A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

TLDR

Flyrock, a major blast‑induced disturbance that can damage nearby structures, requires precise prediction and control through blast design adjustments to mitigate risk. The study aims to predict blast‑induced flyrock using a novel combination of imperialist competitive algorithm and artificial neural network. The authors recorded parameters from 113 blasting operations, measured flyrock distances, trained an ICA‑ANN model, and compared its performance against a back‑propagation ANN and two empirical predictors. Sensitivity analysis identified maximum charge per delay and powder factor as key drivers, and the ICA‑ANN model outperformed both the BP‑ANN and empirical predictors in predicting flyrock distance.

Abstract

Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches.