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Risk assessment of failure of rock bolts in underground coal mines using support vector machines

Risk assessment of failure of rock bolts in underground coal mines using support vector machines In recent years, there has been an increasing incidence of failure of rock bolts due to stress corrosion cracking and localized corrosion attack in Australian underground coal mines. Unfortunately, prediction of the risk of failure from results obtained from laboratory testing is not necessarily reliable because it is difficult to properly simulate the mine environment. An alternative way of predicting failure is to apply machine learning methods to data obtained from underground mines. In this paper, support vector machines are built to predict failure of bolts in complex mine environments. Feature transformation and feature selection methods are applied to extract useful information from the original data. A dataset, which had continuous features and spatial data, was used to test the proposed model. The results showed that principal component analysis‐based feature transformation provides reliable risk prediction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Risk assessment of failure of rock bolts in underground coal mines using support vector machines

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References (25)

Publisher
Wiley
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.2273
Publisher site
See Article on Publisher Site

Abstract

In recent years, there has been an increasing incidence of failure of rock bolts due to stress corrosion cracking and localized corrosion attack in Australian underground coal mines. Unfortunately, prediction of the risk of failure from results obtained from laboratory testing is not necessarily reliable because it is difficult to properly simulate the mine environment. An alternative way of predicting failure is to apply machine learning methods to data obtained from underground mines. In this paper, support vector machines are built to predict failure of bolts in complex mine environments. Feature transformation and feature selection methods are applied to extract useful information from the original data. A dataset, which had continuous features and spatial data, was used to test the proposed model. The results showed that principal component analysis‐based feature transformation provides reliable risk prediction.

Journal

Applied Stochastic Models in Business and IndustryWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

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