Stability Analysis And Deformation Prediction For Surrounding Rock Of Underground Powerhouse

Since excavation span of underground powerhouse is large, its geologic construct and state of stress is complex, the stabilization of underground powerhouse has always been the main focus in underground engineering. Because of initial stress field of rock masses, constitutive model and material parameters have randomness, fuzziness and strongly nonlinear, which result that underground engineering may not be accurately analyzed and simulated by traditional calculation method. However, along with the development of crossing multi-disciplinary, using diversiform methods are effective weapons to study the characteristic of underground engineering. The paper adopts the analytic solution, numerical methods and intelligent algorithms to analyze and evaluate the stabilization of surrounding rock of underground powerhouse and provides advices for the design and construction. The main contents are as follows:(1) At present, BP neural network has been widely used in back analysis of material parameters and initial stress field of rock masses in mechanics. However, BP neural network is prone to be over- trained, slow in convergence, not global minimum but local ones obtained and number of neurons in hidden layer hard to be determined. Due to above problems, the paper adopts RBF neural network and advanced BP neural network to identify mechanical parameters and initial geo-stresses according to actual observed normal stresses of some specific points. Direct computations based on fast Lagrangian analysis of Continuum (FLAC) are performed to get enough training samples for RBF neural network and BP neural network. The example of Hohhot pumped-storage power station shows that the combination of RBF neural network with FLAC is more effective and rapid than the application of BP neural network.(2) According to the requirement of terrain tract, geological conditions and project's time limit of underground powerhouse, how to choose a better construction scheme and step has not always been determined by specification for the design of any hydraulic structure. On the base of the project background of Hohhot pumped-storage power station, the paper applies the FLAC^3D program to study the deformations, stresses and plastic states separately for five layers and seven layers of excavation height model. Comparing the results of two different models, the stability evaluation of its surrounding rocks has been advanced and some reasonable advices are gained in excavation and supporting.(3) Based on active timbering principle of bolt-grouting, shearing strength between bolt-grouting and rock can be transformed into the surface pressure of acting on the inner surface in the cavity and anchor-forced rock is regarded as a equivalent material. Adopting rheological constitutive relation model of Poynting-thomson, the rheological expressions of rock with anchoring force is advanced utilizing elasticity and visco-elasticity. Taking a circular tunnel for example, the precision of the simplified calculation results can satisfy the demand of actual project by checking a precedent with FLAC program.(4) Adaptive-Network-based Fuzzy Inference Systems (ANFIS) are new fuzzy inference systems which organically integrate with neural network and fuzzy logic. ANFIS adopt Hybrid learning algorithm of back propopagation algorithm and Least Square estimate method, which adjust premise parameter and conclusion parameter respectively, so as to make full use of excellent characteristic of neural network and fuzzy logic. This paper combines ANFIS and Hydropower Powerhouse surrounding rock deformation monitoring data to establish the forecasting model of Hydropower rock deformation. The future of rock deformation will be predicted by studying on-site observation data. The paper takes Longtan Power Station as engineering background to illustrate the rationality and feasibility of this method.(5) As neural network lacks strict theoretical foundation, the "Accurate Online Support Vector Regression" method is applied to study the evolution law for underground powerhouse rock surrounding's stability. For example, the crown's deformation of a pumped-storage power station, comparing with other prediction methods' monitoring results; it shows that the learning efficiency and prediction accuracy of AOSVR is superior to other prediction methods obviously.In conclusion, the paper shows that surrounding rock's stability and prediction of deformation have been studied and analyzed from different aspects. Finally a summary is given some problems to be further studied are discussed.