e., PSO-TCN-GRU algorithm) was used to predict the periodic term. It was found that the intelligent algorithm proposed is superior to other classical neural network prediction methods, and has a certain promotional value, which can provide reference for the deformation prediction of similar projects. In addition, it is suggested that data decomposition and feature extraction should be carried out before monitoring data prediction, which is conducive to improving the prediction results of the model.
- displacement prediction,
- pipe jacking method,
- CEEMDAN,
- TCN-GRU,
- PSO,
- monitoring and warning

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References(26)

References

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Prediction of river embankment deformation during pipe jacking based on machine learning

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Prediction of river embankment deformation during pipe jacking based on machine learning

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