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Inversion Analysis on Surrounding Rock Parameters of Tunnel Based on LHS-WOA-ELM
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Shihui XIAO1, 2, Feng WANG3, Baiyan HE1, Guoyuan XU1, Weizhen HUANG1, Weipeng LI2
Mining Research and Development | 2025, 45(3) : 91 - 110
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Mining Research and Development | 2025, 45(3): 91-110
Inversion Analysis on Surrounding Rock Parameters of Tunnel Based on LHS-WOA-ELM
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Shihui XIAO1, 2, Feng WANG3, Baiyan HE1, Guoyuan XU1, Weizhen HUANG1, Weipeng LI2
Affiliations
  • 1.School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641, China
  • 2.Zhuhai Dahengqin Co., Ltd., Zhuhai, Guangdong 519000, China
  • 3.China Railway 18th Bureau Group No. 1 Engineering Co., Ltd., Zhuozhou, Hebei 072750, China
Published: 2025-03-25
Outline
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In order to improve the rationality of mechanical parameters of tunnel surrounding rock, a new inversion model of surrounding rock parameters was proposed based on a tunnel project with a super large-section in Zhuhai. After the initial samples were generated based on Latin hypercube sampling (LHS), the parameter sensitivity analysis was carried out to determine the key parameters of the surrounding rock and improve the sample structure. Then, the whale optimization algorithm (WOA) was used to optimize the number of hidden layer nodes, the initial weights and the thresholds of the extreme learning machine (ELM) to form the LHS-WOA-ELM inversion model. The inversion parameters were substituted into FLAC3D to calculate the displacement and compare with the field measured data. The results show that the parameter sensitivity analysis based on LHS can investigate the co-variation of multi-parameters with fewer samples, and determine the main parameters affecting the displacement of surrounding rock as elastic modulus E, cohesion c and internal friction angle φ. Compared with WOA, ELM and BP algorithm models, the difference between the calculated displacement values obtained by LHS-WOA-ELM inversion model and the measured displacement values is smaller, indicating that the inversion analysis method can well reflect the nonlinear and uncertain characteristics between the surrounding rock parameters and deformation, and further improve the accuracy and efficiency of the surrounding rock inversion in tunnels with super-large section, which can provide a reference for determining the design parameters of underground caverns and mining projects.

Tunnel with super large-section  /  Parameter inversion of surrounding rock  /  Latin hypercube sampling  /  Whale optimization algorithm  /  Extreme learning machine
肖时辉, 王峰, 何柏延, 徐国元, 黄伟真, 李伟鹏. 基于LHS-WOA-ELM的隧道围岩参数反演分析. 矿业研究与开发, 2025 , 45 (3) : 91 -110 .
Shihui XIAO, Feng WANG, Baiyan HE, Guoyuan XU, Weizhen HUANG, Weipeng LI. Inversion Analysis on Surrounding Rock Parameters of Tunnel Based on LHS-WOA-ELM[J]. Mining Research and Development, 2025 , 45 (3) : 91 -110 .
Year 2025 volume 45 Issue 3
59
27
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Article Info
  • Receive Date:2024-04-28
  • Online Date:2026-10-29
  • Published:2025-03-25
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  • Received:2024-04-28
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Affiliations
    1.School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641, China
    2.Zhuhai Dahengqin Co., Ltd., Zhuhai, Guangdong 519000, China
    3.China Railway 18th Bureau Group No. 1 Engineering Co., Ltd., Zhuozhou, Hebei 072750, China
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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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