摘要
针对爆破块度分布评价过程中诸多模糊因素,应用支持向量机理论并结合现场爆破实践,提出支持向量机理论基础上的岩体爆破块度分布预测方法.以抚顺东露天矿现场爆破试验为依据,采用非线性回归优化的方法,建立岩体爆破块度分布的支持向量机分类模型,应用该模型对岩体爆破块度分布进行预测,同时应用Split Desktop3.1计算机图像处理技术分析岩体爆破块度分布,通过比较大块率、尾矿率及平均块度评价预测结果.结果表明,建立的支持向量机分类模型对爆破块度分布预测效果良好,预测结果与实际测量分析结果基本吻合,为岩体爆破块度分布预测提供了新方法.
There are many fuzzy factors in the evaluation process of blast fragmen- tation distribution, so we apply support vector machine theory and combine with field blasting practice, put forward in the prediction method of the blasting fragmenta- tion distribution on the basis theory of support vector machine. Base on the blasting practice of Fushun eastern open pit mine, using the method of nonlinear regression op- timization, establish support vector machine model of rock blasting block distribution, the model was applied on rock blasting block distribution prediction. At the same time, apply Split Desktop3.1 computer image processing technology to analyze rock blasting block distribution, by comparing chunks rate, tailings rate and average block evaluation prediction. The results showed that the establishment of the support vector machine model to predict blasting block distribution has a good effect, predicted and actual measured results are basically consistent. It provides a new method to predict rock blasting block distribution.
出处
《应用泛函分析学报》
2015年第4期415-422,共8页
Acta Analysis Functionalis Applicata
基金
国家自然科学基金资助项目(51104084
51474119)
国家自然科学基金重点资助项目(U1361211)
高等学校博士学科点专项基金项目(20122121110003)
关键词
块度分布
支持向量机
回归优化
块度预测
block distribution
support vector machine
return optimization
blockprediction
