- Title
- Application of an expert system to predict maximum explosive charge used per delay in surface mining
- Creator
- Khandelwal, Manoj; Singh, Trilok
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160759
- Identifier
- vital:12276
- Identifier
-
https://doi.org/10.1007/s00603-013-0368-9
- Identifier
- ISBN:0723-2632
- Abstract
- 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.
- Publisher
- Springer
- Relation
- Rock Mechanics and Rock Engineering Vol. 46, no. 6 (2013), p. 1551-1558
- Rights
- Copyright © 2013 Springer-Verlag Wien.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0905 Civil Engineering; 0914 Resources Engineering and Extractive Metallurgy; Artificial neural network; Conventional vibration predictors; Distance between blast face to monitoring point; Peak particle velocity; Safe explosive charge used per delay
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