Research on improved MVO-GRNN neural network rockburst prediction model
The prevention and control of rockburst disasters are of great significance to the construction of deep underground engineering,and the accurate prediction of rockburst intensity level can effectively guide the prevention and control of rockburst disasters.Based on the comprehensive consideration of the occurrence mechanism and the judgment basis of rockburst,the evaluation index system of rockburst intensity grade was constructed with three main factors:the stress coefficient of rock(σθ/σc),the brittleness coefficient of rock(σc/σt)and the index of rock elastic energy(Wet).These factors affect the occurrence and intensity class of rock bursts.A rockburst prediction model based on an improved Multi-Verse Optimizer algorithm(IMVO)optimized Generalized Regression Neural Network(GRNN)is proposed.Firstly,122 groups of existing rockburst cases were collected from domestic and overseas literature as the sample data of the model;Then,the adaptive balancing mechanism was used to adjust the wormhole existence probability(VWEP)and travel distance rate(VTDR)in the Multi-Verse Optimizer(MVO)to improve the algorithm and it also improves the global optimization ability of traditional Multi-Verse Optimizer.To make the network layer of the GRNN good information transmission and better mining data information,the improved Multi-Verse Optimizer was used to optimize the smoothness of activation function between neurons in the generalized regression neural network.Besides,the sample data were divided into two data sets for training the prediction model and cross-validation,the optimal smooth factor σ of the prediction model was selected,and the prediction model of rockburst intensity grade based on IMVO-GRNN neural network was obtained.Finally,the feasibility and generalization performance of the model is verified by two practical cases with different engineering backgrounds.The results of the research show that the model has greater optimization ability and higher prediction accuracy than the traditional model,and it can effectually predict the intensity grade of rockburst,providing a new way for rockburst prediction.
NETL
NSTL
万方数据
维普
