Resumen
The two most frequently heard terms in the mining industry are safety and production. These two terms put a lot of pressure on blasting engineers and crew to give more while consuming less. The key to achieving the optimum blasting results is sophisticated bench analysis, which must be combined with design blast parameters for good fragmentation and safe ground vibration. Thus, a unique solution for forecasting both optimum fragmentation and reduced ground vibration using rock mass joint angle and blast design parameters will aid the blasting operations in terms of cost savings. To arrive at a proper understanding and a solution, 152 blasts were carried out in various mines by adjusting blast design parameters concerning the measured joint angle. The XG Boost, K-Nearest Neighbor, and Random Forest algorithms were evaluated, and the XG Boost outputs were shown to be superior in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Co-efficient of determination (R2) values. Using XG Boost, the decision-tree-based ensemble Machine Learning algorithm that uses a gradient-boosting framework and a simultaneous formula was developed to predict both fragmentation and ground vibration using joint angle and the same set of parameters.