Stability Evaluation Model of Slope Dangerous Rock Mass Based on GA-PSO Hybrid Optimization SVR
The stability evaluation of dangerous rock mass of slope is one of the important contents of geological hazard prevention.The traditional stability evaluation methods have low accuracy and slow convergence rate when solving complex non-linear problems.Therefore,a stability evaluation model of slope dangerous rock mass based on GA-PSO hybrid optimization support vector regression(SVR)is proposed.Firstly,a training sample set of slope dangerous rock mass is established by col-lecting a lot of measured data and monitoring data.Then,the SVR algorithm is introduced into the stability evaluation,and its nonlinear mapping performance is used to fit the stability function of the dangerous rock mass of the slope.In order to improve the optimization ability of SVR model,GA-PSO hybrid optimization algorithm is formed by combining genetic algorithm(GA)and particle swarm optimization algorithm(PSO),and is used to solve the optimization problems in SVR model.A number of practical engineering cases of dangerous rock slope are selected to test the algorithm.The results show that compared with the traditional method,the GA-PSO hybrid optimization SVR model can accurately predict the stability of dangerous rock mass of slope,and has higher accuracy and faster convergence speed.
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维普
