Classification Prediction of Rockburst in Railway Tunnel Based on Hybrid PSO-BP Neural Network

Rockburst is one of the main disasters in railway tunnel construction. In order to accurately predict the rockburst intensity level of the railway tunnel, the rock stress coefficient sigma(theta)/sigma(c), rock brittleness coefficient sigma c/sigma t, and elastic energy index Wet are used as evaluation indexes of rockburst intensity, and a BP neural network rockburst prediction model based on hybrid particle swarm optimization algorithm is proposed. First, 90 groups of existing rockburst examples are selected as the basic data of the mode based on the research results at home and abroad. Then, the BP neural network is improved by using particle swarm optimization (PSO) combined with the simulated annealing algorithm. The results are obtained from the training data. Based on hybrid PSO-BP neural network, the prediction model of rockburst intensity is obtained. Finally, the model is applied to the actual railway tunnel project to verify. The results show that the model takes into account individual optimization and global optimization and can correctly and effectively predict the rockburst grade of the railway tunnel, which provides a new method for rockburst prediction of the railway tunnel.

Automated summary

This paper proposes a BP neural network rockburst prediction model based on hybrid particle swarm optimization algorithm, which can accurately predict the rockburst intensity level of railway tunnels by considering multiple evaluation indexes.