Naslov
Rockburst Hazard Prediction in Underground Projects Using Two Intelligent Classification Techniques: A Comparative Study

Autori
Ahmad, Mahmood ; Hu, Ji-Lei ; Hadzima-Nyarko, Marijana ; Ahmad, Feezan ; Tang, Xiao-Wei ; Rahman, Zia Ur ; Nawaz, Ahsan ; Abrar, Muhammad

Izvornik
Symmetry (2073-8994) 13 (2021), 4; 632, 18

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
rockburst hazard prediction ; risk assessment ; random tree ; J48 algorithm ; machine learning

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo

POVEZANOST RADA