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Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting

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Abstract

Desired rock fragmentation is the main goal of the blasting operation in surface mines, civil and tunneling works. Therefore, precise prediction of rock fragmentation is very important to achieve an economically successful outcome. The primary objective of this article is to propose a new model for forecasting the rock fragmentation using adaptive neuro-fuzzy inference system (ANFIS) in combination with particle swarm optimization (PSO). The proposed PSO–ANFIS model has been compared with support vector machines (SVM), ANFIS and nonlinear multiple regression (MR) models. To construct the predictive models, 72 blasting events were investigated, and the values of rock fragmentation as well as five effective parameters on rock fragmentation, i.e., specific charge, stemming, spacing, burden and maximum charge used per delay were measured. Based on several statistical functions [e.g., coefficient of correlation (R 2) and root-mean-square error (RMSE)], it was found that the PSO–ANFIS (with R 2 = 0.89 and RMSE = 1.31) performs better than the SVM (with R 2 = 0.83 and RMSE = 1.66), ANFIS (with R 2 = 0.81 and RMSE = 1.78) and nonlinear MR (with R 2 = 0.57 and RMSE = 3.93) models. Finally, the sensitivity analysis shows that the burden and maximum charge used per delay have the least and the most effects on the rock fragmentation, respectively.

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Acknowledgements

The authors would like to extend their appreciation to manager, engineers and personnel of Shur river dam, especially Mr. Alireza Farazmand, for providing the needed information and facilities that made this research possible.

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Authors and Affiliations

  1. Department of Mining Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran

    Mahdi Hasanipanah & Mohammad Saber Zamzam

  2. School of Mining, College of Engineering, University of Tehran, Tehran, 11155-4563, Iran

    Hassan Bakhshandeh Amnieh

  3. Young Researchers and Elite Club, Qom Branch, Islamic Azad University, Qom, Iran

    Hossein Arab

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  1. Mahdi Hasanipanah
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Correspondence to Mahdi Hasanipanah.

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Hasanipanah, M., Amnieh, H.B., Arab, H. et al. Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Comput & Applic 30, 1015–1024 (2018). https://doi.org/10.1007/s00521-016-2746-1

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