Embankment Long-Term Settlement Prediction Based on Sparse Dictionary Learning

ZHOU Zeling; SHEN Jian; DAI Xianrong; DUAN Bing; PAN Xiaodong

doi:10.3724/j.gyjzG24090604

Volume 55 Issue 9

Sep. 2025

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2025 > 55(9): 218-225

ZHOU Zeling, SHEN Jian, DAI Xianrong, DUAN Bing, PAN Xiaodong. Embankment Long-Term Settlement Prediction Based on Sparse Dictionary Learning[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(9): 218-225. doi: 10.3724/j.gyjzG24090604

Citation:

ZHOU Zeling, SHEN Jian, DAI Xianrong, DUAN Bing, PAN Xiaodong. Embankment Long-Term Settlement Prediction Based on Sparse Dictionary Learning[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(9): 218-225. doi: 10.3724/j.gyjzG24090604

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Embankment Long-Term Settlement Prediction Based on Sparse Dictionary Learning

doi: 10.3724/j.gyjzG24090604

1. College of Civil Engineering, Zhejiang University of Technology, Hangzhou 310014, China;
2. Zhejiang Key Laboratory of Green Construction and Intelligent Operation & Maintenance for Coastal Infrastructure(Zhejiang University of;
Technology), Hangzhou 310014, China;
3. Zhejiang Institute of Communications Co., Ltd., Hangzhou 310030, China

Received Date: 2024-09-06
Available Online: 2025-11-05

Abstract

Abstract

An accurate prediction of long-term embankment settlements is crucial for ensuring road safety and maintaining normal operations. Existing studies have employed inverse analysis of soil parameters based on monitoring data， subsequently updating settlement predictions using the inferred parameters. However， this method is hindered by high computational costs， limiting its widespread application in practical engineering. This paper proposed a long-term settlement prediction method for embankments based on sparse dictionary learning. A dictionary was constructed using finite element simulation results， and key atoms within the dictionary were identified and weighted through the analysis of settlement and horizontal displacement monitoring data. The long-term settlement was then predicted as a linear combination of a few significant atoms. The effectiveness of the proposed method was demonstrated using the Ballina trial embankment in Australia. Results indicated that the method successfully identified key atoms and their weights based on the dictionary derived from finite element analysis. By integrating sparse dictionary learning with multi-source monitoring data， this approach facilitated accurate long-term settlement predictions with low computational cost and high prediction accuracy.
- sparse dictionary learning,
- multi-source monitoring data,
- orthogonal matching pursuit,
- settlement prediction,
- finite element model

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References(16)

References

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