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Materials 2016, 9(7), 531; doi:10.3390/ma9070531

Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers

1
Mathematics Department, Universidad de Oviedo, Oviedo 33007, Spain
2
Department of Mining Technology, Topography and Structures, University of León, León 24071, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Jorge de Brito
Received: 13 March 2016 / Revised: 7 June 2016 / Accepted: 24 June 2016 / Published: 29 June 2016
(This article belongs to the Section Structure Analysis and Characterization)
View Full-Text   |   Download PDF [805 KB, uploaded 29 June 2016]   |  

Abstract

The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine. View Full-Text
Keywords: hard-rock stability; span design graph; entry-type excavations; support vector machine; extreme learning machine hard-rock stability; span design graph; entry-type excavations; support vector machine; extreme learning machine
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

García-Gonzalo, E.; Fernández-Muñiz, Z.; García Nieto, P.J.; Bernardo Sánchez, A.; Menéndez Fernández, M. Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers. Materials 2016, 9, 531.

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