What question did this study set out to answer?

This research aims to evaluate the accuracy of different models for predicting blasted material size at drawpoints in sublevel caving.

April 22, 2026Open Access

Evaluating the accuracy of empirical and regression models for predicting blast-induced fragment size in sublevel caving: Ernest Henry mine case study

Puntos clave

This research aims to evaluate the accuracy of different models for predicting blasted material size at drawpoints in sublevel caving.
Conducted full-scale blasting trials at Ernest Henry mine, varying explosive density and burden size.
Measured fragmentation using laser scanning to gather particle-size distribution data.
Evaluated five different predictive models for performance in forecasting P20, P50, and P80 sizes.
Extended Kuz-Ram and TCM yielded better predictions for finer fragment sizes, achieving MAPE of 8%.
URBM provided the highest accuracy for median (P50) and coarse (P80) sizes, with MAPE of 5% and 3% respectively.
Sensitivity analysis highlighted essential variables affecting median size predictions including rock properties and design parameters.

Resumen

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.

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