Visualization of Predicted Ground Vibration Induced by Blasting in Urban Quarry Site Utilizing Web-GIS

Blasting is routinely carried out at various quarries. When blasting is done in an urban area, the ground vibration induced by the operation may affect nearby residents physically and mentally. In this study, a visualization system of ground vibration induced by blasting is constructed for the purpose of reducing these adverse effects. The system consists of two phases. The first is the ground vibration prediction by using artificial intelligence, specifically an ANN (Artificial Neural Network). The second is the visualization of the predicted vibration through Web-GIS. Four prediction factors, namely MIC (Maximum Instantaneous Charge), distance, elevation difference, and direction were used and PPV (Peak Particle Velocity) was used as an index of ground vibration strength. Colored contours representing vibration intensity were generated using GIS tools based on predicted PPV. Furthermore, the contour is converted into a KMZ file and overlaid on a web-based map (Google Maps) that also displays other pertinent information about the quarry vicinity. This means that the system can be used by anyone who has an internet connection and access to a browser. The data would be available to residents, local government officers, and anyone else who wishes to use it. In addition, the ground vibration prediction data and contour maps could also be used to optimize blasting designs in advance. Through the use of this system, optimal blasting can be done, maximizing the productivity of the quarry as well as minimizing the impact on the local residences

Application of artificial intelligence techniques for predicting the flyrock, Sungun mine, Iran

Flyrock is known as one of the main problems in open pit mining operations. This phenomenon can threaten the safety of mine personnel, equipment and buildings around the mine area. One way to reduce the risk of accidents due to flyrock is to accurately predict that the safe area can be identified and also with proper design of the explosion pattern, the amount of flyrock can be greatly reduced. For this purpose, 14 effective parameters on flyrock have been selected in this paper i.e. burden, blasthole diameter, sub-drilling, number of blastholes, spacing, total length, amount of explosives and a number of other effective parameters, predicting the amount of flyrock in a case study, Songun mine, using linear multivariate regression (LMR) and artificial intelligence algorithms such as Gray Wolf Optimization algorithm (GWO), Moth-Flame Optimization algorithm (MFO), Whale Optimization Algorithm (WOA), Ant Lion Optimizer (ALO) and Multi-Verse Optimizer (MVO). Results showed that intelligent algorithms have better capabilities than linear regression method and finally method MVO showed the best performance for predicting flyrock. Moreover, the results of the sensitivity analysis show that the burden, ANFO, total rock blasted, total length and blast hole diameter are the most significant factors to determine flyrock, respectively, while dynamite has the lowest impact on flyrock generation.Peer ReviewedObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPostprint (published version