Rock Fragmentation Size Distribution Prediction and Blasting Parameter Optimization Based on the Muck-Pile Model

Abstract

Rock fragmentation size distribution is often used as an important index to account for the blasting effect because it directly affects the subsequent loading, transportation, and secondary crushing. Due to the mismatching of explosive and rock wave impedance, high boulder yield often occurs which affects the blasting effect. In this study, methods of measuring rock acoustic impedance, rock strength point loading, and detonation wave velocity have been used to obtain more accurate input parameters. Then, in the watershed image segmentation technique, the Gates-Gaudin-Schuhmann and Rosin-Rammler distribution functions have been used to analyze and quantitatively describe the rock fragmentation size distribution in the existing muck-pile. Finally, taking the rock properties, explosive performance, blasting parameters, and system characteristic variable into consideration, support vector machine (SVM) regression model has been analyzed on the learning and prediction of samples. The results show that SVM has a good prediction accuracy, high precision, and strong generalization ability. The optimized matching coefficient of rock and explosive wave impedance K ranges from 2.50 to 2.58 times. This study has developed a series of simple, accurate methods for rock properties analysis, detonation wave velocity measurement, and muck-pile model image processing, and a basis for predicting and evaluating rock fragmentation size distribution and optimizing the matching coefficient before carrying out a blasting operation.