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Application of an Expert System to Predict Maximum Explosive Charge Used Per Delay in Surface Mining
Ist Teil von
Rock mechanics and rock engineering, 2013-11, Vol.46 (6), p.1551-1558
Ort / Verlag
Vienna: Springer Vienna
Erscheinungsjahr
2013
Link zum Volltext
Quelle
Springer Nature - Complete Springer Journals
Beschreibungen/Notizen
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.