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By M W. N Buxton, M S. Shishvan, J Benndorf

The flow of information, and consequently the decision-making along the chain of mining from exploration to beneficiation, typically occurs in a discontinuous fashion over long timespans. In addition, due to the uncertain nature of the knowledge about deposits and the inherent spatial distribution of material characteristics, actual production performance in terms of produced ore grades and quantity and extraction process efficiency often deviate from expectations. Reconciliation exercises to adjust mineral reserve models and planning assumptions are performed with timely lags of weeks, months or even years.With the development of modern information and communication technology over the last decade, a flood of data about different aspects of the production process is available in a real-time manner. For example, sensor technology enables online characterisation of geochemical, mineralogical and physical material characteristics on conveyor belts or at working faces. The ability to utilise this additional information and feed it back into reserve block models and planning assumptions opens up new opportunities to continuously control the decisions made in production planning to increase resource recovery and process efficiency. This leads to a change in paradigm from a discontinuous method to near real-time reserve reconciliation and model updating, which calls for suitable modelling and optimisation methodologies to quantify prior knowledge in the reserve model, process and integrate information from different sensor sources, back propagate the gain in information into reserve models and efficiently optimise operational decisions in real-time.This paper introduces the concept of an integrated closed-loop framework for real-time reserve management, incorporating sensor-based material characterisation, geostatistical modelling under uncertainty, modern data assimilation methods for sequential model updating and mining system simulation and optimisation. The effectiveness of the framework and the value added will be demonstrated in an illustrative case study.CITATION:Benndorf, J, Buxton, M W N and Shishvan, M S, 2014. Sensor-based real-time resource model reconciliation for improved mine production control – a conceptual framework, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 223–234 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By C A. Myburgh, S Craig, K Deb

In focusing on open pit mine scheduling, one of the biggest areas that a mining engineer can add value is in the area of cut-off grade optimisation. It is not atypical to add up to an extra 20 per cent in value by bringing forward higher-grade ore and deferring costs into the future.There are many techniques available to maximise net present value (NPV) when applied to open pit mine scheduling. That in itself raises the question of why so many of these techniques exist. The short answer is that none of these techniques are robust. Every time the problem changes, the algorithms also have to change.Instead of trying to make a problem fit the constraints of an existing technique, there is much to be said for usefully hybridising different approaches. This paper describes an innovative approach where evolutionary algorithms are combined with both local search and linear programming. The goal is maximising NPV through optimisation of cut-off grade policy and extraction sequence for an open pit mine. The cut-off/cut-over grade policy produced caters for multiple processing streams and stockpiles.Finally we demonstrate the quality of the algorithm by comparing some results with a well-known mathematical solution.CITATION:Myburgh, C A, Deb, K and Craig, S, 2014. Applying modern heuristics to maximising net present value through cut-off grade optimisation, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 155–164 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By D Kentwell

A resource model serves two main purposes depending on the stage of a project. The first is to provide an early indication of likely tonnages and grades at certain cut-offs so that decisions can be made on whether continued spending on the project is justified. The second is to provide a basis for mine planning, feasibility studies and reserves. Resource models are not designed for selection of actual blocks in the ground for processing. This is the purpose of a grade control model, which is created at a stage beyond both resources and reserves.Inferred, Indicated and Measured Resources contain different levels of uncertainty with respect to the accuracy of the resulting grade and tonnage curves. These uncertainties arise from three broad areas: quality of information, geological assumptions and estimation technique. This paper focuses on known problems with the ordinary kriging (OK) estimation technique. Resource estimates often produce smoothed block grade distributions with much lower block grade variability than true selective mining unit (SMU) block grade distributions. There are two main factors that are involved in this smoothing. Firstly, where the block size used for estimation is larger than the SMU block size, this will smooth the block grade distribution and move the grade and tonnage curves away from the true SMU block grade and tonnage curves. Secondly, and less well recognised, is that smoothing is inherent in the OK estimator. This also moves the grade and tonnage curves away from the true SMU grade and tonnage curves, even when the estimate is done at the SMU block size. There are a number of techniques, such as simulation, uniform conditioning and degraded search neighbourhoods that can provide a better approximation of the true SMU block grade and tonnage curves at the expense of individual block accuracy.This paper aims to explore the extent to which OK resource estimates designed to maximise individual block accuracy can differ from (an estimate of) the true resource at SMU block size in terms of grade and tonnage curves. The differences at economic cut-offs and above are examined. In some cases these differences are significant. Both the OK estimation technique and block size play a part in the differences observed. To do this we will examine a number of commodities and deposit types using real project data.The conclusion is that the current industry standard default practice of targeting maximum individual block accuracy (conditional unbiasedness) using OK at resource stage, without consideration or evaluation of the extent of associated smoothing at SMU scale, can lead to significant errors in fundamental mine planning decisions. This then has implications for resource classification if the classifications are based heavily on the OK quality without consideration of the true SMU distribution.CITATION:Kentwell, D, 2014. Aligning resource estimates with mine planning, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 185–192 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By J Deraisme, P Epinoux, O Bertoli

