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

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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)

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