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

  • P. T. Kalaivaani
  • T. Akila
  • M. M. Tahir
  • Munir Ahmed
  • Aravindhan SurendarEmail author
Original Article
  • 31 Downloads

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.

Keywords

Flyrock distance Recurrent fuzzy neural network Particle swarm optimization Adaptive neuro-fuzzy inference system 

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • P. T. Kalaivaani
    • 1
  • T. Akila
    • 2
  • M. M. Tahir
    • 3
  • Munir Ahmed
    • 4
  • Aravindhan Surendar
    • 5
    Email author
  1. 1.Department of ECEVivekanandha College of Technology for WomenTiruchengode, NamakkalIndia
  2. 2.Department of Computer Science, College of Computer ScienceKing Khalid UniversityAbhaKingdom of Saudi Arabia
  3. 3.UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia
  4. 4.Department of Management SciencesCOMSATS University IslamabadPunjabPakistan
  5. 5.School of ElectronicsVignan Foundation for Science, Technology and ResearchGunturIndia

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