A dimensional analysis approach to study blast-induced ground vibration
- Authors: Khandelwal, Manoj , Saadat, Mahdi
- Date: 2014
- Type: Text , Journal article
- Relation: Rock Mechanics and Rock Engineering Vol. 48, no. 2 (2014), p. 727-735
- Full Text: false
- Reviewed:
- Description: The prediction of ground vibration is of great importance in the alleviation of the detrimental effects of blasting. Therefore, a vibration control study to minimize the harm of ground vibration and its influence on nearby structures can play an important role in the mining industry. In this paper, a dimensional analysis (DA) technique has been performed on various blast design parameters to propose a new formula for the prediction of the peak particle velocity (PPV). After obtaining the DA formula, 105 data sets were used to determine the unknown coefficients of the DA equation, as well as site constants of different conventional predictor equations. Then, 12 new blast data sets were used to compare the capability of the DA formula with conventional predictor equations. The results were compared based on the coefficient of determination and mean absolute error between measured and predicted values of the PPV. © 2014, Springer-Verlag Wien.
Application of an expert system to predict maximum explosive charge used per delay in surface mining
- Authors: Khandelwal, Manoj , Singh, Trilok
- Date: 2013
- Type: Text , Journal article
- Relation: Rock Mechanics and Rock Engineering Vol. 46, no. 6 (2013), p. 1551-1558
- Full Text: false
- Reviewed:
- Description: The present paper mainly deals with the prediction of maximum explosive charge used per delay (Q MAX) using an artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). One hundred and fifty blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. One hundred and twenty-four blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. The other 26 new randomly selected data sets were used to test, evaluate and compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R), mean absolute error and mean squared between measured and predicted values of Q MAX. It was found that coefficient of correlation between measured and predicted Q MAX by ANN was 0.985, whereas it ranged from 0.316 to 0.762 by different conventional predictor equations. Mean absolute error and mean squared error was also very small by ANN, whereas it was very high for different conventional predictor equations. © 2013 Springer-Verlag Wien.