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A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting

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Abstract

In this paper, a novel hybrid approach is proposed for predicting peak particle velocity (PPV) due to bench blasting in open pit mines. The proposed approach is based on the combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO). In this approach, the PSO is used to improve the performance of ANFIS. Furthermore, a model is developed based on support vector regression (SVR) approach. The models are trained and tested based on actual data compiled from 120 blast rounds in Sarcheshmeh copper mine. To determine the accuracy and efficiency of ANFIS–PSO and SVR models, a statistical model (USBM equation) is applied. According to the obtained results, both techniques can be used to predict the PPV, but the comparison of models shows that the ANFIS–PSO model provides better results. Root mean square error (RMSE), variance account for (VAF), and coefficient of determination (R 2) indices were obtained as 1.83, 93.37 and 0.957 for ANFIS–PSO model, respectively.

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Acknowledgments

The authors would like to thank two anonymous reviewers for their critical reviews and constructive comments.

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Authors and Affiliations

  1. Department of Mining Engineering, Isfahan University of Technology, Isfahan, P.O. Box 8415683111, Iran

    Ebrahim Ghasemi, Hamid Kalhori & Raheb Bagherpour

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  1. Ebrahim Ghasemi
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  2. Hamid Kalhori
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  3. Raheb Bagherpour
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Correspondence to Ebrahim Ghasemi.

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Ghasemi, E., Kalhori, H. & Bagherpour, R. A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting. Engineering with Computers 32, 607–614 (2016). https://doi.org/10.1007/s00366-016-0438-1

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