Underground Blasting Optimization by Artificial Intelligence Techniques

Abstract

Drilling and blasting is recognized as the most economical method in hard rock mining and it has been widely applied both surface and underground mining. Over the past few decades, the efficiency of mine blasting has been greatly increased but still there are unavoidable drawbacks of drilling and blasting method. Blasting hazards such as ground vibration, flyrock, air blast and toxic fumes should be considered before blasting design stage. Especially blasting in tunnelling and underground mine, uneven break of perimeter area is an essential issue not only for ensuring the safe working environment but also for the profitability of the project. In this paper, some of artificial intelligence applications to predict blasting hazards are reviewed. Then, nonlinear multiple regression analysis and artificial neuron network (ANN) models were developed and applied to predict uneven break on a tunnel project located in Gumi, Korea. The results indicated that ANN was successfully utilized to predict uneven break of tunnel blasting.