Abstract：Rockburst is a common engineering geological disaster in deep rock excavations. To evaluate the possibility of rockburst，a rockburst prediction method using the particle swarm optimization(PSO) algorithm and the general regression neural network(GRNN) model is proposed. This approach employs the technology of neural network to build up a regression model based on existing rockburst database，and takes advantage of PSO algorithm to optimize the parameters of the network which is believed to reduce the adverse influence of man-induced factors in model construction. Then，four major influence factors，including the maximum induced tangential stress on the boundaries of tunnels or caverns，the uniaxial compressive strength and the uniaxial tensile strength of the rock，and also the elastic energy index of the rock，are selected as the inputs for establishing the PSO-GRNN model based on the energy theory and the data obtained from 26 practical cases. The generated PSO-GRNN model is finally applied to predict the rockburst for the Cangling tunnel and Dongguashan copper mine，in which the feasibility and applicability of the proposed approach are illustrated. The methodology presented in the paper provides a reference for some similar engineering involving rockburst.
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2013, Vol. 32 
