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Evaluation of ground vibrations induced by blasting in a limestone quarry


Affiliations
1 Department of Mining Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi 221 005, India
 

Despite being a versatile and low-cost method, rock blasting produces undesirable severe effects. The present study aims to examine the ground vibrations produced by blasting, which are of serious concern to mine operators as well as the nearby inhabitants. Forty-nine field-scale trial blasts were conducted and recor­ded to measure ground vibrations produced by blasting in a limestone quarry in Rajasthan, India. The multivariate linear regression (MLR) and artificial neural network (ANN) techniques were used to predict the peak particle velocity (PPV) with distance between the blasting site and measuring station, charge per delay and scaled distance as the input parameters. Subsequently, a coefficient of determination (R2) was calculated using MLR and ANN appro­aches. Additionally, to verify whether the recorded events exceeded the threshold levels, the values of PPV and dominant frequency propounded by the United States Bureau of Mines (USBM), German standard (DIN), and Director General of Mines Safety, India were carefully scrutinized. Results were compared based on R2 values obtained by the USBM predictor equation, MLR and ANN techniques. It was found that ANN provided a good prediction with a high degree of correlation (0.901) in comparison to MLR (0.754). Also, frequency analysis for the study field showed that the dominance of frequencies was in the range 10–40 Hz. Although the values were within safe limits, disturbances may be witnessed in nearby structures if PPV values are high at lower frequency range.
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  • Uysal, O., Erarslan, K., Cebi, M. A. and Akcakoca, H., Effect of barrier holes on blast induced vibration. Int. J. Rock Mech. Min. Sci., 2008, 45(5), 712–719.

  • Ozdemir, K., Kahriman, A., Tuncer, G., Akgundogdu, A., Elver, E. and Ucan, O. N., Fragmentation assessment using a new image processing technique based on adaptive neuro fuzzy inherence systems. In Proceedings of the Annual Conference on Explosives and Blasting Technique, International Society of Explosives Engi-neers, 2004, vol. 2, pp. 181–188.

  • Felice, J. J., Applications of modelling to reduce vibration and airblast levels. In International Symposium on Rock Fragmenta-tion by Blasting, Vienna, 1993, pp. 145–151.

  • Tuncer, G., Kahriman, A., Ozdemir, K., Guven, S., Ferhatoglu, A. and Gezbul, T., The damage risk evaluation of ground vibration induced by blasting in Naipli Quarry. In Third International Con-ference Modern Management of Mine Producing, Geology and Environmental Protection, Varna, Bulgaria, 2003, pp. 9–13.

  • Erarslan, K., Uysal, Ö., Arpaz, E. and Cebi, M. A., Barrier holes and trench application to reduce blast induced vibration in Sey-itomer coal mine. J. Environ. Geol., 2008, 54(6), 1325–1331.

  • Uysal, O. and Cavus, M., Effect of a pre-split plane on the frequencies of blast induced ground vibrations. Acta Montan. Slovaca, 2013, 18(2), 101–109.

  • Görgülü, K., Arpaz, E., Uysal, Ö., Durutürk, Y. S., Yüksek, A. G., Koçaslan, A. and Dilmaç, M. K., Investigation of the effects of blasting design parameters and rock properties on blast-induced ground vibrations. Arab. J. Geosci., 2015, 8(6), 4269–4278.

  • Amiri, M., Hasanipanah, M. and Amnieh, H. B., Predicting ground vibration induced by rock blasting using a novel hybrid of neural network and itemset mining. J. Neural Comput. Appl., 2020, 9, 1–9.

  • Zouari, H., Geodynamic evolution of centro meridional atlas of Tunisia. Stratigraphy, geometric analysis, cinematic and tectono-sedimentary. Ph D thesis, University of Tunis II, Tunisia, 1995.

  • Görgülü, K., Arpaz, E., Demirci, A., Koçaslan, A., Dilmaç, M. K. and Yüksek, A. G., Investigation of blast-induced ground vibra-tions in the Tülü boron open pit mine. Bull. Eng. Geol. Environ., 2013, 72(3–4), 555–564.

  • Ozer, U., Kahriman, A., Aksoy, M., Adiguzel, D. and Karadogan, A., The analysis of ground vibrations induced by bench blasting at Akyol quarry and practical blasting charts. J. Environ. Geol., 2008, 54(4), 737–743.

  • Azizabadi, H. R., Mansouri, H. and Fouché, O., Coupling of two methods, waveform superposition and numerical, to model blast vibration effect on slope stability in jointed rock masses. J. Com-put. Geotech., 2014, 61, 42–49.

  • Saadat, M., Khandelwal, M. and Monjezi, M., An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran. J. Rock Mech. Geotech. Eng., 2014, 6(1), 67–76.

  • Ambraseys, N. N. and Hendron, A. J., In Dynamic Behaviour of Rock Masses, John Wiley, London, 1968.

  • Langefors, U. and Kihlström, B., The Modern Technique of Rock Blasting, John Wiley, New York, 1978, p. 438.

  • Ghosh, A. and Daemen, J. K., A simple new blast vibration pre-dictor of ground vibrations induced predictor. In Proceedings of the 24th US Symposium on Rock Mechanics, Texas, USA, 1983.

  • Roy, P. P., Vibration control in an opencast mine based on improved blast vibration predictors. J. Min. Sci. Technol., 1991, 12(2), 157–165.

