Rock Fragmentation

The effect of jointing on fragmentation by blasting was investigated experimentally and numerically. Firstly the experimental results were presented. Then the numerical modelling based on the experiments was performed. The same conclusions can be obtained based on both experiments and numerical modelling.

A new three dimensional (3D) mechanistic model of fragmentation, called the Fracture Density Model (FDM) is used to simulate full-scale bench blasts at the Bararp dimensional stone quarry in Sweden. The model uses several computational mechanics algorithms to simulate the effect of blasting in different rock types

This paper develops a new Blastability Index to assess hole-to-hole fragmentation in bench blasting based on a new Rock Factor exclusively obtained from drill-monitoring data, that can provide an automatic assessment of rock structure, strength, and waste/ore, the effective energy factor of the explosive and the delay time per blasthole.

The paper discusses a limestone quarry case study that looks at change in fragmentation due to using different charge sequences when using the regular rhythmic timing method.

Kinematic approach of blast modeling refers to using the velocity, strain, or displacement as controlling parameters to model blasting, rather than the stress and the material constitutive relations. The rock stiffness or constitutive relations is very hard or impossible to obtain due to the complexity of the rock mass property.

A new 3D fragmentation analysis method for muck piles is presented. It works with aerial imagery from UAVs (drones) and handles entire muck piles with minimum user intervention.

Achieving a customer optimized fragmentation by using robust data from new technologies e.g. GPS positions of boreholes, fragmentation analysis to calibrate the geology and fragmentation prediction models. The inclusion of the collected data into the blast’s design makes it possible to accomplish a desired particle size distribution.

This paper is about project improvement to maximize energy distribution while ensuring beneficial blast results (reduce boulder condition). The main problem is how to manage complex lithology conditions related to detonation propagation in energy distribution control.

In this paper, we examine the challenges associated with the use of empirical rock fragmentation models. We highlight key parameters omitted by these models, and propose a machine learning approach that incorporates these parameters, leading to a simple, yet more accurate approach to blast design.

Computer 3D modeling to evaluate the potential underground blasting fragmentation performance based on rock within calculated damage radii along drillholes, as well as the burden distance from explosives along each drillhole. The results estimated from the 3D computer models closely predicted results found in the real world testing.