Prediction of blast-induced ground vibration using multi-variate regression analysis in an opencast mine


  • IIT (BHU), Department of Mining Engineering, Varanasi, 221005, India


The consumption of hydrocarbon is increasing day by day. A number of technologies are being used to meet out the demanded quality. The drilling and blasting is a cheapest way to exploration and excavation in mining industries. The blasting creates excessive amount of energy in different form of ground vibration as shaking of Earth, flyrock, removal and transportation of overburden rockmass and other noise. Blast-induced ground vibration has some adverse effects on surrounding environment as well as community living nearby the opencast mine. The study was conducted at opencast coal mine in Chhattisgarh. A total number of 32 data sets have been measured with different parameters such as; maximum charge per delay (MCPD), observation distance, charge length, spacing, burden, blast hole depth, hole diameter, etc. as well as peak particle velocity (PPV), frequency and peak vector sum (PVS). In present study, main focus on measurement and prediction of peak particle velocity by different predictor model (USBM, Indian Standard, DGMS) and multi-variate statistical regression analysis (MVSRA). Simple linear regression model (SLRM) is used to determine the site characteristics constants. The constants are used to establish new prediction model equations among different parameters. Finally, assess the blast induced ground vibration on the basis of measured and predicted peak particle velocity.


Blasting, ground vibration, PPV, maximum charge per delay, MVSRA

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