"Société le Nickel exploits the Dôme lateritic nickel orebody at its Tiébaghi operations in New Caledonia. The site geology has an obvious bearing on recovery and metallurgical performance, with controls that are extremely complex at all scales, including lithology, oxydo-reduction conditions, mineralogy and multivariate geochemistry. In this context, establishing an adapted recoverable resource estimation method that is efficient and transparent proves an interesting challenge. The method must address the key notions of: support effect – the exploration data set can only warrant the estimation of large panels of 20 × 20 × 3 m3 that are much larger than the selective mining units (SMUs) of 5 × 5 × 3 m3 information effect – the selection at the production stage uses estimates based on information that is much denser than that available for mine planning.The problem is rendered more complex in this type of deposit by the fact that the additive variables used for the estimation (metal accumulation and ore tonnage) are not the ones used to establish the selection at the mining stage (ie the nickel grade of SMUs, which is the ratio of accumulation on tonnage). Eventually, the selection criteria not only involve Ni grades, but also other elements such as Al2O3, Fe2O3, MgO and SiO2.The solution presented in this paper is to construct an SMU multivariate simulation in the saprolitic horizon and construct an efficient post-processing aimed at producing multivariate recoverable resource estimates. The efficiency constraints imposed by the number of blocks to be simulated (1.5 million) lead to resorting to direct block simulations. The underlying multivariate discrete Gaussian model, which had its validity tested beforehand, is put to good use to mimic the selection process that happens at the mining stage by offering the ability to simulate a composite value located at random within each block.The paper also presents the application of a scenario reduction algorithm to pick a representative subset of a few simulations to help appraise the risk attached to the downstream (reserve optimisation and mine sequencing) phases of the project. The original version of the paper (see Deraisme, Bertoli and Epinoux, 2014) used an early version of the scenario reduction plug-in (named S2RM) built in Isatis® software, where measuring the distance between the initial set of scenarios and the reduced set was based on Ni only. The implementation presented here is for the second generation of S2RM, where measuring the distance between the initial and the reduced set of realisations is based on all study variables. The benefits of the measure adapted to the true multivariate nature of the problem are highlighted in this updated version of the paper.CITATION:Bertoli, O, Deraisme, J and Epinoux, P, 2014. Multivariate block simulations of a lateritic-type nickel deposit and post-processing of a representative subset, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 99–108 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By M Spleit

A long-term production schedule for the LabMag iron ore deposit in northern Quebec, Canada is derived using stochastic integer programming. The optimisation formulation maximises the schedule’s net present value while simultaneously managing the risk of deviations in production tonnages and qualities by considering stochastic simulations of the orebody instead of a single deterministic model. The formulation also smooths and minimises haul truck requirements and ensures that as mining progresses, space is created within the mined out pit for the return of waste material.CITATION:Spleit, M, 2014. Production schedule optimisation – meeting targets by hedging against geological risk while addressing environmental and equipment concerns, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 397–404 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By D J. Thobaven

