Study Of Feedback Optimizing And Analyzing The Schemes Of Excavation And Supporting Of Large Cavern Group Using Integrated Intelligent Method

The scale of large cavern group is beyond current regulation. The structure of cavern group and the geologic environment where is located in are complicated, therefore, the factors affecting the surrounding rock stability are complicated too. How to dynamically and rapidly redesign the support system based on monitoring data? That is a very important problem need to be solved. In the dissertation, aiming at the underground powerhouse engineer of Qingjiang Shuibuya hinge, the method of integrated dynamic feedback analysis in construction for large cavern group is proposed, which has been used in the feedback analysis and optimization of support schemes of the Shuibuya underground powerhouse. Concretely, such works carried through as below.(1) Aiming at the disadvantages of experience formula, the case-based SVM method for maximal deformation forecasting of surrounding rocks of tunnels is proposed.(2) Because of the local optimization and time wasting of conventional feedback optimizing method, a new 3D parallel evolution SVM-numerical simulation optimizing method is proposed to optimize large cavern group anchor parameters. Based on MPI, its computing platform is developed. Using this method, the global optimal solution could be gotten rapidly, and the computing velocity improves 10 times more than the ANN-finite element method.(3) The optimization of large underground cavern anchor parameters has some characters such as : the object of stability is incompatible with the object of economics,the analysis of large underground cavern's stability is very complicated, appraising guide lines are required to denote the anchor effect roundly, a lot of schemes of different anchor parameters will occur and a great deal of calculate work is needed. Aiming at the characters, the anchors optimizing model is constructed to optimize large cavern group anchor parameters, the restriction condition, optimization indexes and optimizing steps are decided. The main component analysis method is used to deal with multiple indexes appraisement, not only the system information can be kept but also the system can be predigested, avoiding the artificially deciding the weights of indexes.