Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/xmlui/handle/100/14472
Title: Comparative study of cognitive systems for ground vibration measurements
Authors: VERMA, AK
SINGH, TN
Keywords: Peak Particle Velocity
Support Vector Machine
Artificial Neural Network
Multivariate Regression Analysis
Issue Date: 2013
Publisher: SPRINGER
Citation: NEURAL COMPUTING & APPLICATIONS, 22S341-S350
Abstract: This paper deals with the application of a support vector machine (SVM) optimization technique to predict the blast-induced ground vibration. Peak particle velocity (PPV) is an important parameter to be kept under control to minimize the damage caused due to the ground vibration. A number of previous researchers have tried to use different empirical methods to predict PPV, but these empirical equations have their limitations due to its less versatile application and acceptability from field conditions. Therefore, it is difficult to apply these empirical equations to predict PPV because they are based on limited parameters which does not really reflect and connect with real influencing parameters. In this paper, SVM technique is used for the prediction of PPV by incorporating blast design and explosive parameters, and the suitability of one technique over other has been tested based on the results. To avoid the biasness in man-made choice of parameters of SVM, we have used the chaos optimization algorithm to find the optimal parameters which can help the model to enhance the learning efficiency and capability of prediction. Datasets have been obtained from one of the large opencast mine from southeastern coalfield limited, Chhattisgarh, India. One hundred and twenty-seven datasets were used to establish SVM architecture, and 10 datasets have been randomly chosen for validation of SVM model to see its prediction potential. The results obtained have been compared with different vibration predictors, multivariate regression analysis, artificial neural network and the superiority of application on SVM over previous methodology. The mean absolute percentage error using SVM is very low (0.001) as compared to other predictors indicate its better prediction capability.
URI: http://dx.doi.org/10.1007/s00521-012-0845-1
http://dspace.library.iitb.ac.in/jspui/handle/100/14472
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