Prediction and Analysis Method of Mine Blasting Quality Based on GA-BP Neural Network

Abstract

Selecting reasonable blasting parameters of ore and rock is an important measure to achieve good blasting effect. In the mining process, rock fragmentation is an important index to evaluate the blasting effect, which directly affects the technical scheme, equipment selection, economic effect, and other issues of the mine and even seriously threatens the sustainable safety production of the mine. With the rapid development of information technology, the development of computer intelligent image recognition technology is becoming more and more perfect, and its role is becoming more and more important. Based on the neural network method, this paper studies the computer intelligent image recognition technology. In this paper, the GA-BP network image recognition model is established by combining genetic algorithm with BP algorithm and analyzing the principles of intelligent image recognition, image pattern recognition, and BP neural network learning algorithm. On the basis of experimental analysis, the average accuracy of prediction can reach 67.4%. For the efficiency analysis of computer mathematical analysis, it will generally reach 64.3%. In this paper, taking the lump rate and blasting cost as the optimization objective function, the comparison and selection of multiple schemes of production blasting design are carried out, which provides quantitative decision-making basis for the rational selection of production blasting design parameters.

Mining Technology Evaluation for Steep Coal Seams Based on a GA-BP Neural Network

“…Ozyurt et al 23 developed six different ANN models and investigated the applicability of ANNs and game theory in the development of an underground mining method selection model. Yu and Ren 24 devised a GA-BP network image recognition model to contrast and choose multiple approaches for production blasting design, providing a quantitative basis for the rational selection of production blasting design parameters. Xu and Zhao 25 established a landslide stability analysis and prediction technique based on a GA-BP model, concluding that the GA-BP model algorithm was more accurate and had faster convergence than the BP model.…”