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

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