Project financing challenges caused by declining commodity prices have heightened the need to improve project metrics such as minimising capital payback and maximising net present value (NPV). Khnaiguiyah Mining Company (KMC) recognised the need to improve early cash flows at their Khnaiguiyah zinc-copper project after initial studies showed the project could not provide the desired financial outcomes necessary to compete for investment dollars in the current market.A significant improvement to the project cash flow in early years was realised through simultaneous optimisation of cut-off grade, mining sequence and strategic stockpiling. This improvement was achieved by accelerating mining in the pits, stockpiling lower value material and feeding higher value material directly to the plant. The schedule required higher mining and stockpile capacities than the sequential mine plan although minimal change occurred in the definition of ore and waste as measured by tonnes and grade. The practicality of the strategic schedule was demonstrated by implementation in the production plan with excellent conformance.CITATION:Thobaven, D J, 2014. An application of simultaneous optimisation – improving the payback period of a zinc-copper open cut project, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 137–144 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By E Lima, D Gregory, C Faria, A Lapworth

Open pit mine enterprises are complex systems that depend on multiple parameters such as mineral grade distribution, geotechnical characteristics, economic conditions and engineering requirements, all of which vary in time and space with a different degree of uncertainty. The unpredictable nature of these inputs imposes a risk to mine owners and shareholders which in the worst case can lead to highly undesirable financial outcomes.Desktop studies commonly examine a set of scenarios based on adjusting variables such as commodity prices, mining costs, processing costs, and metal recoveries to understand their relative impact on the economics of a project. It is less common to systematically explore and simulate hundreds of alternative physical designs to understand and compare the financial risk associated with one or more mining projects.This paper presents a risk assessment tool set and methodology that uses web based cloud computing to enable multiple simulations of mine designs to be rapidly and efficiently executed. The methodology includes generating random sets of prices and costs values within their respective probability distributions. Pit designs are then created to maximise the profit for each set of input variables. Various approaches to analysing the results are discussed. Use of the same high performance computing environment for understanding the financial risk associated with the variability of the underlying geological resource model is also discussed, with provided examples using case studies from two of the major mineral producers.CITATION:Lima, E, Gregory, D, Lapworth, A and Faria, C, 2014. Simulating extraction shapes to understand financial risk using high performance computing, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 351–360 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By K G. van den Boogaart, U Mueller

"An accurate prediction of benefit for some types of ore may require not just the ore grade, but a whole compositional characterisation of the estimates; that is, its full mineral composition including waste composition and nuisance elements. An example is Nickel laterite ores, where Ni/Co content must be complemented by estimates of many other elements, as these might have a notable impact on ore processing.Estimates are often obtained with (co-)kriging or co-simulation; however, geostatistics applied to compositional data may yield spurious and inconsistent results because of the constant sum constraint. The log-ratio approach avoids such problems. It proposes a three-step procedure: data are mapped to a set of log ratios of components; for example, the additive log-ratio transformation (alr) transformed scores are modelled with an appropriate fully multivariate geostatistical toolbox; for example, direct/cross-semivariograms and co-kriging or co-simulation results are back-transformed to the original units.Some practical aspects may make the application of this technique difficult: the assumption of Gaussianity and the high dimensionality of the compositions combined with the need for using multivariate methods. This contribution compares several ways of treating compositional data (combining normal score transformation, log-ratio transformations, and minimum/maximum autocorrelation factor (MAF) decomposition) with respect to their ability to generate sensible simulations. The various approaches considered are illustrated with a Nickel laterite data set for which ten variables are available. Both theoretical considerations and illustration results suggest that the best combination is (log)ratio-normal score transform-MAF, in this order. The factors so obtained are closer to Gaussian, approximately spatially decorrelated and can be simulated independently. Simulations can later be recombined to alr-simulations, which in turn may be converted back to point compositions. The resulting simulated compositions are compared with a full Gaussian co-simulation of the raw data. The log-ratio methods show reasonable to good reproduction of mean values, and distribution of the data set and by construction satisfy the total sum constraint, in contrast to the simulations based on raw data for which the total sums fluctuate strongly.CITATION:Mueller, U, Tolosana-Delgado, R and van den Boogaart, K G, 2014. Approaches to the simulation of compositional data – a nickel-laterite comparative case study, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 61–72 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By L Blackwell

