Prediction of rockburst hazard based on particle swarm algorithm and neural network

Rockburst is a typical dynamic phenomenon of mine destruction. With the expansion of mining scale and mining depth, its harm is becoming more and more serious. This has become an important problem to be solved urgently in the mining industry. This paper aims to study the prediction and prediction of rockburst risk based on particle swarm algorithm and neural network. This paper proposes a shock risk assessment method based on BP neural network. It uses existing shock pressure data to establish a regression model through BP network, and uses PSO algorithm to optimize connection weights to evaluate the slow convergence of BP network and easy to fall into local optimality. The shortcomings of and the degree of convergence was evaluated. In addition, this paper proposes a rockburst risk prediction method. In order to improve the accuracy of rockburst prediction, regional prevention and control measures and certain risk mitigation methods can also be quickly adopted. It not only identifies the mechanical properties of coal and rock mass in the laboratory, but also judges the possibility of rockbursts during mining. The experimental results in this paper show that 10 main factors that affect rockbursts are selected, 20 standard mechanical data sets are used, and a PSO-BP-based mine explosion risk assessment model is established, and the model is compared with the standard BP model Make a comparison. The results show that compared with the standard BP model, the evaluation accuracy of the PSO-BP model is increased by 15%. Finally, an example of mine risk assessment verifies the accuracy and overall applicability of the method.