Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/10219
Title: Classification of Stability of Highwall During Highwall Mining: A Statistical Adaptive Learning Approach
Authors: Ram Chandar, K.
Hegde, C.
Yellishetty, M.
Gowtham, Kumar, B.
Issue Date: 2015
Citation: Geotechnical and Geological Engineering, 2015, Vol.33, 3, pp.511-521
Abstract: The depleting coal deposits day by day required the introduction of novel methods of mining like highwall mining. Highwall mining is a method of extraction of coal blocked in the highwall. The method involves considerable challenges in the area of roof control and most importantly the stability of the highwall itself. Highwall mining has gained considerable importance all over the world, owing to the fact that the coal otherwise would not be extracted forever. This paper aims to assess the influence of varying conditions which can affect the stability of the highwall during highwall mining. The effect of gallery length, width of pillar and number of galleries are systematically studied through field investigations where a highwall mining was adopted first time in India. Initially, assessment was carried out using a numerical modelling approach and then the stability of the highwall is classified using multilinear regression, logistic regression and naive Bayes classifier. This will provide a mechanism to predict the stability of the highwall in future cases of similar conditions. The classification is done using statistical adaptive learning methods and a comparison of the methods is done. 2014, Springer International Publishing Switzerland.
URI: https://idr.nitk.ac.in/jspui/handle/123456789/10219
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.