Innovative use of SMART cable bolt data through numerical back analysis for interpretation of postfailure rock mass properties




A significant component of underground mining costs stems from ground support in the form of rock bolts, mesh and screen, cable bolts, and shotcrete. Should any of this support approach or reach failure (stripped or broken cable bolts, severe bagging of screen, large numbers of broken rock bolts, etc.), the support needs to be rehabilitated. This rehabilitation is often much more expensive than the initial support installation, due not just to the cost of the support, but to loss of access, downtime of that area of the mine, potential for injuries, and possible lost mining revenues. Many mines use instrumentation in critical infrastructure, such as haulage drifts, crusher stations, intersections, etc., to monitor the displacement of the backs and/or walls, support loads, etc. Data is generally collected by hand from time to time and entered into a database, but is analyzed only if there is a problem (i.e. reactive engineering). Most mines today also utilize numerical modelling. These models, normally linear-elastic, are often not up to date, and modelling is often done only in reaction to a problem.
In certain cases, modelling of the underground mining environment requires software packages that employ non-linear constitutive models (i.e. models that allow for failure to occur). Linear-elastic models unrealistically allow for stresses to build up far beyond those at failure and, hence, do not redistribute stresses away from failed zones. The challenge with non-linear models is that they require the input of generally unknown post-peak material properties and behaviour. Models must also be three dimensional in order to capture the full state of mining-induced stress conditions due to the complex mine geometry.
Research at the University of Toronto has developed a technique to link between the three-dimensional, linear-elastic, mining-induced stress problem and two-dimensional, non-linear modelling that accounts for the three-dimensional stresses from mine-wide models, and allows for failure to occur. This new modelling technique has been used to explore the rock mass post-peak properties through back analyses at a case study mine. The rock mass ‘post-peak’ parameters have been calibrated using SMART cable bolt data of displacements in mining haulage drives. The key result from back analyses demonstrates that for two areas of the case study mine (separated by almost 500 m in depth, but found in the same rock unit), the post-peak generalized Hoek-Brown value of mr must be reduced significantly to simulate displacements observed in instrumentation data. Failure patterns predicted by the models using the calibrated parameters have also been verified using the spatial distribution of observed microseismic events over the same time spans.
High-quality instrumentation data is the key to postpeak rock mass parameter calibration. The procedures outlined in the paper highlight the need for a new hybrid numerical modelling package that can use the  influence of three-dimensional, mining-induced stresses to predict displacements in, and potential failure of, rock masses surrounding mining infrastructure, in order to radically alter strategic and tactical mine design.
With high-quality estimates of field-scale rock mass post-peak characteristics and stress-strain behaviour, improved confidence in explicit forward modelling of ground support can be gained. This will ultimately aid in the design and optimization of rock mass support and minimize rehabilitation, hence significantly reducing mining costs. Reliable determination of ‘post-peak’ rock mass properties, using techniques described in this paper, will improve reliability in underground mine design, as well as increase safety for mining personnel. Having high-quality and reliable instrumentation that is routinely monitored and analyzed is critical to being able to perform the work described here. Research is currently heading towards real-time wireless monitoring of instruments, which can then also be fed into models in realtime.
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