This paper focuses on some key problems for the stability analysis of larger cavern group under high geostress conditions. A new index of rock-burst risk assessment, Local Energy Release Rate (LERR), is proposed and integrated with plastic failed volume, displacement of various key points at cavern and support cost index to perform a comprehensive evaluation of stability of larger cavern group under high geo-stress conditions. An intelligent recognition algorithm based on Particle Swarm Optimization is suggested to recognize parameters of hard rockmass constitute model. An intelligent optimal algorithm integrated with Particle Swarm Optimization (PSO), numerical calculation Support Vector Machine (SVM), the suggested new comprehensive index, and recognized model parameters is also proposed to obtain global optimal solution for excavation procedure and support scheme. The proposed new algorithms have been used to recognize model and parameters of surrounding rockmass and to perform stability analysis and excavation procedure optimum for large caverns of Laxiwa Hydropower Station located onYellow River in China. The results can give a guide to the project design.
There are a lot of large carven group and underground excavations which are or will be excavated under high geostress condition. Even though a lot of studies have been conducted on stability analysis of large carven group and underground excavations under high geostress condition (Zhu & Wang, 1992; Feng et al., 1997; Feng & Wang, 2000; Gao & Zheng, 2002; An & Feng, 2003a,b; Jiang & Feng, 2003; Feng & An, 2004; Feng & Hudson, 2004), there are some key problems still not to be solved well.
Considering the limitation of conventional index for stability analysis and optimal design of underground rockmass engineering under high geostress condition and the drawbacks of conventional ERR rockburst indexwhich based on linear elastic theory without brittle failure phenomenon, a newrockburst index, Local Energy Release Rate (LERR), is proposed in this paper.
In light of the fact that it is hard to determine parameters of a constitutive model-cohesion weakening and frictional strengthening (CWFS) model (Kennedy & Eberhart, 1995), which performs excellently in modeling the extent and depth 931
Aiming at distinctness of deformation and failure of rockmass under high geostress and optimization of excavation schemes and support schemes for large caverns is a complicated problem having a large search space and large scale of numerical calculation, a new intelligent optimization integrated method is proposed for optimization of excavation sequence and support parameters for large underground caverns under high geostress condition.
