This paper presents a new hybrid artificial
neural network (ANN) optimized by imperialist competitive
algorithm (ICA) to predict peak particle velocity
(PPV) resulting from quarry blasting. For this purpose, 95
blasting works were precisely monitored in a granite quarry
site in Malaysia and PPV values were accurately recorded
in each operation. Furthermore, the most influential
parameters on PPV were measured and used to train the
ICA-ANN model. Considering the measured data from the
granite quarry site, a new empirical equation was developed
to predict PPV. For comparison, a pre-developed
ANN model was developed for PPV prediction. The results
demonstrated that the proposed ICA-ANN model is able to
predict blasting-induced PPV better than other presented
techniques.
