This study compares the accuracy of empirical and regression models for predicting the size distribution of blasted material at drawpoints in sublevel caving. Field data were sourced from Ernest Henry mine (EHM). At EHM, full-scale blasting trials were conducted under controlled conditions by varying explosive density and burden size, with fragmentation measured using laser scanning at different extraction tonnages. To minimise scanning inaccuracies, scan results were combined to represent the particle-size distribution for the EHM data. Five models were evaluated: Kuz-Ram, extended Kuz-Ram, Two Component Model (TCM), Kuznetsov–Cunningham–Ouchterlony, and a regression-based Underground Ring Blasting Model (URBM) developed in our previous work. Models were assessed for predicting P20, P50, and P80 passing sizes. Results show the extended Kuz-Ram and TCM perform better for finer fragment sizes, with Mean Absolute Percentage Error (MAPE) of 8%. URBM was the most accurate for median and coarse sizes (MAPE 5% for P50 and 3% for P80) and showed the least variability in errors. A SHapley Additive exPlanations-based sensitivity analysis of the three most accurate models identified key variables for median size prediction, highlighting the importance of rock properties and ring design parameters.

This study compares the accuracy of empirical and regression models for predicting the size distribution of blasted material at drawpoints in sublevel caving. Field data were sourced from Ernest Henry mine (EHM). At EHM, full-scale blasting trials were conducted under controlled conditions by varying explosive density and burden size, with fragmentation measured using laser scanning at different extraction tonnages. To minimise scanning inaccuracies, scan results were combined to represent the particle-size distribution for the EHM data. Five models were evaluated: Kuz-Ram, extended Kuz-Ram, Two Component Model (TCM), Kuznetsov-Cunningham-Ouchterlony (KCO), and a regression-based Underground Ring Blasting Model (URBM) developed in our previous work. Models were assessed for predicting P20, P50, and P80 passing sizes. Results show the extended Kuz-Ram and TCM perform better for finer fragment sizes, with Mean Absolute Percentage Error (MAPE) of 8%. URBM was the most accurate for median and coarse sizes (MAPE 5% for P50 and 3% for P80) and showed the least variability in errors. A SHAP-based sensitivity analysis of the three most accurate models identified key variables for median size prediction, highlighting the importance of rock properties and ring design parameters.

This study investigates the influence of ore particle size on gravity-driven flow behaviour and dilution at the drawpoint in sublevel caving operations using a particle-based modelling framework implemented in the Blender rigid body physics engine. Two representative case studies with non-uniform particle-size distributions are analysed: the Ernest Henry Mine, supported by ring marker trial data, and the DeGagné dilution curve derived from discrete element simulations calibrated against Ridgeway mine data. For both case studies, a systematic calibration procedure is conducted to identify suitable modelling configurations and evaluate the sensitivity of key physics-engine parameters. Half-geometry simulations are adopted to overcome hardware limitations. The results show that particle linear damping is the dominant parameter controlling flow behaviour, while particle friction and geometry also significantly influence flow responses. Simulations using purely spherical particles underestimate inter-particle contact effects and produce less realistic behaviour. The most representative results are obtained using lower damping for ore relative to waste particles and non-spherical dodecahedral particles. Following calibration, increasing ore particle size by 10% and 20% leads to higher dilution and lower ore recovery, particularly at intermediate extraction levels, due to reduced ore mobility and earlier waste breakthrough.