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Study on the Prediction of Underground Cavern Rock Deformation Based on GRU Neural Network
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Chen Wan1, 2, Xingxia Wang3, Hang Duan4, Long Zheng5, Jianwen Huang3
Chinese Journal of Underground Space and Engineering | 2026, 22(2) : 448 - 458
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Chinese Journal of Underground Space and Engineering | 2026, 22(2): 448-458
Study on the Prediction of Underground Cavern Rock Deformation Based on GRU Neural Network
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Chen Wan1, 2, Xingxia Wang3, Hang Duan4, Long Zheng5, Jianwen Huang3
Affiliations
  • 1.Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, Yichang, Hubei 443002, P. R. China
  • 2.College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei 443002, P. R. China
  • 3.Hubei Key Laboratory of Construction and Management in Hydropower Engineering, Yichang, Hubei 443002, P. R. China
  • 4.China Three Gorges Construction Engineering Corporation, Chengdu 610095, P. R. China
  • 5.China Gezhouba Group Three Gorges Construction Engineering Co., Ltd., Yichang, Hubei 443000, P. R. China
Published: 2026-04-20 doi: 10.20174/j.JUSE.2026.02.07
Outline
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In order to enhance the prediction accuracy of surrounding rock deformation, enable real-time monitoring of deformation status, prevent deformation failure, and ensure construction safety, a novel underground cavern surrounding rock deformation temporal prediction method based on GRU neural network is proposed to tackle the low training efficiency, slow convergence, and poor generalization of traditional methods, along with the establishment of a corresponding prediction framework. Utilizing monitoring data of surrounding rock deformation from the underground powerhouse on the right bank of the Baihetan Dam, predictions are made and subsequently compared and analyzed with the forecasting results generated by the Long Short-Term Memory (LSTM) neural network algorithm. The results indicate that the GRU neural network model effectively addresses the prediction challenges associated with underground cavern surrounding rock deformation, offering advantages such as simplified structure, relatively fewer parameters, rapid training and convergence rates, and high prediction accuracy. Compared to the predictions derived from the LSTM neural network algorithm, the GRU model demonstrates a reduction in training duration by over 70%, with a corresponding decrease in prediction error of more than 50%. The relative error for cumulative maximum deformation is less than 0.3%, the probability of absolute error less than 0.9 mm is as high as 95%, and the maximum absolute error is only 2.05 mm.

underground cavern  /  surrounding rock  /  deformation prediction  /  GRU neural network
Chen Wan, Xingxia Wang, Hang Duan, Long Zheng, Jianwen Huang. Study on the Prediction of Underground Cavern Rock Deformation Based on GRU Neural Network[J]. Chinese Journal of Underground Space and Engineering, 2026 , 22 (2) : 448 -458 . DOI: 10.20174/j.JUSE.2026.02.07
Year 2026 volume 22 Issue 2
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doi: 10.20174/j.JUSE.2026.02.07
  • Receive Date:2025-01-18
  • Online Date:2026-06-17
  • Published:2026-04-20
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  • Received:2025-01-18
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Affiliations
    1.Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, Yichang, Hubei 443002, P. R. China
    2.College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei 443002, P. R. China
    3.Hubei Key Laboratory of Construction and Management in Hydropower Engineering, Yichang, Hubei 443002, P. R. China
    4.China Three Gorges Construction Engineering Corporation, Chengdu 610095, P. R. China
    5.China Gezhouba Group Three Gorges Construction Engineering Co., Ltd., Yichang, Hubei 443000, P. R. 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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