Abstract:

In order to predict the rock mass blastability classification more accurately，the evaluation model was established based on Principal Component Analysis and RBF Neural Network.Taking a mine rock mass for example，the four evaluation indexes（the rock mass density，compressive strength，tensile strength，the integrality index of rock mass） that affect the rock mass blastability were considered，and the sample data of 13 actual mine rock mass were counted.The sample data were processed by the method of principal component，the results were used as input factors of RBF network，and the level of rock mass blastability was used as output factor，the precision of rock mass blastability prediction can be more higher.The research results show that relative errors of predicting outcomes are all controlled within 5%，and compared with the prediction errors by BP neural network，the expected value relative errors of the four rock mass are reduced 71.94%，86.65%，73.20%，76.62%，respectively，the classification prediction accuracy are obviously improved.The model provides a better evaluation for the rock mass blastability classification analysis.

Key words: RBF neural network, rock mass blastability, evaluation index, Principal Component Analysis, prediction precision