Prediction of blast fragmentation using multivariate analysis procedures

Abstract

SUMMARYAn extensive multivariate analysis procedure for prediction of blast fragmentation distribution is presented. Several blasts performed in various mines and rock formations in the world are brought together and evaluated. Blast design parameters, the modulus of elasticity, in situ block size are considered to perform multivariate analysis. The hierarchical cluster analysis is used to separate the blasts data into different groups of similarity. Group memberships were checked by the discriminant analysis. The multivariate regression analysis was applied to develop prediction equations for the estimation of the mean particle size of muckpiles. Two different prediction equations were developed based on the rock stiffness. Validation of the proposed equations on various mines is presented and the capability of the prediction equations was compared with one of the most applied fragmentation distribution models appearing in the blasting literature. Prediction capability of the proposed models was found to be strong. Diversity of the blasts data used is one of the most important aspects of the developed models. The models are not complex and suitable for practical use at mines.

Evaluation of mine waste characterization to identify opportunities for optimizing project economics using fragmentation analysis

“…These factors have been described as controllable and uncontrollable dynamics, and are effectively categorized into three: rock mass properties, blast design parameters and explosive properties. These dynamics were further elaborated by Hudaverdi et al (2011); Kulatilake et al (2010Kulatilake et al ( , 2012 respectively that, blast design parameters (burden, bench height, spacing between boreholes, drill hole etc.) and explosive properties constitute the controllable dynamics.…”

Advanced Analysis of Blast Pile Fragmentation in Open-Pit Mining Utilizing 3D Point Cloud Technology

“…These parameters include, but are not limited to, the volume, as well as geometric shape attributes such as the forward throw distance, the lateral extent, and the elevation of the blast pile. Hudaverdi et al [9] utilized hierarchical cluster analysis for classifying blasting data into similarity groups and applied discriminant analysis for group membership testing, further employing multiple regression analysis to devise a predictive model for average particle size estimation of slag piles. Ge et al [10] implemented 3D laser scanning for scanning landslide deposits, calculating both volume and block distribution of the accumulations.…”

Development of a predictive model of rock fragmentation for Nui Phao open-pit mine in Vietnam using multiple-output Neural Networks and Monte Carlo Dropout technique

“…Actual data collected from fragmentation measurement using computer vision programs have motivated researchers to design more effective prediction models. Several multi-variate regression algorithms have been applied to capture most blast parameters (Chakraborty et al 2004;Hudaverdi, Kulatilake, and Kuzu 2011). However, the nonlinear relationship of blast parameters is still a challenging task for these models.…”