Data-based assessment of rock strengths and cuttability using the monitored parameters while drilling, tunneling, and mining

Mechanized rock excavation is a commonly used method in underground projects in mining and tunneling. The performance of mechanized rock breaking depends on the rock’s cuttability, which is traditionally analyzed through extensive field experiments, significantly affecting work efficiency. Therefore, establishing a method to quickly and accurately assess the rock mass characteristics and cuttability based on drilling, tunneling, and mining parameters is of great importance in practice. In this study, three databases were developed for drilling parameters, tunneling parameters, and mining parameters from field and experimental measurements. The hiking optimization algorithm (HOA) and Ivy algorithm (IVYA) were introduced to optimize the gradient boosting decision tree (GBDT) model. For each database, two hybrid ensemble models were developed to predict uniaxial compressive strength (UCS) and identifying cuttability levels. The training and testing dataset division ratio for all models is 4:1, with cross-validation applied to prevent overfitting. The results indicate that the developed models can be directly applied to real-time analysis of rock strength characteristics and cuttability based on drilling, tunneling, and mining parameters, facilitating the real-time adjustment of cutting equipment operation parameters and improving rock-breaking efficiency. Finally, a user-friendly graphical user interface (GUI) was developed for easy use by non-algorithm operators on site.