Strategic mine scheduling is an industry best practice for increasing the long-term value of mining operations and supply chains. The application of optimisation technology such as mixed integer linear programming (MILP) is able to achieve a mathematically optimal result for a given scheduling model. Such a model will typically define the available source material, processing options and desired constraints for accessing and processing the material. Most strategic scheduling models, however, do not include a sufficiently detailed model of capital expenditure options related to capacity expansion.Typical strategic schedule models do not include important capital expenditure features such as capacity decay or expiration (limited lifespan of trucks), precedence relationships over time (rail construction before a new plant or non-linear expansions due to economies of scale) or mutually exclusive option sets (choices of plant size). By not including these features, a strategic mine scheduling model for a capital-sensitive operation is unable to be optimised to deliver maximum value or profitability.This paper presents several results of MILP optimisation using advanced capital expenditure modelling for mining operations and supply chains. Results of the study demonstrate that optimising capital expenditure at the same time as optimising the mining schedule allows for minimisation of capital purchasing, optimal timing for those purchases and optimal scheduling around them. Overall, this results in an increase in net present value.CITATION:Blackwell, L, 2014. Capital expenditure modelling for strategic mine scheduling, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 315–322 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By H Askari-Nasab, E Torkamani, M M. Badiozamani, S P. Upadhyay, M Tabesh

"Decision-making in mining is a challenging task. Optimal decisions regarding shovel and truck allocations, aligned with the optimal short-term production schedules, are crucial in keeping the operations in line with the company’s long-term objectives. In this paper, we present two main modelling components as the means to achieve near-optimal operational plans: a mixed integer linear goal programming model to allocate trucks and shovels to mining faces over a shift-by-shift basis so that operating costs are minimised and resource utilisations are maximised through the planning horizon a discrete-event simulation model to investigate the behaviour of a truck-and-shovel haulage system in the presence of uncertainty within mining operations. The goals integrated into the mathematical model are to: maximise production minimise deviations from the target head grade minimise deviations in tonnage feed to the processing plants from the desired feed minimise operating cost.The mathematical model provides shovel assignments and the target productions as an input to the discrete event simulation model while meeting the desired goals and constraints of the mining operation. We illustrate the applications of the discrete event simulation model by presenting a large-scale oil sands case study. The main contribution of this work thus far is the development, implementation and verification of a simulation model with a link to optimal short-term mine schedule and extraction sequence of mining faces, which assists in achieving a high level of mine plan compliance across short-term planning and operational planning time horizons.CITATION:Askari-Nasab, H, Upadhyay, S P, Torkamani, E, Tabesh, M and Badiozamani, M M, 2014. Simulation optimisation of mine operational plans, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 299–312 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By K G. van den Boogaart, M Lehmann, U Mueller

Geometallurgical parameters are descriptions of the mineralogy and microstructure of the ore determining its mineralogical and microstructural characteristics. From a conditional geostatistical simulation of such properties, a processing model can compute recovery, equipment usage, processing costs, and thus the monetary value for mining and processing a block with certain processing parameters. The output can be used for optimising mining sequences or finding optimal processing parameters by solving the corresponding stochastic optimisation problem.The approach requires two properties of the simulation not provided by established geostatistical techniques:Many relevant geometallurgical parameters are from non-Euclidean statistical scales such as (mineral) compositions, (grain size) distribution, (grain) geometry, and (stratigraphic type) categorical, which might produce nonsensical values (for example, negative proportions, negative facies probablities, planar grains) when simulated with standard geostatistical techniques.Due to the nonlinearity of processing, the entire conditional distribution of the geometallurgical parameters is relevant, not only its mean and variance. The geostatistical simulation needs to reproduce the joint conditional distributions of all the geometallurgical parameters.The multipoint conditional geostatistical simulation technique discussed here allows for jointly simulating dependent spatial variables from various sample spaces. The technique combines an infill simulation, similar to the one used in multipoint geostatistics, with a new form of distributional regression to estimate conditional distributions of arbitrary scales from different information sources, including training images, training models and observed data. The distributional regression is based on a generalisation of logistic regression and is related to both Bayesian Maximum Entropy geostatistics and high order cumulants.The method ensures that simulated data reside in the set of possible values and honour the characteristics of the joint distribution to be reproduced. The computational effort is substantial, but affordable for a useful application with standard problems: from processing-aware block value prediction and block processing optimisation as shown in the test application to a mathematically completely defined simulated model situation with a complex processing model.CITATION:van den Boogaart, K G, Tolosana-Delgado, R, Lehmann, M and Mueller, U, 2014. On the joint multipoint simulation of discrete and continuous geometallurgical parameters, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 379–388 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By Y Li, S Ramazan, E Topal

