Shi Y.,Shandong University of Science and Technology |
Sun T.,Shandong University of Science and Technology |
Hao J.,Shandong University of Science and Technology |
Hao S.,Hangzhou Focused Photonics Inc
Applied Mechanics and Materials | Year: 2012
This paper selected the factors as the coal mining depth, coal seam thickness, the dips, partings, main roof of the top coal caving as indicators, used the categorical data of the steep seam from domestic mining area as training samples for training. Based on one to one classification of SVM, the top coal caving ability prediction model was established. The example results show that the model prediction method is feasible, the prediction results have very high accuracy and reliability, and has certain promotional value. © (2012) Trans Tech Publications, Switzerland.