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Intelligent geological condition recognition in shield tunneling via time-series clustering and online learning
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Jia-jie ZHEN1, 2, Feng-wen LAI1, Ming HUANG1, Qing-xiang LIAO3, Shuang LI1, Yue-qiang DUAN4
Rock and Soil Mechanics | 2025, 46(11) : 3615 - 3625
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Rock and Soil Mechanics | 2025, 46(11): 3615-3625
• Geotechnical Engineering •
Intelligent geological condition recognition in shield tunneling via time-series clustering and online learning
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Jia-jie ZHEN1, 2, Feng-wen LAI1, Ming HUANG1, Qing-xiang LIAO3, Shuang LI1, Yue-qiang DUAN4
Affiliations
  • 1.College of Civil Engineering, Fuzhou University, Fuzhou, Fujian 350108, China
  • 2.College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350108, China
  • 3.Fujian Fuzhou Construction Development Group Co. Ltd., Fuzhou, Fujian 350009, China
  • 4.Xiamen Engineering Co. Ltd. of CCCC First Highway Engineering Co. Ltd., Xiamen, Fujian 361021, China
Published: 2025-11-14 doi: 10.16285/j.rsm.2024.1483
Outline
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Current machine learning models for recognizing geological conditions during shield tunneling heavily rely on precise geological data labelling, limiting their applicability in complex geological environments. To address this, we propose a continuous dynamic time warping (CDTW)-based agglomerative hierarchical clustering model (CDTW-Agglomerative), which integrates a linear interpolation framework to overcome DTW's discretization issues. An online learning mechanism is implemented for dynamic strata recognition. The model's accuracy and reliability are validated using Xiamen Metro Line 3 data, with generalization tested on Line 6 data. Results show recognition accuracies of 85% and 73% on the two datasets, demonstrating robust generalization. CDTW-Agglomerative outperforms DTW-Agglomerative, SoftDTW-Agglomerative, and CDTW-based models (K-means, K-medoids, Spectral clustering). Notably, it identifies cutterhead stratigraphy without requiring pre-labelled geological data, supporting intelligent decision-making for tunnelling parameters.

shield tunnel  /  machine learning  /  geological condition prediction  /  time series clustering  /  online learning
Jia-jie ZHEN, Feng-wen LAI, Ming HUANG, Qing-xiang LIAO, Shuang LI, Yue-qiang DUAN. Intelligent geological condition recognition in shield tunneling via time-series clustering and online learning[J]. Rock and Soil Mechanics, 2025 , 46 (11) : 3615 -3625 . DOI: 10.16285/j.rsm.2024.1483
Funding
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  • National Natural Science Foundation of China(52378392; 52408356)
  • “Foal Eagle Program” Youth Top-notch Talent Project of Fujian Province, China(00387088)
Appendix
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Year 2025 volume 46 Issue 11
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Article Info
doi: 10.16285/j.rsm.2024.1483
  • Receive Date：2024-12-01
  • Online Date：2026-03-27
  • Published：2025-11-14
Article Data
Affiliations
History
  • Received：2024-12-01
  • Accepted：2025-03-23
Funding
National Natural Science Foundation of China(52378392; 52408356)
“Foal Eagle Program” Youth Top-notch Talent Project of Fujian Province, China(00387088)
Affiliations
    1.College of Civil Engineering, Fuzhou University, Fuzhou, Fujian 350108, China
    2.College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350108, China
    3.Fujian Fuzhou Construction Development Group Co. Ltd., Fuzhou, Fujian 350009, China
    4.Xiamen Engineering Co. Ltd. of CCCC First Highway Engineering Co. Ltd., Xiamen, Fujian 361021, China
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