Ground Support Supplement Article by Robyn Teet, Resolve Mining Solutions

The following is an article from Robyn Teet which appeared in the Ground Support 2023 Supplement

What are we designing for? Understanding risk scenarios for targeted data collection, enabling confident ground support design

By Robyn Teet, Resolve Mining Solutions

The mining industry can be complex and high risk, and as many recent papers, presentations and articles have discussed, as mining is forced deeper with ‘easy-to-mine’ resources dwindling, the complexity of these projects and risk profile grows. Examples of depths for both open pit and underground operations can be seen in Figures 1 and 2 with many operations exceeding 1 km in depth. The aim of this article is to prompt discussion and thought to echo some of the recent conversations at seminars, conferences and online events that the author has observed and been involved with.
The consequence of failure increases significantly with depth as the number of levers available for remediation and recovery reduce. To achieve similar performance outcomes when having fewer levers for recovery available, the likelihood of unforeseen circumstances that typically lead to the underperformance or early closures has to be reduced.
This change brings with it a key challenge that mining companies globally need to ask – are the methods that we use to design data-collection programs, select mining methods, design ground support, develop schedules and costs etc. still relevant? Are we reliant on empirical methods or benchmarking developed for very different conditions and what are the limitations on those empirical methods? How has the risk profile changed and what does this mean to the business? Do we understand the deposit characteristics and variability?
In the context of ground support, which is the theme of this Australian Centre for Geomechanics conference, the intention is to provide sufficient capacity to support the ground and control the risk of both static and dynamic loads. These loads are based on two fundamental components – dead weight and seismic potential.

Figure 1. Examples of dep mines around the world (Nguyen & Pham 2019)

A lot of focus is placed on the dead-weight component, for which, in its most simplistic form, we need to understand the density of the rock and the structures that define the blocks. With this information, typically, a best, worst and most-likely scenario for blocks (wedges) can be built and ground support design is established based on these three scenarios, resulting in dead-weight loads and geometries. Repeating this by the number of major lithological units or geotechnical domains will yield the ground support design for the project. This approach assumes that the variability within the geotechnical domains is very minor and can be grouped into the three overarching categories. However, a small degree of variability can have a large impact on performance at depth. The question that needs to be answered is, ‘How variable are the domains within the deposit?’ Traditionally, this gets included as another risk scenario in the project risk assessments which many would have seen – unforeseen ground conditions. But the question to the business should be, ‘Are the ground conditions truly unforeseen or are they simply based on a limitation in scope and understanding driven by inadequate data collection programs?’

Figure 2. Historical evolution of open pit mines' depth (Parra et al. 2017)

There is a trade-off between data-collection methods and cost which varies with project stage (and mining depth) – the expectation being that as projects advance from scoping study through to feasibility stage, the amount of data has increased, and with it, confidence in the results of the analyses (Figure 3). There have been several publications detailing this with one widely used being produced from the Large Open Pit Project (Read & Stacey 2009). There have also been recent discussions around a Joint Ore Reserve Committee-style reporting framework for geotechnical data to provide a framework for this.
All these kinds of frameworks rely on understanding the deposit and its life story. How did the deposit form? Is it hydrothermal? If it is, it’s likely to have weak weathered structures. Or is it a porphyry? Is there a likely strength contrast between the host rock and the porphyry intrusion with structures related to emplacement, overprinting the host structures. What’s happened since the deposit formed? Has it been in a tectonically active area and developed additional structures and faulting? Has it seen uplift and erosion which can introduce weathering horizons? Has there been any late-stage mineralisation which has locally altered properties of defects and the rock mass?

While this is an oversimplification, it highlights that without the fundamental understanding of the history of the deposit it is almost impossible to be able to understand the risk scenarios and plan subsequent data-collection programs. It also shows that the assumption that data-collection programs are the same for similar types of studies is inherently fraught. It is quite common that much more is understood about deposit formation at an earlier stage than when the project studies are first carried out. This means there is a wealth of information available that can help inform the risk scenario and appropriate data-collection programs before studies commence.

Figure 3. Expansion of data collection with complexity for dead-weight ground support calculations

The feedback loop into operations is the final step in adding data and verifying assumptions made during the design stages and can often become lost as operational responsibilities often leave geotechnical teams managing the day-to-day challenges with little to no time to feed back into the dataset. Monitoring forms a key part of this feedback for ongoing planning, but the monitoring system design must be focused on proving (or disproving) a risk scenario. It is not a pessimistic approach but one defined by risk with effective controls put in place to mitigate the scenario consequences.
By moving the levers from the reactionary space of dealing with issues at the operational level when they become apparent to the proactive space of managing risk based on project-specific parameters, associated investment is shifted from OPEX to CAPEX, limiting the potential for large-scale deviations from plans and increasing the likelihood of delivering on plan and to budget.


Nguyen NM & Pham, DT 2019, ‘Tendencies of mining technology development in relation to deep mines’, Mining Science and Technology, vol. 4, no. 1, pp.16–22.
Parra, A, Morales Varela, N, Vallejos, J & Nguyen, PMV 2017, ‘Open pit mine planning considering geomechanical fundamentals’, International Journal of Mining, Reclamation and Environment, vol. 32, no. 4, pp. 221–238.
Read, J & Stacey, P 2009, Guidelines for Open Pit Slope Design, CSIRO Publishing, Melbourne.

R Teet, Resolve Mining Solutions

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