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Prediction of blast-induced ground vibration using artificial neural network

DSpace at IIT Bombay

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Title Prediction of blast-induced ground vibration using artificial neural network
 
Creator KHANDELWAL, M
SINGH, TN
 
Subject blast vibration
ppv
frequency
artificial neural network
back propagation
multivariate regression analysis
conventional predictors
 
Description An attempt has been made to evaluate and predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the artificial neural network (ANN) technique. A three-layer ,feed-forward back-propagation neural network having 15 hidden neurons, 10 input parameters and two output parameters were trained using 154 experimental and monitored blast records from one of the major producing surface coal mines in India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) and frequency by ANN and other predictors. To develop more confidence in the proposed method, same data sets have also been used for the prediction of PPV by commonly used vibration predictors as well as by multivariate regression analysis (MVRA). Results were compared based on correlation and mean absolute error (MAE) between monitored and predicted values of PPV and frequency. (C) 2009
 
Publisher PERGAMON-ELSEVIER SCIENCE LTD
 
Date 2011-08-26T04:16:42Z
2011-12-26T12:57:16Z
2011-12-27T05:47:17Z
2011-08-26T04:16:42Z
2011-12-26T12:57:16Z
2011-12-27T05:47:17Z
2009
 
Type Article
 
Identifier INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES, 46(7), 1214-1222
1365-1609
http://dx.doi.org/10.1016/j.ijrmms.2009.03.004
http://dspace.library.iitb.ac.in/xmlui/handle/10054/11188
http://hdl.handle.net/10054/11188
 
Language en