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

Abstract：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.