prikaz prve stranice dokumenta Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study
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Scientific paper - Original scientific paper
Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study
Symmetry (Basel), 13 (2021), 4; 632. https://doi.org/10.3390/sym13040632
Ahmad, Mahmood; Hu, Ji-Lei; Hadzima-Nyarko, Marijana; Ahmad, Feezan; Tang, Xiao-Wei; Rahman, Zia Ur; Nawaz, Ahsan; Abrar, Muhammad

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Ahmad, M., Hu, J. L., Hadzima Nyarko, M., Ahmad, F., Tang, X. W., Rahman, Z. U. ... Abrar, M. (2021). Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study. Symmetry (Basel), 13. (4). doi: 10.3390/sym13040632

Ahmad, Mahmood, et al. "Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study." Symmetry (Basel), vol. 13, no. 4, 2021. https://doi.org/10.3390/sym13040632

Ahmad, Mahmood, Ji Lei Hu, Marijana Hadzima Nyarko, Feezan Ahmad, Xiao Wei Tang, Zia Ur Rahman, Ahsan Nawaz and Muhammad Abrar. "Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study." Symmetry (Basel) 13, no. 4 (2021). https://doi.org/10.3390/sym13040632

Ahmad, M., et al. (2021) 'Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study', Symmetry (Basel), 13(4). doi: 10.3390/sym13040632

Ahmad M, Hu JL, Hadzima Nyarko M, Ahmad F, Tang XW, Rahman ZU, and sur.. Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study. Symmetry (Basel) [Internet]. 2021 [cited 2025 February 01];13(4). doi: 10.3390/sym13040632

M. Ahmad, et al., "Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study", Symmetry (Basel), vol. 13, no. 4, 2021. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:133:243155. [Accessed: 01 February 2025]

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Metadata
Title (english)Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study
AuthorMahmood Ahmad
AuthorJi-Lei Hu
AuthorMarijana Hadzima-Nyarko
AuthorFeezan Ahmad
AuthorXiao-Wei Tang
AuthorZia Ur Rahman
AuthorAhsan Nawaz
AuthorMuhammad Abrar
Author's institutionJosip Juraj Strossmayer University of Osijek
Faculty of Civil Engineering and Architecture Osijek
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Abstract (english)
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.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleSymmetry (Basel)
Numberingvol. 13, no. 4, 632
e-ISSN2073-8994
DOIhttps://doi.org/10.3390/sym13040632
URN:NBNurn:nbn:hr:133:243155
Publication2021
Document URLhttps://www.mdpi.com/2073-8994/13/4/632
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution 4.0 International (CC BY 4.0) icon
Created on2024-12-04 11:41:16