- Title
- Application of soft computing to predict blast-induced ground vibration
- Creator
- Khandelwal, Manoj; Kumar, Lalit; Yellishetty, Mohan
- Date
- 2011
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/161723
- Identifier
- vital:12567
- Identifier
-
https://doi.org/10.1007/s00366-009-0157-y
- Identifier
- ISBN:0177-0667
- Abstract
- In this study, an attempt has been made to evaluate and predict the blast-induced ground vibration by incorporating explosive charge per delay and distance from the blast face to the monitoring point using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 2-5-1 architecture was trained and tested using 130 experimental and monitored blast records from the surface coal mines of Singareni Collieries Company Limited, Kothagudem, Andhra Pradesh, India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) by ANN and conventional vibration predictors. Results were compared based on coefficient of determination and mean absolute error between monitored and predicted values of PPV. © 2009 Springer-Verlag London Limited.
- Publisher
- Springer-Verlag London Limited
- Relation
- Engineering with Computers Vol. 27, no. 2 (2011), p. 117-125
- Rights
- Copyright © 2009 Springer-Verlag London Limited.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0102 Applied Mathematics; 0801 Artificial Intelligence and Image Processing; 0802 Computation Theory and Mathematics; Artificial neural network; Back-propagation; Blast vibration; Conventional vibration predictors; PPV
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