- Title
- Application of an expert system for the assessment of blast vibration
- Creator
- Khandelwal, Manoj
- Date
- 2012
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160791
- Identifier
- vital:12275
- Identifier
-
https://doi.org/10.1007/s10706-011-9463-4
- Identifier
- ISBN:0960-3182
- Abstract
- The purpose of this article is to evaluate and predict the blast induced ground vibration using different conventional vibration predictors and artificial neural network (ANN) at a surface coal mine of India. Ground Vibration is a seismic wave that spread out from the blast hole when detonated in a confined manner. 128 blast vibrations were recorded and monitored in and around the surface coal mine at different strategic and vulnerable locations. Among these, 103 blast vibrations data sets were used for the training of the ANN network as well as to determine site constants of various conventional vibration predictors, whereas rest 25 blast vibration data sets were used for the validation and comparison by ANN and empirical formulas. Two types of ANN model based on two parameters (maximum charge per delay and distance between blast face to monitoring point) and multiple parameters (burden, spacing, charge length, maximum charge per delay and distance between blast face to monitoring point) were used in the present study to predict the peak particle velocity. Finally, it is found that the ANN model based on multiple input parameters have better prediction capability over two input parameters ANN model and conventional vibration predictors. © 2011 Springer Science+Business Media B.V.
- Publisher
- Springer
- Relation
- Geotechnical and Geological Engineering Vol. 30, no. 1 (2012), p. 205-217
- Rights
- Copyright © 2011 Springer Science+Business Media B.V.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0905 Civil Engineering; Artificial neural network; Back propagation; Peak particle velocity; Surface mine; Vibration predictors
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