Enhancing Mine Subsidence Prediction and Control Methodologies

During the last 25 years, technological advancement and subsidence research have resulted in more accurate and diverse prediction capabilities. The work presented in this paper focuses on the development of integrated prediction capabilities for dynamic deformation indices and for strains over sloping terrain. In order to assist subsidence engineers with accurate prediction methodologies, the research also addresses the development and validation of techniques that can enable model calibration using alternative measured subsidence parameters such as horizontal or ground strain instead of the traditional vertical subsidence values. This paper presents the basic concepts and validation of the above mentioned enhanced prediction and control methodologies, which are necessary for effective assessment and control of mining-induced subsidence, using examples and case studies. In addition, a risk assessment approach for evaluating landscape stability over mined areas is presented. The enhanced prediction methodologies have been incorporated into the Surface Deformation Prediction Software (SDPS) package.