基于统计分级判别的爆破块度预测模型

doi:10.13722/j.cnki.jrme.2017.0815

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Abstract A database of blast fragmentation with collections from multiple mines in the world is used. 27 samples in the database were used for the training and 10 samples were used for the classification. The original data were processed through the principal component analysis in order to obtain the independent variables. Then the discriminant analysis was performed to establish the standard of blast fragmentation classification by using the new variables as the indicators. The multiple regression analysis was used to derive the prediction model for corresponding block of different classes. The results show that the proposed model avoided the interaction between the blasting factors and is better than the Kuz-Rom model. The validity of the proposed model was verified.

Key words： rock mechanics blasting fragmentation diversity of sample data principal component analysis discriminant analysis multiple regression analysis

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	Articles by authors
	WU Renjie1
	2
	LI Haibo1
	2
	YU Chong1
	XIA Xiang1

Cite this article:

WU Renjie1,2,LI Haibo1, et al. Model for blasting fragmentation prediction based on statistical #br# classification[J]. , 2018, 37(1): 141-147.

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http://www.rockmech.org/EN/10.13722/j.cnki.jrme.2017.0815 OR http://www.rockmech.org/EN/Y2018/V37/I1/141

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