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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 R Dimitrakopoulos, K Kawahata, L Montiel

A method that optimises mining complexes that are comprised of multiple processing destinations, open pits and underground operations is presented. Mining, blending, processing and transportation decision variables are simultaneously optimised while accounting for geological uncertainty. The method uses a simulated annealing algorithm at different decision levels to generate a stochastic-based extraction sequence and processing policies. A case study shows its ability to generate a higher net present value while facing a reduced amount of risk when compared to traditional optimisation methods.CITATION:Montiel, L, Dimitrakopoulos, R and Kawahata, K, 2014. Globally optimising open pit and underground mining operations under geological uncertainty, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 201–212 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By P Malkin, S Thomas, P M. Stone, D Kostyuk

In open pit mining, it is common that the bottleneck to productivity and profit lies in the truck-shovel mining operation, especially as strip ratios and mining intensity have increased with resource depletion and advances in mining equipment capabilities. This paper outlines a study of truck-shovel productivity using a new proprietary discrete event simulation platform for truck-shovel operations called MineDES. The study will specifically address the issue of whether truck queuing at shovels should be considered productive or not, and how mining engineers can determine the optimal truck fleet size for specific operational configurations.The MineDES simulation platform will be described and illustrated, highlighting the features that make it an appropriate tool for conducting the experiments at the heart of this study. The study encompasses a set of experiments, consisting of a number of simple truck-shovel operational scenarios for a benchmark open pit mining operation, which are simulated using MineDES. Each experiment is designed to highlight different factors, which are most important in calculating the ideal truck fleet size and for understanding the marginal productive impact of each additional truck added to the fleet. The scenarios will principally address the issues of how different types and levels of unscheduled equipment downtime and process variation, combined with the traffic delays/congestion arising in more intense mining operations, will affect productivity. The results of the experiments are interpreted using a simple fit-for-purpose cost and revenue model in order to highlight the indicative cost of over- or under-trucked configurations.The study has found that the optimal fleet size that maximises productivity and value will, in the presence of even small process variation, lead to non-zero trucks queuing at shovels on average. As process variations and unscheduled downtime events increase in the truck-shovel system, the required fleet size to optimise productivity and value increases. MineDES provides a convenient platform for quantifying this for real operations.CITATION:Kostyuk, D, Thomas, S, Malkin, P and Stone, P M, 2014. Is truck queuing productive?, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 267–274 (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 R Dimitrakopoulos, C Paduraru

The optimisation of mine complexes and related value chains is a challenging problem due to the simultaneous presence of a highly-dimensional decision space, integer decision variables, non-linear constraints and recovery functions, as well as geological and market uncertainty. In addition, if the uncertainty is revealed progressively, then decisions need to continuously adapt to new information, which poses additional challenges. Stochastic dynamic programming is a well-known class of methods for addressing problems where a decision-maker reacts to new information. While simplistic applications of dynamic programming using exact representations would be intractable for any realistic mine planning problem, new dynamic programming methods using approximate value function representations have been successfully implemented in the past to address problems of similar complexity. This work formulates the problem of mine complex optimisation as an approximate dynamic program and presents an application of the resulting method to a gold-copper deposit.CITATION:Paduraru, C and Dimitrakopoulos, R, 2014. Mineral supply chain optimisation under uncertainty using approximate dynamic programming, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 415–422 (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 B King

A large number of mineral deposits contain regions in which it is economically beneficial to extract ore both via open pit and underground mining techniques. Current industry practice lacks an effective means to establish the extent of the open pit and underground mining areas for an individual deposit. We present a mixed integer programming model for scheduling both the open pit and underground mine that can be used to create a holistic extraction sequence for a specific deposit. The model considers both spatial and temporal elements of the open pit and underground mines in order to maximise the net present value of the entire deposit, while adhering to its production, processing and block sequencing constraints. By doing so, we can schedule the open pit and underground in concert.CITATION:King, B, 2014. Modelling open pit and underground production scheduling in concert, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 369–374 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By J McCluskey, N Barros

"Long-term strategic planning at BHP Billiton’s Western Australia Iron Ore business aims for sustainable value maximisation of its large, long-life, low-cost, expandable mineral inventory and supply chain infrastructure. While mine plans are key components of this value equation, this objective requires a whole-of-business approach through comprehensive consideration and optimisation of the full value chain. Only through a system view do we believe that the value proposition of a set of plans is truly optimised.The fundamental considerations include: mineral inventory information, given its intrinsic uncertainty and risk/knowledge horizon a marketing view of future quality premium/discount based on overall market quality load whole of supply chain optimisation on a throughput accounting basis alternative sequences of capital decisions closure costs with significant magnitude of total movement requiring optimisation as part of the plan. In addition, the plan provides a view of acceptable uncertainty levels at different times in the future.BHP Billiton Iron Ore uses a structured Life of Asset planning process that supports the business with the optimisation of its maximum value proposition through a non-deterministic scenario-based optimisation approach on an annual basis. This is a key input to each year’s medium-term plans and budgets, including activities identified through a value of information approach that resolve the key uncertainties and steadily improve confidence in the plan.CITATION:McCluskey, J and Barros, N, 2014. Life of asset planning, uncertainty and value optimisation of the iron ore business – from exploration, to market, to closure, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014, pp 33–40 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Nov 24, 2014

