Risk assessment of failure of rock bolts in underground coal mines using support vector machines. (15th August 2017)

• Record Type: Journal Article
• Authors: Jiang, Peng; Craig, Peter; Crosky, Alan; Maghrebi, Mojtaba; Canbulat, Ismet; Saydam, Serkan
• Other Names: Nicolis Orietta guestEditor.; Pascual Rodrigo guestEditor.
• Is Part Of: Applied stochastic models in business and industry. Volume 34:Number 3(2018)
• Page Start: 293
• Page End: 304
• Publication Date: 2017-08-15
• Subjects: feature learning -- rock bolt -- stress corrosion cracking -- support vector machine -- underground mining; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2273
• Languages: English
• ISSNs: 1524-1904

Abstract

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.