Enhancing Mine Subsidence Prediction and Control Methodologies for Long-Term Landscape Stability

Abstract

Prediction and control methodologies for ground deformation due to underground mining (commonly referred to as mine subsidence) provide engineers with the means to minimize negative effects on the surface. Due to the complexity of subsidence-related movements, numerous techniques exist for predicting mine subsidence behavior. This thesis focuses on the development, implementation, and validation of numerous enhanced subsidence prediction methodologies. To facilitate implementation and validation, the improved methodologies have been incorporated into the Surface Deformation Prediction System (SDPS), a computer program based primarily on the influence function method for subsidence prediction. The methodologies include dynamic subsidence prediction, alternative model calibration capability, and enhanced risk-based damage assessment. Also, the influence function method is further validated using measured case study data. In addition to discussion of previous research for each of the enhanced methodologies, a significant amount of background information on subsidence and subsidence-related topics is provided. The results of the research presented in this thesis are expected to benefit the mining industry, as well as initiate ideas for future research.