Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine.

It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted phenomena of blasting and it can damage surrounding structures. Hence, in this paper, estimation and minimization of blast-induced peak particle velocity (PPV) were conducted in two separate phases, namely prediction and optimization. In the prediction phase, an artificial neural network (ANN) model was developed to forecast PPV using as model inputs burden, spacing, distance from blast face, and charge per delay. The results of prediction phase showed that the ANN model, with coefficient of determinations of 0.938 and 0.977 for training and testing stages, respectively, can provide a high level of accuracy. In the optimization phase, the developed ANN model was used as an objective function of firefly algorithm (FA) in order to minimize the PPV. Many FA models were constructed to see the effects of FA parameters on the optimization results. Eventually, it was found that the FA-based optimization was able to decrease PPV to 17 mm/s (or 60% reduction). In addition, burden of 3.1 m, spacing of 3.9 m, and charge per delay of 247 kg were obtained as the values optimized by FA. The results confirmed that both developed techniques of ANN and FA are powerful, accurate, and applicable in estimating and minimizing blasting-induced ground vibration and they can be used with caution in similar fields. [ABSTRACT FROM AUTHOR]