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《Blasting》 2009-03
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Projection Pursuit Regression Method of Rock Blastability Classification Based on Artificial Fish-Swarm Algorithm

FANG Chong1,2,ZHANG Xin-gui2,DAI Zhi-hong3(1.College of Civil and Aitectural Engineering,Guangxi University,Nanning 530004,China;2.Guangxi Hydraulic and Electric Polytechnic,Nanning 530023,China;3.Guangxi ElectricPower Industry Investigation Design and Research Institute,Nanning 530023,China)  
From analyzing on the influencing factor of rock blastability,the rock hardness coefficient,acoustic impedance,explosive specific charge and average fissure length were chosen as the evaluation index to establish the comprehensive evaluation model of projection pursuit.The project direction was optimized by the Artificial Fish-swarm Algorithm,and applied to evaluate the rock blastability in a mine.The results show that this model can avoid the influence of subjective to weight coefficient and the result is accurate and objective.It put forward a new evaluation method to rock blastability.
【Fund】: 中国-新加坡国际合作项目《地下岩洞爆炸损伤的模型试验研究》;; 广西自然科学基金(桂科自0728037)
【CateGory Index】: TD235.1
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