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Prediction of Thrust and Torque of TBM Based on Information Fusion and Stacked Convolutional Neural Networks
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Yao-hong YANG1, 2, Zhe ZHANG1, Jian-guo CHEN3, Qun-sheng LI3, Yu-xiang LIU1
Water Resources and Power | 2025, 43(9) : 92 - 96
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Water Resources and Power | 2025, 43(9): 92-96
Prediction of Thrust and Torque of TBM Based on Information Fusion and Stacked Convolutional Neural Networks
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Yao-hong YANG1, 2, Zhe ZHANG1, Jian-guo CHEN3, Qun-sheng LI3, Yu-xiang LIU1
Affiliations
  • 1.School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
  • 2.Henan Provincial Key Laboratory of Hydrosphere and Watershed Water Security, Zhengzhou 450046, China
  • 3.Henan Water Conservancy First Engineering Bureau Group Co., Ltd., Zhengzhou 450000, China
Published: 2025-09-25 doi: 10.20040/j.cnki.1000-7709.2025.20241868
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It is the key issues of reasonably and accurately predicting the thrust and torque of tunnel boring machines (TBM) to realize the intelligent control of TBMs. This paper proposes a two-stage prediction method of knowledge-data-driven spatio-temporal stacked convolutional network (KD-NTS-GAT). Firstly, based on expert knowledge and the NTS-NOTEARS method, a new information fusion technique is proposed. The discrete expert experience and the continuous NTS-NOTEARS indicators is mapped and smoothly fused through clustering. The causal relationships among the key operating parameters of the TBM is quantitatively extracted to improve the authenticity of the causal relationships significantly. Then, causality is further combined as a prior knowledge with stacked convolutional network deep learning model for predicting thrust and torque of TBM. Taking the bid Ⅳ of Xinjiang Water Conveyance Tunnel Project as an example, a comparative analysis of the KD-NTS-GAT method and the pure data-driven method shows that the KD-NTS-GAT has better prediction capability on thrust and torque. The conclusions can provide a reference for the intelligent control of TBM construction.

TBM  /  information fusion  /  NTS-NOTEARS  /  stacked convolutional network
Yao-hong YANG, Zhe ZHANG, Jian-guo CHEN, Qun-sheng LI, Yu-xiang LIU. Prediction of Thrust and Torque of TBM Based on Information Fusion and Stacked Convolutional Neural Networks[J]. Water Resources and Power, 2025 , 43 (9) : 92 -96 . DOI: 10.20040/j.cnki.1000-7709.2025.20241868
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Year 2025 volume 43 Issue 9
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Article Info
doi: 10.20040/j.cnki.1000-7709.2025.20241868
  • Receive Date：2024-10-04
  • Online Date：2025-12-15
  • Published：2025-09-25
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History
  • Received：2024-10-04
  • Revised：2024-11-18
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Affiliations
    1.School of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
    2.Henan Provincial Key Laboratory of Hydrosphere and Watershed Water Security, Zhengzhou 450046, China
    3.Henan Water Conservancy First Engineering Bureau Group Co., Ltd., Zhengzhou 450000, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2025.20241868
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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