A mine waste dump progression plan can provide a clear outlook of the mining operation. To produce such an output requires detailed placement schedule of the mined material, including the volume (or tonnage) and the allocated dumping location. However, current practise mainly focuses on the ore production, over-simplifying the waste material scheduling. As a result, a rock dump is often treated as a single point in long-term planning, making it difficult to predict the waste dump progression pattern over the life-of-mine. Without such a guidance, it is almost impossible to carry out progressive rehabilitation on the waste rock dumps. The lack of dumping schedule could cause delay in development construction, ie tailing storage facility and run-of-mine pad. Other downstream effect due to the over-simplification is inaccurate estimation of required truck hours, which could have huge financial impact on the operation.In this paper, mixed integer programming (MIP) models of different objective functions, ie maximise truck productivity by minimising the overall haulage distance, minimise required truck deviation between adjacent years, and a hybrid between the two objectives, are utilised to generate the long-term optimum rock placement schedules under the criteria of satisfying site specific conditions. All three MIP models are implemented in a large scale open pit mine. The numerical solutions from the models forms three different rock placement schedules, based on which, the yearly truck requirements are easily calculated and compared. The graphical results show the three corresponding waste dump progression patterns over the life-of-mine, providing the optimised long-term forecast of the operation.CITATION:Li, Y, Topal, E and Ramazan, S, 2014. Optimising the long-term mine waste dump progression and truck hour schedule in a large-scale open pit mine using mixed integer programming, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 335–344 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By M Astorga, J Jackson, J Gaunt

The Ban Houayxai mine (BHX) is a relatively low-grade, low-cost, open pit gold-silver deposit in Laos operated by Phu Bia Mining, a subsidiary of PanAust. The ore production rate at BHX is 4.5 Mt/a, with direct tipping to a semi-autogenous grinding/ball mill circuit with a carbon-in-leach process plant. Approximately 100 000 oz of gold is produced per annum.The operation is located in mountainous terrain with minimal run-of-mine stockpiling. This results in limited capacity for blending from stockpile, with the mill instead reliant upon direct feed ex-pit. Since commissioning in 2012, the plant has seen significant variation in milling rates due to variability in the feed properties of the oxide, transition and primary ores. As part of an ongoing continuous improvement program, an integrated approach was initiated, focusing on maintaining and enhancing production in the future as the proportion of harder primary ore increases. This will specifically focus on direct blending from the loading face using the ore properties and blast fragmentation to maintain mill throughput.This approach was based on the concept of physical assets management, commencing with improving information and knowledge of the condition of the orebody through modelling the characteristics, variability and performance of the feed for processes relevant to throughput. These models are used to support both mine and process plant production planning.The key ore feed characteristics and parameters modelled for the life-of-mine are blastability index, powder factor, crushability or impact resistance and grindability. These predictive spatial models were based on the data from diamond drill holes used in resource definition and geotechnical drilling programs by integrating geotechnical, geological, geochemical and metallurgical data.At the early stages of implementation, the models are being utilised for ore blending decisions to provide guidance and support for budgeting, long-term mine planning, blast design for mill feed and providing the mill with an expectation of performance.CITATION:Jackson, J, Gaunt, J and Astorga, M, 2014. Predicting mill ore feed variability using integrated geotechnical/geometallurgical models, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 165–176 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By B Jones, M Chappell

