Research on Rock Burst Prediction based on a Support Vector Machine
With the development of mining,water conservancy,transportation and other fields toward deeper areas,rockburst problems occur frequently in engineering,which seriously endanger the safety of personnel,so it is.crucial to establish an accurate and effective rock burst prediction model.A rock burst prediction model based on support vector machine theory is constructed,and the optimal parameters of the model are obtained by combining the sparrow search algorithm.Relying on 157 groups of domestic and foreign measured rock burst cases,the model prediction accuracy is used as the identification framework,and the new model prediction results evaluation index(mean deviation)is integrated to analyze the prediction model performance,and both numerical simulation and engineering application are used to verify the effectiveness of the SSA-SVR model.The results show that the model prediction accuracy increases with the increase of input parameter categories.Genetic algorithm(GA),particle swarm algorithm(PSO)and sparrow search algorithm(SSA)have some optimization effect on support vector machines,so the optimized SVR rock burst prediction model is reliable and effective.
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