J. Shakeri - Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
H. Amini Khoshalan - Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
H. Dehghani - Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
M. Bascompta - Department of Mining Engineering, Polytechnic University of Catalonia, Barcelona, Spain
K. Onyelowe - Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria

In this research work, a comprehensive study is conducted to predict flyrock as a typical and undesirable phenomenon occurring during the blasting operation in open-pit mining. Despite the availability of several empirical methods for predicting the flyrock distance, the complexity of flyrock analysis has resulted in the low performance of these models. Therefore, the statistical and robust artificial intelligence techniques are applied for flyrock prediction in the Sungun copper mine in Iran. For this purpose, the linear multivariate regression (LMR), imperialist competitive algorithm (ICA), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) methods are applied to predict flyrock with effective parameters including the blasthole diameter, stemming, burden, powder factor, and maximum charge per delay. According to the attained results, the ANN model with the structure of ۵-۸-۱, Levenberg-Marquardt as the learning algorithm, and log-sigmoid (logsig) as the transfer functions are selected as the optimal network with the RMSE and R۲ values of ۵.۰۴ m and ۹۵.۶% to predict flyrock, respectively. Also it can be concluded that the ICA technique has a relatively high capability in predicting flyrock, with the LMR and ANFIS models placed in the next. Finally, the sensitivity analysis reveal that the powder factor and blasthole diameters have the most importance on the flyrock distance in the present work.

Flyrock distance, linear multivariate regression, Imperialist Competitive Algorithm, Adaptive Neuro-Fuzzy Inference System, Artificial Neural Network