关键词: 隧道工程, 围岩参数反演, 随机梯度下降算法, 神经网络, 正交试验法, 数值模拟

Abstract:

In this study, onsite monitoring data are used to analyze the deformation information of surrounding rocks and conduct back analysis of the mechanical parameters of rock mass. A case study is conducted on the Niuliantang tunnel in the TJ1 bid section of the JianheLiping expressway in Guizhou, China. The influencing factors, such as the elastic modulus, cohesion force, Poisson′s ratio, and internal friction angle of the surrounding rock are selected. Orthogonal tests and finite element simulations are designed to obtain 25 sets of physicomechanical parameters of the surrounding rock and their corresponding simulation results for crown settlement and arch waist convergence. Furthermore, traditional back propagation(BP) neural networks are improved using a stochastic gradient descent(SGD) algorithm. Crown settlement and arch waist convergence are used as input parameters, and the elastic modulus, cohesion force, Poisson′s ratio, and internal friction angle of the surrounding rock are used as output parameters. This approach enables the inversion analysis of the surrounding rock parameters. The feasibility and accuracy of the SGDoptimized BP neural network model are validated by substituting the inversionobtained surrounding rock parameters into the finite element model. The deformation and initial load characteristics of the surrounding rock are analyzed, and construction suggestions are provided. The results reveal that the relative difference between the crown settlement, arch waist, and arch shoulder convergence calculated by the SGDoptimized BP neural network model and the monitoring results range from 2.50% to 24.01%, which is smaller than those calculated by the traditional BP neural network model(11.51%~93.71%). These findings validate the feasibility and superiority of the SGDoptimized BP neural network model. Because of the stress concentration of the spray layer and anchor bolt at the arch foot of the upper and lower benches, the arch foot support must be enhanced to prevent engineering accidents.

Key words: tunnel engineering, inversion of surrounding rock parameters, stochastic gradient descent algorithm, neural networks, orthogonal experiment, numerical simulation