Abstract
Blasting is a mechanism of rock fragmentation. Usages of high explosive causes ground vibration, air noise, back breaks, over breaks as well as fly rock. Excessive blast-induced vibrations cause severe damage to nearby structures and residents in and around mine. So, it is essential to the precise prediction of ground vibration to reduce the ecological damage. Vibrations are expressed in terms of peak particle velocity (PPV). The aim of this study was to assess and predict the ground vibration at different explosive quantities and distances, applications of generalized regression neural network (GRNN) as well as empirical predictors were used. A total of fourteen blast events were collected at various strategic and valuable locations in the site, out of these ten blast events were considered for training and reaming for validate of the GRNN model. A three-layer GRNN with 2-14-1 architecture was developed and trained with Levenberg–Marquardt algorithm. Five emphircal predictor equations were proposed by the United States Bureau of Mines (USBM), Ambraseys-Hendron, Langefors- Kihlstrom, Central Mining Research Institute (CMRI) predictor and Bureau of Indian standard were applied to governing a relation between peak particle velocity and its influencing parameters. The obtained results reveal that the proposed GRNN model can predict the PPV accurately as compared to the other predictor models available. Obtained results were compared based on evaluation performance models such as coefficient of determination (R2) and mean square error (MSE) between monitored and predicted values of PPV. It was observed that GRNN approach provides high R2 (0.9988) and low MSE (0.0001) among all other empirical predictor approaches for accurate prediction of ground vibration.
