Mechanical Parameter Inversion in Sandstone Diversion Tunnel and Stability Analysis during Operation Period

A large number of experimental studies show that the mechanical parameters of deep buried surrounding rock show significant attenuation characteristics with the increase of strain from the rheological acceleration stage to the attenuation stage. However, the existing numerical models all take mechanical parameters as constants when describing the rheological behavior of surrounding rocks, which can only be applied to the stability analysis of the shallowly buried tunnel. Therefore, this work proceeding from the actual project, improved the sandstone rheological constitutive model and optimized the algorithm of parameter inversion, and put forward a long-term stability analysis model that can accurately reflect the rheological characteristics of surrounding rocks under the complex geological condition including high stress induced by great depth and high seepage pressure. In the process, a three-dimensional nonlinear rheological damage model was established based on Burgers rheological model by introducing damage factors into the derivation of the sandstone rheological constitutive model to accurately describe the rheological behaviors of the deep buried tunnel. And BP (Back Propagation) neural network optimized by the multi-descendant genetic algorithm is used to invert the mechanical parameters in the model, which improves the efficiency and precision of parameter inversion. Finally, the rheological equation was written by using parametric programming language and incorporated into the general finite element software ANSYS to simulate the rheological behavior of the tunnel rock mass at runtime. The results of the model analysis are in good agreement with the monitoring data in the later stage. The research results can provide a reference for the stability analysis of similar projects.A large number of experimental studies show that the mechanical parameters of deep buried surrounding rock show significant attenuation characteristics with the increase of strain from the rheological acceleration stage to the attenuation stage. However, the existing numerical models all take mechanical parameters as constants when describing the rheological behavior of surrounding rocks, which can only be applied to the stability analysis of the shallowly buried tunnel. Therefore, this work proceeding from the actual project, improved the sandstone rheological constitutive model and optimized the algorithm of parameter inversion, and put forward a long-term stability analysis model that can accurately reflect the rheological characteristics of surrounding rocks under the complex geological condition including high stress induced by great depth and high seepage pressure. In the process, a three-dimensional nonlinear rheological damage model was established based on Burgers rheological model by introducing damage factors into the derivation of the sandstone rheological constitutive model to accurately describe the rheological behaviors of the deep buried tunnel. And BP (Back Propagation) neural network optimized by the multi-descendant genetic algorithm is used to invert the mechanical parameters in the model, which improves the efficiency and precision of parameter inversion. Finally, the rheological equation was written by using parametric programming language and incorporated into the general finite element software ANSYS to simulate the rheological behavior of the tunnel rock mass at runtime. The results of the model analysis are in good agreement with the monitoring data in the later stage. The research results can provide a reference for the stability analysis of similar projects.