Inversion of Mechanical Parameters of Tunnel Surrounding Rock Based on Improved GWO-BP Neural Network
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Geoscience and Mining Technology · Geomechanics and Mining Engineering · Rock Mechanics and Modeling
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Civil Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Hunan Province Key Laboratory of Geotechnical Engineering Stability Control and Health Monitoring, Hunan University of Science and Technology, Xiangtan 411201, China
Funding
Science and Technology Program of Hunan Provincial Department of Transportation
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