A GA-SVM based model for throwing rate prediction in the open-pit cast blasting.

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; (T) 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. [ABSTRACT FROM AUTHOR]