ABSTRACT

ABSTRACT:

The ability to predict surface subsidence associated with block caving mining is important for both environmental impact and operational hazard assessments. Use of numerical modelling provides an opportunity to enhance understanding of the surface subsidence phenomenon and develop improved methodologies for subsidence prediction. Previous numerical studies were primarily focused on site specific predictive modelling. A conceptual modelling study employing combined continuum-discrete element method is planned to investigate mechanisms governing subsidence development. As a necessary prerequisite for such a study the analysis of potential modelling approaches is conducted. In a framework of combined continuumdiscrete element method there are three approaches to rock mass representation: equivalent continuum, discrete network and mixed. Initial modelling illustrated that reasonable simulation of surface subsidence development can be achieved using equivalent continuum and mixed approaches.

1 INTRODUCTION

Block caving mining is characterized by caving and extraction of a massive volume of ore which translates into a formation of major surface depression or subsidence zone directly above and in the vicinity of the mining operations (see Fig. 1). The block caving induced subsidence may endanger mine infrastructure and is a major concern for operational safety. Moreover, changes to surface landforms brought about by block caving subsidence may have certain environmental impacts. Therefore the ability to predict surface subsidence associated with block caving mining is critical for both mining operational hazard and environmental impact assessments. Owing to problems of scale and lack of access a fundamental understanding of the complex rock mass response in block caving settings remains limited. Block caving geomechanics is still largely an empirically based discipline. Use of numerical modelling provides an opportunity to investigate the factors governing caving mechanisms and develop improved methodologies for the prediction of associated surface subsidence.

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