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An Intelligent Inversion Study of In-situ Stress Field in Deep Buried Super-long Tunnels under Complex Geological Conditions
(1. College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059; 2. State Key Laboratory of Geological Disaster Prevention and Geological Environment Protection, Chengdu University of Technology,Chengdu 610059; 3. Guizhou Transportation Planning Survey & Design Academe Co., Ltd., Guiyang 550081)
Abstract To improve the inversion accuracy and efficiency of in-situ stress in deep-buried super-long tunnels un? der complex geological conditions, an intelligent inversion method is developed based on tectonic analysis and RBF(radial basis function) neural network for deep-buried super-long tunnels under complex geological conditions. The main steps of this method are as follows: firstly, the regional geological conditions and tectonic analysis will be used to establish a 3D geological model of the tunnel engineering area, and the Heim hypothesis and Kinnick hypothesis will be applied to jointly determine the range of conditions for the tectonic stress boundary as the training samples for intelligent inversion. Then, the RBF neural network method will be used to obtain the optimal stress boundary conditions, which will be used for an inversion calculation of the in-situ stress. A deep-buried super-long tunnel in southwest China is chosen as the pilot project, in which the established intelligent inversion method for in-situ stress fields is applied in an actual scenario. In addition, the in-situ stress values obtained from the inversion calculation are fitted and compared with the actually measured values, which shows that the overall fitting error of the maximum principal stress inversion is about 10% and the fitting accuracy is close to 90%, indicating that the method is feasible and the inversion calculation results are reasonable.
Abstract：
To improve the inversion accuracy and efficiency of in-situ stress in deep-buried super-long tunnels un? der complex geological conditions, an intelligent inversion method is developed based on tectonic analysis and RBF(radial basis function) neural network for deep-buried super-long tunnels under complex geological conditions. The main steps of this method are as follows: firstly, the regional geological conditions and tectonic analysis will be used to establish a 3D geological model of the tunnel engineering area, and the Heim hypothesis and Kinnick hypothesis will be applied to jointly determine the range of conditions for the tectonic stress boundary as the training samples for intelligent inversion. Then, the RBF neural network method will be used to obtain the optimal stress boundary conditions, which will be used for an inversion calculation of the in-situ stress. A deep-buried super-long tunnel in southwest China is chosen as the pilot project, in which the established intelligent inversion method for in-situ stress fields is applied in an actual scenario. In addition, the in-situ stress values obtained from the inversion calculation are fitted and compared with the actually measured values, which shows that the overall fitting error of the maximum principal stress inversion is about 10% and the fitting accuracy is close to 90%, indicating that the method is feasible and the inversion calculation results are reasonable.
REN Yang1,
2 LI Tianbin1,
2 ZHANG Jiaxin3 WANG Gangwei1 etc
.An Intelligent Inversion Study of In-situ Stress Field in Deep Buried Super-long Tunnels under Complex Geological Conditions[J] MODERN TUNNELLING TECHNOLOGY, 2023,V60(2): 47-53
2023, Vol. 60 