Installing ground support correctly is not an easy task, whether lack of operator training, poor ground conditions or underperforming equipment. Confirming the quality of installation can help with planning and remedial works and help to maintain a safe working environment. A 2?m bolt with 700?mm of encapsulation will pass a 10?t pull test (assuming ground conditions permit); however, it will not provide any data of the unencapsulated length and thus the ability to withstand corrosion. Complimentary testing in the form of non-destructive testing (NDT) can provide factual data that leads to the assessment of the quality of the encapsulation. By improving the quality of the encapsulation, the risk of corrosion is reduced, the ground support durability is improved and hence the asset life is enhanced.This paper presents a case study of a trial using the NDT system RokTel™. Installations of varying quality were installed and tested. The characteristics tested were corrosion, load and free length. These characteristics can be tested and assessed for cable bolts, thread bar bolts and split sets. The aim of this paper is to present NDT testing as a viable method for assessing installation quality and ongoing performance of the ground support.CITATION:Jones, B and Chappell, M, 2014. A non-destructive testing method for measuring bolt condition, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 307–314 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014

By I D. Gregson, A Gandhe, A S. Garcia, K M. MacAndrew

"Falls of ground still cause around one-third of all underground mining fatalities. The recent introduction of mechanised room and pillar working into Indian coalmines is an example of the application of new design by measurement and safety monitoring strategies, which can be used to reduce falls of ground risks in mines.Rock Mechanics Technology, now part of Golder Associates UK Ltd (Golder), is providing rock bolt support and pillar design services to the mines using a design by measurement approach. This involves in situ monitoring to confirm support and pillar designs and pillar extraction sequences.Site examples are given of the support designs determined and the monitoring programs undertaken for both design and safety.Routine safety monitoring of roof movement using telltales is also described. A new auto warning telltale has been developed specifically to warn of imminent caving as pillars are extracted.The approach used by Golder has played a major role in ensuring the success of these projects. As a consequence, mechanised room and pillar working is now firmly established in India, and wider application is planned to help meet future demand for coal.The experience confirms that the design by measurement method for this application has important advantages over conventional design approaches using analytical or empirical methods. Firstly, it measures the design performance or outcome rather than trying to calculate or predict it. Secondly, it provides the opportunity for the design to be adjusted to obtain the optimum outcome. Thirdly, it can do this at a local level, so that the support design can be matched to changing conditions to give the most cost-effective design. Finally, it incorporates long-term monitoring of support performance, again at a local level, so that subsequent change can be identified to enable corrective measures to be implemented in a timely manner and to ensure that ground control safety is maintained.CITATION:MacAndrew, K M, Garcia, A S, Gregson, I D and Gandhe, A, 2014. Application of design by measurement for support and pillar design in mechanised room and pillar coal mining in India, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference, pp 65–76 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 5, 2014

By F M. Weir, M Fowler

The design of excavations in rock must, implicitly or explicitly take into consideration the influence of small- and large-scale geological structures. For most sites the complexity of a fractured rock mass is best captured using a 3D fracture system model based on field data. A discrete fracture network (DFN) approach involves stochastic modelling of the ‘smaller scale’, nondeterministic structures. For slope stability or tunnelling projects DFN modelling provides a valuable geotechnical tool for characterisation of a rock mass and identification of likely failure mechanisms.This paper presents a suite of the DFN modelling undertaken for the design of a large open pit in Australia. The proposed pit is to encounter a 160?m thick, high strength, sheared shale unit, which overlies a crystalline basement. DFN modelling was undertaken separately for both the shale and basement units. While the model inputs and development for each unit are briefly presented, the focus of this paper is on the various applications of the DFN modelling.A key advantage of the DFN approach for design studies is its probabilistic application to structural analyses and results. Multiple realisations of the same model were generated for both the shale and basement units, with the stability analysis carried out on each iteration. The generation of a large number of DFN models and analyses allow assessment of wedge volume, factor of safety and proportion of unstable slope for a range of conditions. The DFN modelling at the case study site was also utilised for a number of other purposes. For example, investigation of likely fault lengths in the shale unit and combined rock mass and defect failure. The development of this tool has been influential in improving our understanding of fracture networks for excavation design.CITATION:Weir, F M and Fowler, M, 2014. Rock mass characterisation and analysis for excavation design – a discrete fracture network approach, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 445–456 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014