By B King

Processing expansion options and mining fleet purchases are major cost items that receive due focus in most cost management analyses. If these items are not needed, they can significantly reduce a project’s short-term capital spend and improve profitability and value to shareholders. When conditions change prior to purchasing equipment, the purchase may no longer represent the best decision for the project.This paper outlines a structured approach for making major cost decisions to improve the value of a project, determining which items should be kept and which should be eliminated.CITATION:King, B, 2014. Cost management optimisation, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 7–12 (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 S Chatterjee

The production scheduling of an open pit mine determines the optimal extraction sequencing that significantly impacts a mine’s life as well as net present value. The optimisation of production scheduling of large open pit mines with geological uncertainty is a computationally very intensive process. In this paper, an approximation algorithm is proposed to schedule an open pit mine, where instead of solving the whole problem at once, the production schedule is generated by sequentially solving subproblems. The subgradient method is used to generate the upper bound solution of the Lagrangian relaxed subproblem. If the upper bound relaxed algorithm is not a feasible solution, a mixed integer programming is solved on the generated upper-bound solution. An application at a copper mine with an orebody represented by 16 532 mining blocks is presented using the proposed algorithm. Results show that all constraints are satisfied. The net present values are also calculated and results reveal an 11 per cent higher net present value (NPV) is generated when compared to the NPV generated when the same approach is applied to the deterministic model of the deposit where uncertainty is not accounted for.CITATION:Chatterjee, S, 2014. Stochastic production scheduling – solution through Langrangian relaxation with the branch-and-cut algorithm, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 323–328 (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 G S. Esterhuizen, M M. Murphy, T S. Bajpayee

The strength reduction method (SRM) has been implemented in the FLAC3D finite difference software and applied to coalmine entry support evaluations. The FLAC3D numerical models have been calibrated against instrumented field sites in varying geologies, depths and field stress conditions. The calibration between the field and model results obtained a satisfactory match for rock deformation and support response. Previous research has also developed good correlations between numerical model stability estimations using the SRM and empirically derived stability estimations using the analysis of roof bolt systems (ARBS) software and the coalmine roof rating (CMRR). The purpose of this paper is to further apply the SRM to investigate how specific geotechnical and support parameters can impact coalmine entry stability. Results from different studies demonstrate how the calibrated models were used to investigate a few of the well-known questions in both mine design and support design. For mine design the impact of strong and weak beds in the roof strata, rotating the principal stresses and weakening the floor on excavation stability is investigated. For support design the impact of support length, support spacing and adding cable bolts to the design is demonstrated. These scenarios are evaluated in developmental coalmine entries for a variety of geology and field stress conditions. Stability factors are calculated for each scenario using the SRM. The SRM allows the impact of each parameter to be quantified and allows the support system to be optimised for given geotechnical conditions.CITATION:Esterhuizen, G S, Murphy, M M and Bajpayee, T S, 2014. Analysis of geotechnical and support parameters on coalmine entry stability using the strength reduction method, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 383–392 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014

By D W. Airey

Frictional rock bolts are widely used because of their low cost. However, they have a thin wall section that makes them prone to corrosion, and they cannot be relied on to provide long-term ground support. It is difficult to assess the capacity after the bolts have been installed as they can only be pulled out if this has been allowed for during insertion. The questions of how long can the rock bolts function effectively and the effectiveness of various surface coatings, which can have significant cost implications are, investigated in this study.A series of accelerated corrosion tests have been performed on frictional rock bolts. The rock bolts, typically 900 mm long, have been inserted into concrete cylinders produced using a styrofoam insert to create the hole for the bolt insertion. Strata rock bolts with four surface coatings have been tested: uncoated black steel, galvanised, enduro (plastic coated) and coated with a proprietary aluminium/zinc alloy. Tests have been performed on two diameters of bolts. Following insertion of the bolts, the concrete cylinders were placed in a large drum filled with saltwater and current was applied to accelerate the corrosion process. The rock bolts were then extracted at various times after the commencement of corrosion. Comparison of the insertion and extraction resistances shows that corrosion initially increases the bolt resistance and that substantial section loss is required for the bolt resistance to drop significantly.CITATION:Airey, D W, 2014. Laboratory corrosion tests of frictional rock bolts, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 257–262 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 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 M J. Dunn

The geotechnical model is the cornerstone of any mining geotechnical design and as such the confidence level and reliability of the geotechnical model is paramount. The geotechnical model confidence level needs to be commensurate with the level of design that is being applied or study that is being undertaken. The geotechnical model is made up of several components and the reliability of the data underlying these components ultimately determines the reliability of the geotechnical model.Data reliability is linked to the quantity of data collected, the spatial distribution of the data collected, quality of the data collection and the interpretation of the data. In a typical project, there is an opportunity to improve data reliability through the scoping, prefeasibility, feasibility and final design and implementation stages.Currently, very little quantitative guidance exists in the literature on assessing the confidence level of geotechnical studies, although there have been attempts by various authors (Haile, 2004; Haines, Swart and Kruger, 2006; Read and Stacey, 2009; Dunn, Basson and Parrott, 2011) to qualitatively describe what level of geotechnical data is required. Recently a number of authors (Read, 2013; Fillion and Hadjigeorgiou, 2013; Thomas, 2013) have outlined methods that could be applied to assess the reliability of some types of geotechnical data that are used in the development of geotechnical models.This paper briefly discusses some of the factors influencing data reliability and the geotechnical model confidence and some of the tools available.CITATION:Dunn, M J, 2014. Geotechnical models and data confidence in mining geotechnical design, in Proceedings AusRock 2014: Third Australasian Ground Control in Mining Conference , pp 127–134 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 5, 2014