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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, br. 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 i Muhammad Abrar. "Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study." Symmetry (Basel) 13, br. 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, i sur.. Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study. Symmetry (Basel) [Internet]. 2021. [pristupljeno 14.01.2025.];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, br. 4, 2021. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:133:243155. [Citirano: 14.01.2025.]

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Naslov (engleski)Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study
AutorMahmood Ahmad
AutorJi-Lei Hu
AutorMarijana Hadzima-Nyarko
AutorFeezan Ahmad
AutorXiao-Wei Tang
AutorZia Ur Rahman
AutorAhsan Nawaz
AutorMuhammad Abrar
Autorova ustanovaSveučilište Josipa Jurja Strossmayera u Osijeku
Građevinski i arhitektonski fakultet Osijek
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Građevinarstvo
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaSymmetry (Basel)
Brojčani podacivol. 13, br. 4, 632
e-ISSN2073-8994
DOIhttps://doi.org/10.3390/sym13040632
URN:NBNurn:nbn:hr:133:243155
Datum objave publikacije2021
URL dokumentahttps://www.mdpi.com/2073-8994/13/4/632
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2024-12-04 11:41:16