Three-dimensional inversion analysis of an in situ stress field based on a two-stage optimization algorithm  

大型地下洞室群区域三维地应力场二次反演分析（英文）

作　　者：She-rong ZHANG An-kui HU Chao WANG 

机构地区：[1]State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China [2]School of Civil Engineering, Tianjin Univerxity, Tianjin 300072, China

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2016年第10期782-802,共21页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(No.51321065);the National Natural Science Foundation of China(No.51509182);the Tianjin Youth Research Program of Application Foundation and Advanced Technology(No.15JCQNJC08000),China

摘　　要：Establishing an accurate in situ stress field is important for analyzing the rock-mass stability of the undergroundcavern at the Huangdeng hydropower station in China. Because of the complexity and importance of the in situ stress field, ex-isting back analysis methods do not provide the necessary accuracy or sufficiently recognize nonlinear relations between thedistribution of the in situ stress field and its formative factors. Those factors are related to the geological structures of high com-pressive tectonic stress regimes, including geological faults and tuff interlayers. The new two-stage optimization algorithm pro-posed in this paper is a combination of stepwise regression （SR）, difference evolution （DE）, support vector machine （SVM）, andnumerical analysis techniques. Stepwise regression is used to find the set of unknown parameters that best match the modelingprediction and determine the range of parameters to be recognized. Difference evolution is used to determine the optimum pa-rameters of the SVM. The SVM is used to create the DE-SVM nonlinear reflection model to obtain the optimal values of theparameters from measured stress data. We compare the new two-stage optimization algorithm to other two popular methods, amultiple linear regression （MLR） analysis method and an artificial neural network （ANN） method, to estimate the in situ stressfield for the actual underground cavern at the Huangdeng hydropower station. The two-stage optimization algorithm produces amore realistic estimate of the stress distribution within the investigated area. Thus, this technique may have practical applica-tions in realistic scenarios requiring efficient and accurate estimations of the in situ stress in a rock-mass.Establishing an accurate in situ stress field is important for analyzing the rock-mass stability of the underground cavern at the Huangdeng hydropower station in China. Because of the complexity and importance of the in situ stress field, existing back analysis methods do not provide the necessary accuracy or sufficiently recognize nonlinear relations between the distribution of the in situ stress field and its formative factors. Those factors are related to the geological structures of high compressive tectonic stress regimes, including geological faults and tuff interlayers. The new two-stage optimization algorithm proposed in this paper is a combination of stepwise regression(SR), difference evolution(DE), support vector machine(SVM), and numerical analysis techniques. Stepwise regression is used to find the set of unknown parameters that best match the modeling prediction and determine the range of parameters to be recognized. Difference evolution is used to determine the optimum parameters of the SVM. The SVM is used to create the DE-SVM nonlinear reflection model to obtain the optimal values of the parameters from measured stress data. We compare the new two-stage optimization algorithm to other two popular methods, a multiple linear regression(MLR) analysis method and an artificial neural network(ANN) method, to estimate the in situ stress field for the actual underground cavern at the Huangdeng hydropower station. The two-stage optimization algorithm produces a more realistic estimate of the stress distribution within the investigated area. Thus, this technique may have practical applications in realistic scenarios requiring efficient and accurate estimations of the in situ stress in a rock-mass.

关 键 词：In SITU stress  STEPWISE regression (SR)  Difference evolution (DE)  Support vector machine (SVM)  Finite element Huangdeng underground CAVERN 

分 类 号：TU931[建筑科学—结构工程]