Title page for ETD etd-06272008-153059

Type of Document Master's Thesis
Author Andrews, Kevin
Author's Email Address kandrews@vt.edu
URN etd-06272008-153059
Title Enhancing Mine Subsidence Prediction and Control Methodologies for Long-Term Landscape Stability
Degree Master of Science
Department Mining and Minerals Engineering
Advisory Committee
Advisor Name Title
Karmis, Michael E. Committee Chair
Agioutantis, Zacharias Committee Member
Karfakis, Mario G. Committee Member
Westman, Erik Christian Committee Member
  • subsidence prediction
  • risk-based damage analysis
  • long-term stability
  • dynamic subsidence
  • ground strain
  • ground deformation
  • subsidence
  • mine subsidence
Date of Defense 2008-06-23
Availability unrestricted
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.

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