TITLE:
Numerical Prediction of Air Overpressure during Mining Operations in an Open Pit Mine
AUTHORS:
Michael Bulangashane Ndagano, Alex Kalonji-Kabambi, Augustin Tshifung, Albert Agisha Ntwali
KEYWORDS:
Numerical Prediction, Artificial Intelligence (AI), Air Overpressure (AOp), Blasting, Open Pit Mining
JOURNAL NAME:
Engineering,
Vol.17 No.7,
July
30,
2025
ABSTRACT: Blasting is considered an indispensable process in mining excavation operations. Generally, only a small percentage of the total energy of blasting is consumed in the fragmentation and displacement of the rock, and the rest of the energy is transmitted to the structures and environment surrounding the mined area. The air overpressure (AOp) induced by explosions in open-cast mines has unavoidable environmental and safety consequences, but can be minimized to an acceptable threshold to limit environmental damage and the impact on the sustainability of mining activities. The development of numerical predictive models of AOp was the main objective of this study. Thus, the methodology used to achieve this main objective was articulated around six parts: knowledge of the study area, processing and statistical analysis of the data collected, development of both numerical and empirical prediction models, and evaluation of model performance of the numerical model parameters. The results show that only numerical models are suitable for predicting AOp. Moreover, numerical models generally perform better than empirical models in predicting this phenomenon. Among these AI models, the results show that the DT model is the best suited for predicting AOp in this study, with remarkable performance results (RRSE of 0.08, RAE of 0.05, RMSE of 0.29, MAE of 0.37, MAPE of 0.07, and an R2 of 0.994). This could therefore justify its application in practical engineering to predict blast-induced AOp in open-cast mines to reduce undesirable environmental effects.