Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms

During blasting operations in open pit mines, most of the blast energy (approximately 40%) is wasted due to ground vibration. This phenomenon causes damages to the surrounding structures and pit slope. Therefore, prediction of ground vibration with an appropriate degree of accuracy is important to identify safety area of blasting. In this paper, in the first step, a predictive equation based on gene expression programming (GEP) was developed to estimate ground vibrations of blasting operations conducted in Gol-E-Gohar iron mine. For this aim, 115 blasting operations were identified and the most effective parameters on peak particle velocity (PPV), i.e., burden, spacing, stemming, hole-depth, hole-diameter, powder factor, maximum charge per delay and distance from the blast face were collected from the mine. Capability of the developed GEP model was compared with a non-linear multiple regression (NLMR) model and five conventional equations. The obtained results revealed that the developed GEP model is more efficient compared to the other models in predicting PPV. In the second step, to optimize the GEP predictive model, cuckoo optimization algorithm (COA) was employed and proposed. To do this, several strategies were defined and then several optimized blasting patterns were determined for each strategy. It was found that by developing the COA model, a significant reduction can be happened in PPV values.

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