By A Duran

When dealing with rocks in the geotechnical profession, a common activity involves providing an estimate of the intact strength. The tactile approach and descriptions of strength that apply to soils is well documented, can be readily checked through the use of a pocket penetrometer and are known to provide reliable estimates of strength. Moreover, although different soil strength classification systems are present, the bounds between classification classes are compatible across almost all documented classification systems. However, this is not the case for rock strength materials. There are numerous systems within the literature, some with different concepts, and almost all have different bounds to each strength classification. In this paper, the author presents seven classification systems for estimating intact rock strength and highlights issues in their implementation. Example cases are presented to discuss the typical concerns confronting field strength estimates.CITATION:Duran, A, 2014. Comparison of field estimated strengths, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 101–106 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014

By A M. Suchowerska, J P. Carter, J P. Hambleton

"In order to effectively predict the roof collapse of underground openings using continuum models, it is imperative that a realistic failure criterion is used to represent the rock mass. However, for coal mining operations, there is still uncertainty as to the appropriate failure criterion that should be used for the coal measure strata. This study compares stability numbers and collapse mechanisms for a rectangular underground opening predicted based on three commonly used failure criteria: the standard Mohr-Coulomb, Mohr-Coulomb with a tension cut-off and Hoek-Brown. Three methods of analysis are considered: an analytical upper bound method an upper and lower bound finite element formulation the displacement finite element method.The theoretical results are also compared with field measurements of subsidence above longwall coal mining. For the standard Mohr-Coulomb failure criterion, the friction angle governs the shape of the collapse mechanism, and the so-called critical width (the cavity width above which failures extend to the ground surface) ascertained from field observations in the New South Wales coalfields corresponds to a friction angle of approximately 30°. On the other hand, predictions obtained based on the Hoek-Brown failure criterion consistently overestimate the critical width. The results also show that tensile failure governs the stability of the cavity and shape of the collapse mechanism when a tension cut-off is introduced for the Mohr-Coulomb yield criterion.CITATION:Suchowerska, A M, Hambleton, J P and Carter, J P, 2014. Prediction of roof collapse for rectangular underground openings, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference, pp 367–374 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 5, 2014

By I D. Gregson, A S. Garcia, K M. MacAndrew

The design work undertaken by Rock Mechanics Technology (now part of Golder Associates (Golder)) has played a major part in successfully establishing mechanised room and pillar working in Indian coalmines, both for the development of new districts and for the re-establishment of old districts prior to pillar extraction on panel retreat.Golder has used appropriate design of pillars, extraction sequences and final snook sizes to minimise ground control risks during pillar extraction. Where the strata will not cave reliably, partial extraction systems leaving either a crush pillar or a substantial yield pillar have been adopted.Remote monitoring of selected pillars during the extraction process using stress cells, extensometry and convergence measurements provides confirmation of pillar behaviour and verification of designs.A new telltale device, giving a visible warning of impending caving, is now installed at all Indian pillar extraction sites and is proving very successful.CITATION:MacAndrew, K M, Garcia, A S and Gregson, I D, 2014. Coalmine pillar design and performance during mechanised pillar extraction in India, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 347–358 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014

By T J. Sullivan, H Pinkster, G Swarbrick

From 2002 to 2006, longwall mining was undertaken directly beneath the Cataract Tunnel, which is a 100-year-old, heritage-listed tunnel in Hawkesbury Sandstone responsible for delivering 20 per cent of Sydney’s annual water supply. At one location, undermining resulted in movement of at least 350 mm along a known fault that transects the tunnel. During this movement, the continued operation of the tunnel was ensured through a careful system of planning, monitoring and management.Of key importance to the success of the operation was the use of advanced numerical modelling to guide operations at all levels, including feasibility assessment, pre-mine mitigation works, monitoring design, operation performance and post-mining condition. This allowed many aspects of the potential impacts to be explored, including joint movements, rock mass deformations and changes in permeability. A key challenge was to successfully model near-field influences on adverse geological structure due to very far-field effects. This paper explains the model’s conceptual development, calibration, performance and limitations.Modelling provided the confidence to manage the impacts of large mining-induced movement on key infrastructure and ultimately predicted fault movement accurately. The fault movement remains one of the highest ever recorded due to mine subsidence.CITATION:Swarbrick, G, Sullivan, T J and Pinkster, H, 2014. Longwall mining beneath critical infrastructure – prediction versus performance of the Cataract Tunnel, New South Wales, Australia, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 157–166 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014