Prediction of Flyrocks, Airblasts and Ground Vibrations Using Neural Computing and Applications at ZCDC Mine

Authors

  • Charles Chewu Mining Engineering Department. Manicaland State University of Applied Sciences (M.S.U.A.S.), Mutare, Zimbabwe https://orcid.org/0000-0002-3930-3944
  • Tonderai Chikwere Mining Engineering Department. Manicaland State University of Applied Sciences (M.S.U.A.S.), Mutare, Zimbabwe
  • Desire Runganga Mining Engineering Department. Manicaland State University of Applied Sciences (M.S.U.A.S.), Mutare, Zimbabwe https://orcid.org/0000-0003-3301-9528
  • Elia Chipfupi Mining Engineering Department. Manicaland State University of Applied Sciences (M.S.U.A.S.), Mutare, Zimbabwe
  • Tatenda Nyamagudza Mining Engineering Department. Manicaland State University of Applied Sciences (M.S.U.A.S.), Mutare, Zimbabwe https://orcid.org/0000-0002-7895-9604

DOI:

https://doi.org/10.56532/mjsat.v3i3.135

Keywords:

Flyrock, Airblast, Multivariate Regression, Bayesian Regularisation

Abstract

When an explosive detonates in a blasthole, approximately 20 to 30% of the energy is only utilized for fragmenting the rock mass whilst the bulk of the energy is lost in the form of ground vibrations, flyrocks and airblasts. Employees’ residences, mine offices, processing plants and engineering workshops built close to the mining area and are in danger of being damaged by blast induced hazards.  In addition, due to flyrocks there was a high level of ore losses, ore dilution, equipment damages, mine roads and powerlines. To assess and reduce these negative impacts, monitoring of flyrocks, ground vibrations and airblast was carried out and generate a prediction model. A three-layer, feed-forward back-propagation of a 9-10-3 network architecture was trained, validated, and tested using the Bayesian regularization algorithm. A total of 100 monitored blast records obtained from the mine were used as input parameters for the ANN prediction model. Subsequently a Multivariate Regression analysis prediction model was run and used to compare with the results obtained from ANN model. Based on the study's findings, an ANN model proved to be the best for field predictions.To determine the relative impact of each input parameter on flyrocks, gound vibration, and Airblast, a sensitivity analysis was also carried out and lastly blast optimization which managed to reduce blast induced impacts by over 30%.

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Published

2023-09-11

How to Cite

[1]
C. Chewu, T. Chikwere, D. Runganga, Elia Chipfupi, and T. Nyamagudza, “Prediction of Flyrocks, Airblasts and Ground Vibrations Using Neural Computing and Applications at ZCDC Mine”, Malaysian J. Sci. Adv. Tech., vol. 3, no. 3, pp. 212–221, Sep. 2023.