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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摘要依托世界多个矿山的爆破块度统计数据，以27个样本作为训练样本，10个样本作为待判样本，通过主成分分析法对原始数据加以处理，获得相对独立的新变量，后将新变量作为指标代入分类判别法中，量化爆破块度分级标准，利用多元回归分析推得不同级别相对应的块度预测模型，并与工程中广泛使用的Kuz-Rom结果进行对比。结果表明：提出的模型在建立时避免了爆破因素间的相互影响；通过对待判样本的对比预测可知，该模型预测精度优于Kuz-Rom模型，并验证了建立不同级别相对应爆破块度预测模型的正确性。

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	武仁杰1
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	李海波1
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	于崇1
	夏祥1

关键词 ：岩石力学, 爆破块度, 样本数据多样化, 主成分分析, 判别分析, 多元回归分析

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

引用本文:

武仁杰1，2，李海波1，2，于崇1，夏祥1. 基于统计分级判别的爆破块度预测模型[J]. 岩石力学与工程学报, 2018, 37(1): 141-147.
WU Renjie1，2，LI Haibo1，2，YU Chong1，XIA Xiang1. Model for blasting fragmentation prediction based on statistical #br# classification. , 2018, 37(1): 141-147.

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

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