REVIEW OF ARTIFICIAL NEURAL NETWORK ALGORITHM APPLICATION IN PREDICTING BLAST VIBRATIONS - Yuga Maulana; Ganda Marihot Simangunsong; Tri Karian (Universitas Lambung Mangkurat, 2021)
The blasting method is one of the best hard rock excavation methods in mining activities. This method has negative impacts, one of which is the vibrations generated by the residual energy of the explosion. This impact will affect the environment around the blasting area, both slope stability, tunnels, infrastructure, and human settlements if it is close to the blasting site. Therefore, it needs initial planning and prediction to anticipate the blasting vibration that occurs. In general, the blast vibration can be predicted using the scale distance method which uses two parameters, namely the maximum amount of explosive material per time delay and the distance of measurement from the location of the explosion. This method has been widely researched to produce several empirical equations from each explosion location studied. However, as technology develops, several studies have tried to use artificial intelligence technology, one of which is the artificial neural network algorithm as a new approach for predicting detonation vibrations. In this method, the development of the parameters used in predicting the weighting of the most influential parameters from the formation of detonation vibrations can be carried out. This study will review several studies related to the use of artificial neural networks in predicting blasting vibrations in the studies that have been carried out and also compare with prediction methods using several empirical equations.