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Risk assessment of failure of rock bolts in underground coal mines using support vector machines
Author(s) -
Jiang Peng,
Craig Peter,
Crosky Alan,
Maghrebi Mojtaba,
Canbulat Ismet,
Saydam Serkan
Publication year - 2017
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2273
Subject(s) - coal mining , support vector machine , feature selection , feature (linguistics) , underground mining (soft rock) , mining engineering , computer science , transformation (genetics) , cracking , engineering , data mining , coal , machine learning , linguistics , philosophy , biochemistry , chemistry , gene , waste management
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.