Rockburst hazard prediction in underground projects using two intelligent classification techniques : A comparative study

Ahmad, M. and Hu, J.-L. and Hadzima-Nyarko, M. and Ahmad, F. and Tang, X.-W. and Rahman, Z.U. and Nawaz, A. and Abrar, M. (2021) Rockburst hazard prediction in underground projects using two intelligent classification techniques : A comparative study. Symmetry, 13 (4): 632. ISSN 2073-8994

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Abstract

Rockburst is a complex phenomenon of dynamic instability in the underground excavation of rock. Owing to the complex and unclear rockburst mechanism, it is difficult to accurately predict and reasonably assess the rockburst potential. With the increasing availability of case histories from rock engineering and the advancement of data science, the data mining algorithms provide a good way to predict complex phenomena, like rockburst potential. This paper investigates the potential of J48 and random tree algorithms to predict the rockburst classification ranks using 165 cases, with four parameters, namely maximum tangential stress of surrounding rock, uniaxial compressive strength, uniaxial tensile strength, and strain energy storage index. A comparison of developed models’ performances reveals that the random tree gives more reliable predictions than J48 and other empirical models (Russenes criterion, rock brittleness coefficient criterion, and artificial neural networks). Similar comparisons with convolutional neural network resulted at par performance in modeling the rockburst hazard data.

Item Type:
Journal Article
Journal or Publication Title:
Symmetry
Uncontrolled Keywords:
Research Output Funding/no_not_funded
Subjects:
?? no - not fundedcomputer science (miscellaneous)general mathematicsphysics and astronomy (miscellaneous)chemistry (miscellaneous)mathematics(all) ??
Departments:
ID Code:
215772
Deposited By:
Deposited On:
06 Mar 2024 09:30
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Jul 2024 12:14