Аңдатпа
A quantitative assessment of Fall of Ground (FoG) hazards in underground excavations is essential in mining since it can assist engineers in implementing reliable ground control measures. FoG occurs within a wide range of conditions in the form of roof fall, rib spilling, and fallouts due to the combined actions of stress concentrations and structural failures. In this study, the FoG index, a dimensionless index, is introduced to quantify the FoG hazard and its influencing factors using the Rock Engineering Systems (RES) and Artificial Neural Network (ANN) approaches. The RES is applied as a practical tool for determining complex and highly nonlinear correlation among the input parameters via an interaction matrix, while ANN is implemented to objectively assign weights to the input parameters of the FoG index. The East Sary-Oba mine, located in Kazakhstan, is used as a case study. The mine, characterized by complex geological formations including faults and shear zones, faces persistent FoG hazards that challenge existing risk mitigation strategies. A FoG database with over 150 documented FoG cases, consisting of parameters such as the rock mass parameters, the excavation geometry, the excavation supports, the mining methods, failure mode, and the FoG size, was employed to construct the RES interaction matrix and to derive the FoG index. Overall, the validation results showed excellent agreement between the FoG index and the field observations. It was concluded that the results of this study could be used to elaborate guidelines for FoG hazard assessment in underground mines.