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Stacked Ensemble Learning Method for TBM Surrounding Rock Classification Prediction of Surrounding Rock in TBM Excavation
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He-chao ZHU1, Chang-rui YAO2, 3, Yong-ping SHAO4, Liang TANG2, 3, Xiang-xun KONG2, 3, Bo-yu LI2, 3, Tian-yu ZHANG2, 3
Science Technology and Engineering | 2025, 25(14) : 6016 - 6022
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Science Technology and Engineering | 2025, 25(14): 6016-6022
Papers·Architectural Science
Stacked Ensemble Learning Method for TBM Surrounding Rock Classification Prediction of Surrounding Rock in TBM Excavation
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He-chao ZHU1, Chang-rui YAO2, 3, Yong-ping SHAO4, Liang TANG2, 3, Xiang-xun KONG2, 3, Bo-yu LI2, 3, Tian-yu ZHANG2, 3
Affiliations
  • 1. Angang Cornerstone Mining Co., Ltd., Anshan 114001, China
  • 2. Key Laboratory of Structures Dynamic Behavior and Control of Ministry of Education, HarbinInstitute of Technology, Harbin 150090, China
  • 3. School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
  • 4. China Railway 17th Bureau Group Second Engineering Co., Ltd., Xi'an 710000, China
Published: 2025-05-18 doi: 10.12404/j.issn.1671-1815.2404244
Outline
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The data-driven approach of machine learning enables the intelligent construction of TBM(tunnel boring machines), which is crucial for optimizing the tunneling process, improving the safety of tunneling and reducing labor costs. In order to solve the problems of excessive noise, redundant parameters and difficult effective feature extraction in TBM operation data, a data-driven machine learning method was used to mine the complex machine-soil interaction contained in the data and realize the classification and prediction of TBM surrounding rock mass. First, for the large amount of operational data generated during TBM tunneling, the KDE (kernel density estimation) method was used to extract features from typical tunneling parameter curves, and the maximum probability of the key operating parameters during stable tunneling stage of TBM is obtained. Then, based on the actual TBM operation data, an integrated learning algorithm for surrounding rock classification stacking was proposed. The algorithm is further optimized through k-fold cross-validation, and the complex relationships in the data are mined by using the two-layer learning framework of base classifier and meta-classifier. Finally, a data set of 5 868 TBM segments was used to verify the effectiveness of the proposed algorithm. The results show that the average F1 of the four-classification problem is 0.705, and the average F1 of the two-classification problem is 0.797, which are better than the four selected base classifiers.

TBM tunnel  /  classification of surrounding rock  /  data-driven  /  stacked integrated learning  /  TBM data processing
He-chao ZHU, Chang-rui YAO, Yong-ping SHAO, Liang TANG, Xiang-xun KONG, Bo-yu LI, Tian-yu ZHANG. Stacked Ensemble Learning Method for TBM Surrounding Rock Classification Prediction of Surrounding Rock in TBM Excavation[J]. Science Technology and Engineering, 2025 , 25 (14) : 6016 -6022 . DOI: 10.12404/j.issn.1671-1815.2404244
Year 2025 volume 25 Issue 14
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Article Info
doi: 10.12404/j.issn.1671-1815.2404244
  • Receive Date:2024-06-06
  • Online Date:2025-07-09
  • Published:2025-05-18
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History
  • Received:2024-06-06
  • Revised:2025-02-21
Funding
Affiliations
    1. Angang Cornerstone Mining Co., Ltd., Anshan 114001, China
    2. Key Laboratory of Structures Dynamic Behavior and Control of Ministry of Education, HarbinInstitute of Technology, Harbin 150090, China
    3. School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
    4. China Railway 17th Bureau Group Second Engineering Co., Ltd., Xi'an 710000, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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