  • Singh, V. K., Singh, D. and Singh, T. N., Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int. J. Rock Mech. Min. Sci., 2001, 38(2), 269–284.

  • Singh, T. N., Kanchan, R., Saigal, K. and Verma, A. K., Predic-tion of p-wave velocity and anisotropic property of rock using artificial neural network technique. Council of Scientific and In-dustrial Research, 2004.

  • Kosko, B., Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence, Prentice Hall, Eng-lewood Cliffs, NJ, 1992, p. 449.

  • Meulenkamp, F. and Grima, M. A., Application of neural net-works for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int. J. Rock Mech. Min. Sci., 1999, 36(1), 29–39.

  • Khandelwal, M. and Singh, T. N., Evaluation of blast-induced ground vibration predictors. J. Soil Dyn. Earthq. Eng., 2007, 27(2), 116–125.

  • Siskind, D. E., Structure, response and damage produced by ground vibration from surface mine blasting. US Department of the Interior, Bureau of Mines, New York, USA, 1980.

  • GSO, Vibrations in building construction. DIN 4150, German Standards Organization, Berlin, 1984.

  • Adhikari, G. R., Jain, N. K., Roy, S., Theresraj, A. I., Balachander, R., Venkatesh, H. S. and Rn, G., Control measures for ground vibration induced by blasting at coal mines and assessment of damage to surface structures. J. Rock Mech. Tunnel. Technol., 2006, 12(1), 3–19.

  • Abdel-Rasoul, E. I., Measurement and analysis of the effect of ground vibrations induced by blasting at the limestone quarries of the Egyptian cement company. ICEHM 2000, Cairo University, Egypt, 2000, pp. 54–71.

  • Dowding, C. H., Suggested method for blast vibration monitoring. Int. J. Rock Mech. Min. Geomech. Abstr., 1992, 29(2), 143–156.

  • Monjezi, M., Rizi, S. H., Majd, V. J. and Khandelwal, M., Artifi-cial neural network as a tool for backbreak prediction. J. Geotech. Geol. Eng., 2014, 32(1), 21–30.

  • Zhongya, Z. and Xiaoguang, J., Prediction of peak velocity of blasting vibration based on artificial neural network optimized by dimensionality reduction of FA-MIV. J. Math. Prob. Eng., 2018, 12.

  • Lawal, A. I. and Idris, M. A., An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations. Int. J. Environ. Stud., 2020, 77(2), 318–334.

  • Leondes, C. T., Neural network systems techniques and applica-tions. In Advances in Theory and Applications, Academic Press, 1998.

  • Blackwell, W. J. and Chen, F. W., Neural Networks in Atmospheric Remote Sensing, Artech House, 2009, pp. 78–90.

  • Nicholls, H. R., Blasting vibrations and their effects on structures. US Department of the Interior, Bureau of Mines, 1971, pp. 656–660.

  • Rorke, A. J., Blasting impact assessment for the proposed new largo colliery based on new largo mine plan 6. J. AJR NL001 2011 Rev., 2011.

  • Aloui, M., Bleuzen, Y., Essefi, E. and Abbes, C., Ground vibra-tions and air blast effects induced by blasting in open pit mines: case of Metlaoui Mining Basin, south western Tunisia. J. Geol. Geophys., 2016, 5(3), 1–8.

  • Ak, H., Iphar, M., Yavuz, M. and Konuk, A., Evaluation of ground vibration effect of blasting operations in a magnesite mine. J. Soil Dyn. Earthq. Eng., 2009, 29(4), 669–676.

  • DGMS, Damage of structures due to blast induced ground vibra-tions in the mining area. Directorate General of Mines and Safety, Technical (S&T) Circular No. 7, 1997, pp. 9–12.


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  • Evaluation of ground vibrations induced by blasting in a limestone quarry

Abstract Views: 246  |  PDF Views: 123

Authors

Punit Paurush
Department of Mining Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi 221 005, India
Piyush Rai
Department of Mining Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi 221 005, India

Abstract


Despite being a versatile and low-cost method, rock blasting produces undesirable severe effects. The present study aims to examine the ground vibrations produced by blasting, which are of serious concern to mine operators as well as the nearby inhabitants. Forty-nine field-scale trial blasts were conducted and recor­ded to measure ground vibrations produced by blasting in a limestone quarry in Rajasthan, India. The multivariate linear regression (MLR) and artificial neural network (ANN) techniques were used to predict the peak particle velocity (PPV) with distance between the blasting site and measuring station, charge per delay and scaled distance as the input parameters. Subsequently, a coefficient of determination (R2) was calculated using MLR and ANN appro­aches. Additionally, to verify whether the recorded events exceeded the threshold levels, the values of PPV and dominant frequency propounded by the United States Bureau of Mines (USBM), German standard (DIN), and Director General of Mines Safety, India were carefully scrutinized. Results were compared based on R2 values obtained by the USBM predictor equation, MLR and ANN techniques. It was found that ANN provided a good prediction with a high degree of correlation (0.901) in comparison to MLR (0.754). Also, frequency analysis for the study field showed that the dominance of frequencies was in the range 10–40 Hz. Although the values were within safe limits, disturbances may be witnessed in nearby structures if PPV values are high at lower frequency range.

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DOI: https://doi.org/10.18520/cs%2Fv122%2Fi11%2F1279-1287