Current Science

Open Access Open Access  Restricted Access Subscription Access

An advance tool to predict ground vibration using effective blast design parameters


Affiliations
1 Coal India Limited, Northern Coalfields Limited, Singrauli 826 004, India, India
2 Mining Engineering Department, Indian Institute of Technology, Dhanbad 826 004, India, India
 

The blasting technique is mainly used for breaking the rock mass. It is also required to control blast-induced ground vibrations for the safety of nearby habitats. This study was conducted in two different mines and 56 blast vibration data were collected from overburden benches. During trial blasts, it was confirmed that the study benches had similar geology. Analysis of blasts data was done using advanced data analysis software such as MATLAB-based artificial neural network (ANN) and Waikato Environment for Knowledge analysis (WEKA) and compared with the empirical equations. The ANN prediction model gave a significantly high R2 = 0.92 with a low root mean square error (RMSE, 0.67), while WEKA gave a comparatively low R2 = 0.86 with a high RMSE (1.11)
User
Notifications
Font Size

  • Choudhary, B. S., Agrawal, A. and Arora, R., Stemming material and Inter-row delay timing effect on blast results in limestone mines. Sadhana – Acad. Proc. Eng. Sci., 2021, 46, 17–24.

  • Resende, R., Lamas, L., Lemos, J. and Calçada, R., Stress wave propagation test and numerical modelling of an underground complex. Int. J. Rock Mech. Min. Sci., 2014, 72, 26–36.

  • Yan, W. M., Tham, L. G. and Yuen, K.-V., Reliability of empirical relation on the attenuation of blast-induced vibrations. Int. J. Rock Mech. Min. Sci., 2013, 59, 160–165.

  • Khandelwal, M. and Singh, T. N., Prediction of blast-induced ground vibration using artificial neural network. Int. J. Rock Mech. Min. Sci., 2009, 46, 1214–1222.

  • Agrawal, A., Choudhary, B. S. and Murthy, V. M. S. R., Seismic energy prediction to optimize rock fragmentation: a modified approach. Int. J. Environ. Sci. Technol., 2021, 18, 1–22.

  • Khandelwal, M. and Singh, T. N., Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach. J. Sound Vib., 2006, 289, 711–725.

  • Piatetsky-Shapiro, G., KDnuggets news on SIGKDD service award, 2005; http://www.kdnuggets.com/news

  • Witten, I. H., Frank, E., Hall, M. A., Pal, C. J. and Data, M., Practical machine learning tools and techniques. In Data Mining, 2005, p. 4.

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

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

  • Rai, R. and Singh, T. N., A new predictor for ground vibration prediction and its comparison with other predictors. Indian J. Eng. Mater. Sci., 2004, 11, 178–184.

  • Elevli, B. and Arpaz, E., Evaluation of parameters affected on the blast induced ground vibration (BIGV) by using relation diagram method (RDM). Acta Montan. Slovaca, 2010, 15, 261.

  • Dowding, C. H., Blast Vibration Monitoring and Control, Prentice-Hall Inc, Englewood’s Cliffs, New Jersey, USA, 1985, pp. 288–290.

  • Choudhary, B. S. and Agrawal, A., Blast assessment to improve productivity and safety of habitats in dolomitestone quarry using artificial intelligence. In National Seminar Digitization and e-Governance in Mining Industry, Udaipur, 2019.

  • Bakhsandeh Amnieh, H., Mohammadi, A. and Mozdianfard, M., Predicting peak particle velocity by artificial neural networks and multivariate regression analysis – Sarcheshmeh copper mine, Kerman, Iran. J. Min. Environ., 2013, 4, 125–132.

  • Radson, D. and Boyd, A. H., The Pareto principle and rate analysis. Qual. Eng., 1997, 10, 223–229.

  • Duvall, W. I. and Fogelson, D. E., Review of Criteria for Estimating Damage to Residences from Blasting Vibrations. US Department of the Interior, Bureau of Mines, 1962.

  • Langefors, U. and Kihlström, B., The Modern Technique of Rock Blasting, Wiley, New York, 1963.

  • Ambraseys, N. R. and Hendron, A. J., Dynamic behavior of rock masses. In Rock Mechanics in Engineering Practice (eds Stagg, K. G. and Zienkiewicz, O. C.), John Wiley, London, 1968.

  • Standard, I., Criteria for safety and design of structures subjected to underground blast. ISI, IS-6922, 1973.


Abstract Views: 344

PDF Views: 144




  • An advance tool to predict ground vibration using effective blast design parameters

Abstract Views: 344  |  PDF Views: 144

Authors

Jai Jain
Coal India Limited, Northern Coalfields Limited, Singrauli 826 004, India, India
Anurag Agrawal
Mining Engineering Department, Indian Institute of Technology, Dhanbad 826 004, India, India
Bhanwar Singh Choudhary
Mining Engineering Department, Indian Institute of Technology, Dhanbad 826 004, India, India

Abstract


The blasting technique is mainly used for breaking the rock mass. It is also required to control blast-induced ground vibrations for the safety of nearby habitats. This study was conducted in two different mines and 56 blast vibration data were collected from overburden benches. During trial blasts, it was confirmed that the study benches had similar geology. Analysis of blasts data was done using advanced data analysis software such as MATLAB-based artificial neural network (ANN) and Waikato Environment for Knowledge analysis (WEKA) and compared with the empirical equations. The ANN prediction model gave a significantly high R2 = 0.92 with a low root mean square error (RMSE, 0.67), while WEKA gave a comparatively low R2 = 0.86 with a high RMSE (1.11)

References





DOI: https://doi.org/10.18520/cs%2Fv123%2Fi7%2F887-894