Abstract:Particle swarm optimization (PSO) algorithm is a stochastic global optimization technique and has become the hotspot of evolutionary computation because of its excellent performance and simplicity for implementation. In light of the fact that it is hard to determine the parameters of a constitutive model—cohesion weakening and frictional strengthening (CWFS) model,which performs excellently in modeling the extent and depth of brittle failure zone for hard rock under high in-situ stress condition,a new method is presented to identify parameters of CWFS model using PSO. At first,the stochastic values of parameters are initialized and the difference in failure zone between the value computed and the datum measured is regarded as fitness value to evaluate quality of the parameters. Then the parameters are updated continually using PSO until the optimal parameters are found. Thus parameters are identified adaptively during computation. The results of applications to two real tunnels,i.e.,Mine-by tunnel in Canada and Taipingyi tunnel in China,show that the method is feasible and efficient for identifying constitutive parameters and predicting the extent and depth of brittle failure of hard rock under high in-situ stress condition with high precision.