Simultaneous prediction and optimisation of rock fragmentation and ground vibration using an ANN-RF ensemble in open-pit blasting.

ABSTRACT: In this study, we model a solution surface using an ensemble machine learning approach to simultaneously predict and optimise rock fragmentation and ground vibration during blasting operations at the Jwaneng Diamond Mine in Botswana. Using 120 production blasts and ten input parameters, we trained several models: ANN, RF, ANN-RF and other hybrid baselines PSO-ANN, PSO-ELM, ANN-SVR, PSO-XGBoost and GA-ANN . The ANN-RF ensemble achieved the best test performance (R2 = 0.956, RMSE = 0.315, MAE = 0.250, and VAF = 95.5 for fragmentation; R2 = 0.930, RMSE = 0.380, MAE = 0.302, VAF = 92.5 for PPV). Tree-SHAP analysis identified powder factor and burden as dominant fragmentation drivers, and burden, charge per delay, and distance as key vibration controls. A projected gradient-descent search on th

SUBMITTER: Saubi O 

PROVIDER: S-EPMC12852921 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature