Abstract
Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)–artificial neural network (ANN), genetic algorithm (GA)–ANN and particle swarm optimization (PSO)–ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R2). The obtained results showed that although all predictive models are able to approximate flyrock, PSO–ANN predictive model can perform better compared to others. Based on R2, values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. These results show higher efficiency of the PSO–ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.
Highlights
As a common solution to eliminate the rock mass, blasting operations are used in some engineering works such as tunnel excavation, road construction, and hydraulic channels [1]
root mean square error (RMSE) values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of imperialist competitive algorithm (ICA)–artificial neural network (ANN), particle swarm optimization (PSO)–ANN and genetic algorithm (GA)–ANN predictive models, respectively. These results indicated that lower system error can be obtained by developing PSO–ANN model among all implemented models
At the end of each model designing, eight models were constructed and their related results were achieved based on RMSE and R2
Summary
As a common solution to eliminate the rock mass, blasting operations are used in some engineering works such as tunnel excavation, road construction, and hydraulic channels [1]. To predict flyrock phenomenon, three hybrid intelligent techniques, namely ICA–ANN, GA–ANN and PSO–ANN, are applied These models were proposed based on the most important parameters influencing flyrock. Raina et al [40] performed a research according to selected parameters of rock mass and blast design for evaluating the horizontal (FSH) and vertical (FSV) safety factors of flyrock. The parameters including density, explosive density hole diameter, and confinement state were used by McKenzie [41] for prediction of flyrock and particle (rock) size. Two power empirical equations were introduced in the study carried out by Marto et al [23] who developed two high-performance empirical formulations for prediction of flyrock These results were obtained from 113 operations where each of them contained charge per delay and powder factor. This technique was based on graph shown for different values of maximum charge per delay in a range of (75–550 kg) and for various powder factor values in a range of (0.5–1.1 kg/m3)
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