A GA-SVM based model for throwing rate prediction in the open-pit cast blasting
文献类型：EI期刊论文
| 作者 | Lu Feng |
| 发表日期 | 2012 |
| 关键词 | Explosives Artificial intelligence Blasting Gallium Lithology Open pit mining |
| 英文摘要 | This paper probed into the whole height bench cast blasting process and described the influence factors from 3 major perspectives: natural geological, blasting scheming and factitious ones, and selected the throwing rate which was generally accepted in the cast blasting field to assess the blasting performance. Then a novel GA-SVM model was constructed to analyze the real collected explosion data from open pit mining, and verified in a certain open-pit. Also the MIV method was employed to analyze the influence factor at each input factor. The study indicate that: (1) the presented GA-SVM model performs more robust and accurate than other artificial intelligence models such as BP, RBF, GRNN and GA-BP, which has a more stable prediction accuracy of 83.75%. Moreover, due to the ubiquitous paradigm of the presented approach, it provides a single, unified approach to evaluating other blasting performance factors such as the longest thrown distance and loose coefficient etc; (2) for this certain open pit which maintains a steady lithological character and design parameters, the bench height, explosive specific charge possess a positive correlation coefficient with the throwing rate, while line of least resistance, the slope angle and the profile width perform the opposite. |
| 出处 | Meitan Xuebao/Journal of the China Coal Society
 |
| 卷 | 37期:12页:1999-2005 |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/31179]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Lu Feng. A GA-SVM based model for throwing rate prediction in the open-pit cast blasting. 2012. |
入库方式： OAI收割
来源：地理科学与资源研究所
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