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Prediction of Flyrock in Mine Blasting: A New Computational Intelligence Approach

  • Hima Nikafshan RadEmail author
  • Iman Bakhshayeshi
  • Wan Amizah Wan Jusoh
  • M. M. Tahir
  • Loke Kok Foong
Original Paper
  • 16 Downloads

Abstract

Blasting is the predominant rock fragmentation technique in civil constructions, underground and surface mines. Flyrock is the unwanted throw of rock fragments during blasting and is the major cause of considerable damage in and around the mines. The present research aimed to propose a new intelligence-based method to predict flyrock. In this regard, the recurrent fuzzy neural network (RFNN) combined with the genetic algorithm (GA) is proposed. For checking the suitability of the RFNN-GA model, artificial neural network (ANN), hybrid ANN and GA and a nonlinear regression model were also employed. To achieve the aims of the research, data for 70 blasting sites including four input parameters (spacing, burden, stemming and maximum charge per delay) and one output parameter (flyrock) were gathered from two quarry mines at the Shur River dam, Iran. The performance of the proposed prediction methods was then assessed with statistical evaluation criteria, i.e., R-square and root mean square error. The results indicate the proposed RFNN-GA model was more superior for prediction of flyrock than the GA-ANN, ANN and nonlinear regression models. According to a sensitivity analysis, the maximum charge per delay was the most influential parameter in flyrock prediction in this case.

Keywords

Blasting operation RFNN-GA ANN GA-ANN 

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Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  • Hima Nikafshan Rad
    • 1
    Email author
  • Iman Bakhshayeshi
    • 2
  • Wan Amizah Wan Jusoh
    • 3
  • M. M. Tahir
    • 4
  • Loke Kok Foong
    • 5
  1. 1.College of Computer ScienceTabari University of BabolBabolIran
  2. 2.SydneyAustralia
  3. 3.Faculty of Civil Engineering and EnvironmentUniversiti Tun Hussein Onn MalaysiaParit Raja, Batu PahatMalaysia
  4. 4.UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia
  5. 5.Center of Tropical Geoengineering, School of Civil Engineering, Faculty of EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia

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