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A novel intelligent approach to simulate the blast-induced flyrock based on RFNN combined with PSO

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Abstract

This research focuses to propose a new hybrid approach which combined the recurrent fuzzy neural network (RFNN) with particle swarm optimization (PSO) algorithm to simulate the flyrock distance induced by mine blasting. Here, this combination is abbreviated using RFNN–PSO. To evaluate the acceptability of RFNN–PSO model, adaptive neuro-fuzzy inference system (ANFIS) and non-linear regression models were also used. To achieve the objective of this research, 72 sets of data were collected from Shur river dam region, in Iran. Maximum charge per delay, stemming, burden, and spacing were considered as input parameters in the models. Then, the performance of the RFNN–PSO model was evaluated against ANFIS and non-linear regression models. Correlation coefficient (R2), Nash and Sutcliffe (NS), mean absolute bias error (MABE), and root-mean-squared error (RMSE) were used as comparing statistical indicators for the assessment of the developed approach’s performance. Results show a satisfactory achievement between the actual and predicted flyrcok values by RFNN–PSO with R2, NS, MABE, and RMSE being 0.933, 0.921, 13.86, and 15.79, respectively.

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Authors and Affiliations

  1. Department of ECE, Vivekanandha College of Technology for Women, Tiruchengode, Namakkal, Tamilnadu, 637205, India

    P. T. Kalaivaani

  2. Department of Computer Science, College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia

    T. Akila

  3. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    M. M. Tahir

  4. Department of Management Sciences, COMSATS University Islamabad, Vehari Campus, Punjab, Pakistan

    Munir Ahmed

  5. School of Electronics, Vignan Foundation for Science, Technology and Research, Guntur, India

    Aravindhan Surendar

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  1. P. T. Kalaivaani
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  2. T. Akila
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Correspondence to Aravindhan Surendar.

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Kalaivaani, P.T., Akila, T., Tahir, M.M. et al. A novel intelligent approach to simulate the blast-induced flyrock based on RFNN combined with PSO. Engineering with Computers 36, 435–442 (2020). https://doi.org/10.1007/s00366-019-00707-2

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  • DOI: https://doi.org/10.1007/s00366-019-00707-2

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