Name
Title | ||
|---|---|---|
Three hybrid intelligent models in estimating flyrock distance resulting from blasting. |
Abstract | ||
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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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00366-018-0596-4 | Eng. Comput. (Lond.) |
Keywords | Field | DocType |
Flyrock, Genetic algorithm, Particle swarm optimization, Imperialist competitive algorithm | Particle swarm optimization,Mathematical optimization,Intelligent decision support system,Mean squared error,Parametric statistics,Coefficient of determination,Statistics,Artificial neural network,Imperialist competitive algorithm,Mathematics,Genetic algorithm | Journal |
Volume | Issue | ISSN |
35 | 1 | 0177-0667 |
Citations | PageRank | References |
11 | 0.55 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammadreza Koopialipoor | 1 | 18 | 1.02 |
| A. Fallah | 2 | 17 | 2.82 |
| danial jahed armaghani | 3 | 585 | 36.46 |
| Aydin Azizi | 4 | 17 | 2.39 |
| Edy Tonnizam Mohamad | 5 | 181 | 10.85 |