Inversion of Mechanical Parameters of Tunnel Surrounding Rock Based on Improved GWO-BP Neural Network
Accurately determining the mechanical parameters of surrounding rock in tunnel design and construction presents a significant challenge due to the complexity of the environment. This study proposes a novel approach for inverting these parameters using an advanced optimization method, the Improved Gr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/537 |
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| author | Chen Zhang Qiunan Chen Wenbing Zhou Xiaocheng Huang |
| author_facet | Chen Zhang Qiunan Chen Wenbing Zhou Xiaocheng Huang |
| author_sort | Chen Zhang |
| collection | DOAJ |
| description | Accurately determining the mechanical parameters of surrounding rock in tunnel design and construction presents a significant challenge due to the complexity of the environment. This study proposes a novel approach for inverting these parameters using an advanced optimization method, the Improved Grey Wolf Optimization (IGWO), integrated with a BP neural network (IGWO-BP). Key enhancements such as cubic chaotic mapping, refraction backward learning, nonlinear convergence factors, and updated position formulas were applied to improve the algorithm’s search efficiency. By optimizing the neural network’s weights and biases, a precise relationship between rock mechanics and displacement was established. The method was validated through a case study of the Lianhua Tunnel (YK37 + 330 section), utilizing field data of crown settlement and peripheral displacement. The approach accurately predicted mechanical parameters, with relative errors below 5.02% for crown settlement and 4.15% for peripheral displacement. These results demonstrate the reliability and practical applicability of the proposed technique for tunnel engineering. |
| format | Article |
| id | doaj-art-9b2b0125059e4abcb999226be3bdc8d1 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9b2b0125059e4abcb999226be3bdc8d12025-01-24T13:19:44ZengMDPI AGApplied Sciences2076-34172025-01-0115253710.3390/app15020537Inversion of Mechanical Parameters of Tunnel Surrounding Rock Based on Improved GWO-BP Neural NetworkChen Zhang0Qiunan Chen1Wenbing Zhou2Xiaocheng Huang3College of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaCollege of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaCollege of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaCollege of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, ChinaAccurately determining the mechanical parameters of surrounding rock in tunnel design and construction presents a significant challenge due to the complexity of the environment. This study proposes a novel approach for inverting these parameters using an advanced optimization method, the Improved Grey Wolf Optimization (IGWO), integrated with a BP neural network (IGWO-BP). Key enhancements such as cubic chaotic mapping, refraction backward learning, nonlinear convergence factors, and updated position formulas were applied to improve the algorithm’s search efficiency. By optimizing the neural network’s weights and biases, a precise relationship between rock mechanics and displacement was established. The method was validated through a case study of the Lianhua Tunnel (YK37 + 330 section), utilizing field data of crown settlement and peripheral displacement. The approach accurately predicted mechanical parameters, with relative errors below 5.02% for crown settlement and 4.15% for peripheral displacement. These results demonstrate the reliability and practical applicability of the proposed technique for tunnel engineering.https://www.mdpi.com/2076-3417/15/2/537tunnel engineeringIGWO-BP neural networknumerical simulationinversion of surrounding rock mechanical parameters |
| spellingShingle | Chen Zhang Qiunan Chen Wenbing Zhou Xiaocheng Huang Inversion of Mechanical Parameters of Tunnel Surrounding Rock Based on Improved GWO-BP Neural Network Applied Sciences tunnel engineering IGWO-BP neural network numerical simulation inversion of surrounding rock mechanical parameters |
| title | Inversion of Mechanical Parameters of Tunnel Surrounding Rock Based on Improved GWO-BP Neural Network |
| title_full | Inversion of Mechanical Parameters of Tunnel Surrounding Rock Based on Improved GWO-BP Neural Network |
| title_fullStr | Inversion of Mechanical Parameters of Tunnel Surrounding Rock Based on Improved GWO-BP Neural Network |
| title_full_unstemmed | Inversion of Mechanical Parameters of Tunnel Surrounding Rock Based on Improved GWO-BP Neural Network |
| title_short | Inversion of Mechanical Parameters of Tunnel Surrounding Rock Based on Improved GWO-BP Neural Network |
| title_sort | inversion of mechanical parameters of tunnel surrounding rock based on improved gwo bp neural network |
| topic | tunnel engineering IGWO-BP neural network numerical simulation inversion of surrounding rock mechanical parameters |
| url | https://www.mdpi.com/2076-3417/15/2/537 |
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