A model for cave propagation and subsidence assessment in jointed rock masses

Abstract

Cave mining methods allow for the bulk extraction of large, low grade orebodies in a cost effective manner. The fundamental mechanics of caving involves the self-propagating yield (failure) of an in situ rock mass in response to production draw from a mining horizon located at depth. Since the inception of large-scale mechanised cave mining methods in the iron ore mines of northern Michigan, USA, during the early part of the 20th century, researchers have sought to understand and predict the nature of cave propagation through simple one-dimensional volume based relationships and empirical methods. Although historically these methods have successfully been applied to many cave operations, numerical modelling is considered to be able to provide a more fundamental, rigorous and robust assessment of cave propagation behaviour now and in the future. A numerical model for cave propagation and subsidence assessment has been developed based on fundamental rock mass behaviour and the development of numerical modelling techniques. Unlike most existing techniques, the cave volume is not introduced manually into the model; rather it is allowed to develop based on the specified mass-based production schedule, evolving stress conditions and the simulated constitutive behaviour of the rock mass. In doing so, hang-ups, over-breaks and rapid advance rates can all be predicted. The resulting numerical model is able to accurately capture rock mass strength and deformation modulus anisotropy and scale effects as well as the effect of large-scale discontinuities on cave propagation behaviour. In addition, the strain-softening and bulking behaviour during the complex process of caving induced yield and mobilisation is also considered. A production draw algorithm has been developed that accurately reflects the mass withdrawn and drawpoint production variability for all cave mining methods; block, panel and sub-level caving. This algorithm is complemented by an algorithm that updates the evolving ground surface profile to reflect the development of a crater. The methodology has been applied to four large-scale case study back-analyses that provide validation of the numerical techniques and assessment criteria.