A nonlinear model to estimate vibration frequencies in surface mines

Document Type : Research Paper

Authors

1 Mining engineering department, Urmia university of Technology.

2 Faculty of Mining and Metallurgical Engineering, Urmia University of Technology, Urmia

Abstract

Twenty measured blast data from the Golegohar iron mine (southern Iran) were used to generalize nonlinear models for the estimation of dominant frequencies of blast waves using rock mass, explosive characteristics, and blast design. The imperialist Competitive Algorithm (ICA) was used to determine the nonlinear regression model coefficients. Possessing a good correlation coefficient, the proposed model can be directly used for predicting blast-induced dominant frequencies of waves. The determination coefficient (R2) found by the ACI-based nonlinear model was 0.98 for frequency, while that of the traditional Multivariate Linear Regression Model (MVLRM) was 0.89. Also, according to the calculation of other well-known statistical errors between the estimated and real measured values of frequency, ICA-based models have higher Variance Account for (VAF) value, as well as lower values of Route Mean Square Error (RMSE), Variance Absolute Relative Error (VARE), Median Absolute Error (MEDAE), and Mean absolute percentage error (MAPE)compared to the linear model. It was found that the proposed nonlinear model is more accurate and capable of estimating the values of the dominant frequency of blast waves.

Keywords

References

[1] Hossaini, S. and Sen G. (2004). Effect of explosive type on particle velocity criteria in ground vibration. Journal of Explosives Engineering, 21(4), 34-36.
[2] Khandelwal, M. and Singh T.N. (2006). Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach. Journal of Sound and Vibration, 289(4), 711-725.
[3] Bakhshandeh Amnieh, H., Siamaki A., and Soltani S. (2012). Design of blasting pattern in proportion to the peak particle velocity (PPV): Artificial neural networks approach. Safety Science, 50(9), 1913-1916.
[4] Khandelwal, M. and Singh T.N. (2009). Prediction of blastinduced ground vibration using artificial neural network. International Journal of Rock Mechanics and Mining Sciences, 46(7), 1214-1222.
[5] Q. Yuan, L. Wu, Qingjun Zuo, and Li B. (2014). Peak particle velocity and principal frequency prediction based on RS-FNN comprehension method for blasting vibration. Electronic Journal of Geotechnical Engineering, 19, 10043-10056.
[6] Singh, T. and Singh V. (2005). An intelligent approach to prediction and control ground vibration in mines. Geotechnical & Geological Engineering, 23(3), 249-262.
[7] Mines, U.S.B.o. and Siskind D. (1980). Structure response and damage produced by ground vibration from surface mine blasting. US Department of the Interior, Bureau of Mines New York.
[8] Singh, T., Singh A., and Singh C. (1994). Prediction of ground vibration induced by blasting. Indian Min Eng J, 31-34(33), 16.
[9] Singh, T. (2004). Artificial neural network approach for prediction and control of ground vibrations in mines. Mining Technology, 113(4), 251-256.
[10] Berta, G. (1994). Blasting-induced vibration in tunnelling. Tunnelling and Underground Space Technology, 9(2), 175-187.
[11] Adhikari, G. and Singh R. (1989). Structural response to ground vibration from blasting in opencast coal mines. Journal of Mines, Metals & Fuels, 37(4), 135-138.
[12] Singh, T.N. and Verma A.K. (2010). Sensitivity of total charge and maximum charge per delay on ground vibration. Geomatics, Natural Hazards and Risk, 1(3), 259-272.
[13] Saeidi, O., Torabi S.R., Ataei M., and Rostami J. (2014). A stochastic penetration rate model for rotary drilling in surface mines. International Journal of Rock Mechanics and Mining Sciences, 68, 55-65.
[14] Kamkar-Rouhani, A. and Hojat A.). Determination of groundwater and geological factors using geoelectrical methods to design a suitable drainage system in Gol-e-Gohar iron ore M. Mokhtarian-Asl & A. Alipour / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 167-171 171 mine, Iran.
[15] Singh, D. and Sastry V. (1986). Rock fragmentation by blasting influence of joint filling material. Journal of Explosive Engineering, 18-27.
[16] Khabbazi, A., Atashpaz-Gargari E., and Lucas C. (2009). Imperialist competitive algorithm for minimum bit error rate beamforming. International Journal of Bio-Inspired Computation, 1(1-2), 125-133.
[17] Atashpaz-Gargari, E. and Lucas C. (2007). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. in Evolutionary computation, 2007. CEC 2007. IEEE Congress on. IEEE, 4661-4667.
[18] Shokrollahpour, E., Zandieh M., and Dorri B. (2011). A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem. International Journal of Production Research, 49(11), 3087-3103.
[19] Sadaei, H.J., Enayatifar R., Lee M.H., and Mahmud M. (2016). A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting. Applied Soft Computing, 40, 132-149.
[20] Sharifi, M.A. and Mojallali H. (2015). A modified imperialist competitive algorithm for digital IIR filter design. Optik - International Journal for Light and Electron Optics, 126(21), 2979-2984.
[21] Ardalan, Z., Karimi S., Poursabzi O., and Naderi B. (2015). A novel imperialist competitive algorithm for generalized traveling salesman problems. Applied Soft Computing, 26, 546-555.
[22] Maroufmashat, A., Sayedin F., and Khavas S.S. (2014). An imperialist competitive algorithm approach for multi-objective optimization of direct coupling photovoltaic-electrolyzer systems. International Journal of Hydrogen Energy, 39(33), 18743-18757.
[23] Nazari-Shirkouhi, S., Eivazy H., Ghodsi R., Rezaie K., and Atashpaz-Gargari E. (2010). Solving the integrated product mixoutsourcing problem using the Imperialist Competitive Algorithm. Expert Systems with Applications, 37(12), 7615-7626.
[24] Mokhtarian Asl, M. and Sattarvand J. (2016). An imperialist competitive algorithm for solving the production scheduling problem in open pit mine. Int. Journal of Mining & GeoEngineering, 50(1), 131-143.
[25] Behnamian, J. and Zandieh M. (2011). A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties. Expert Systems with Applications, 38(12), 14490-14498.
[26] Lian, K., Zhang C., Gao L., and Shao X. (2012). A modified colonial competitive algorithm for the mixed-model U-line balancing and sequencing problem. International Journal of Production Research, 50(18), 5117-5131.
[27] Mortazavi, A., Khamseh A.A., and Naderi B. (2015). A novel chaotic imperialist competitive algorithm for production and air transportation scheduling problems. Neural Computing and Applications, 26(7), 1709-1723.
International Journal of Mining and Geo-Engineering
Volume 54, Issue 2
December 2020
Pages 167-171

Files

Share

How to cite

Statistics