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Rock mass classification is widely used throughout the underground mining industry—in both coal and hard-rock mines. It is used in all stages of the mining process, from site characterization to production operations. The goal of the International Workshop on Rock Mass Classification in Underground Mining was to provide a forum for leading practitioners of rock mass classification to come together and share their methods and experiences with the technique. The workshop was held in Vancouver, British Columbia, Canada, on May 31, 2007. It was co-chaired by Christopher Mark, Ph.D., P.E., National Institute for Occupational Safety and Health, Pittsburgh, PA, and Rimas Pakalnis, P.Eng., University of British Columbia, Vancouver, Canada. The proceedings of the workshop contain 16 invited papers from 9 countries, reflecting the international depth and breadth of current practice. Applications in both hard-rock and coal mining are well represented. Some of the topics that were addressed at the workshop include: • Major rock mass classification systems used in mining and their variants • Collection of input data through observation, rock testing, and geophysics • Design of mine layouts and rock support systems using classification • Estimation of rock mass strength and other input parameters for numerical models from classification • Applications in weak rock, raise boring, cavability assessment, and other special topics • Risk assessment using rock mass classification

Jan 5, 2007

By Terry P. Medhurst, Stuart A. MacGregor, Peter J. Hatherly

In addition to fractures and joints, compositional factors and bedding influence the strength of clastic sedimentary rocks. Rock mass rating schemes must therefore consider all of these factors. In this paper, a rating scheme for clastic sediments based on geophysical measurements is described. Geo-physical logging allows an approximation of rock com-position to be obtained and an assessment of bedding frequency and laminations. Velocity measurements incorporate the effects of fracturing. The rating requires scores for the intact rock, bedding/cohesion, and defects. These are combined to yield a Geophysical Strata Rating (GSR). Determination of the GSR is objective and repeatable. GSR values are typically between 20 and 80 and can be related to the Coal Mine Roof Rating.

Jan 5, 2007

By David Hill

The Australian underground coal mining industry has made extensive use of the Coal Mine Roof Rating (CMRR) classification system for a diverse range of purposes in recent years. These include mining method selection, and coal pillar and roof support design. This paper outlines a series of case histories, from large-scale feasibility studies to local support design investigations, that collectively illustrate the broad applicability, advantages, and usefulness of the methodology, as well as some of the current limitations. The key role of the CMRR in an overall hazard definition methodology is demonstrated for a major Australian project, and some ideas with regard to the future application of the CMRR, in the context of geotechnical risk management within a progressive, highly productive extractive industry, are put forward.

Jan 5, 2007

By Radford B. Langston, John A. Marjerison, Graham C. Howell, Hendrik A. D. Kirsten

Probabilistic design is gaining wider acceptance in the rock engineering community since it allows more rigorous determination of risk relating to ground fall or excavation instability. Risk analysis can be conducted by various means, but the basis is formed by either objectively or subjectively determining probability of occurrence of an event. In the case of rock engineering, this event is either instability or excavation failure. Rock mass classification systems provide objective analysis of data collected on a typically subjective basis that also relate closely to excavation stability. A probabilistic analysis technique is presented that uses statistical distributions of rock mass and material properties, ground support fixture specifications, stress conditions, opening geometry, and ground support installation quality to more rigorously determine probability of failure for an underground opening and the subsequent risk to personnel.

Jan 5, 2007

Multiple-seam interactions are a major ground control hazard in many U.S. underground coal mines. In some U.S. coalfields, particularly in central Appalachia and the West, the majority of today’s mines are operating above and/or beneath previously mined seams. The effects of multiple-seam interactions can include roof falls, rib spalling, and floor heave. These can seriously disrupt mining operations and threaten the safety of miners. In early 2006, a West Virginia coal miner was killed by rib roll that occurred in a high-stress zone beneath a remnant structure in an overlying mine. Fortunately, not every multiple-seam situation results in hazardous conditions. Indeed, the vast majority do not. For the past several years, the National Institute for Occupational Safety and Health has been conducting research to develop better techniques for predicting the location and severity of multiple-seam interactions. During this investigation, more than 50 mines were visited across the U.S. coalfields. Nearly 300 case histories were collected and analyzed using multivariate statistical techniques. The study also employed the numerical model LaM2D to estimate the multiple-seam stress, the Analysis of Longwall Pillar Stability (ALPS) and the Analysis of Retreat Mining Pillar Stability (ARMPS) programs to determine pillar stability factors (SFs), and the Coal Mine Roof Rating to measure roof quality. The study focused on the two most common types of multiple-seam interactions: • Undermining, where stress concentrations caused by previous full extraction in an overlying seam is the main concern; and • Overmining, where previous full extraction in an underlying seam can result in stress concentrations and rock damage from subsidence. The study confirmed that overmining is much more difficult than undermining, and isolated remnant pillars cause more problems than gob-solid boundaries. For the first time, however, it was possible to quantify these effects in terms of the equivalent thickness of interburden needed to compensate for them. The study also found that pillar design is a critical component of multiple-seam mine planning. Many of the failed cases involved pillars whose SF seemed inadequate once the multiple-seam stresses were accounted for. Weaker roof was also found to significantly increase the risk of multiple-seam interactions. The most important result of the study is an equation that predicts the critical thickness of the interburden required to minimize the likelihood of a multiple-seam interaction. This equation was incorporated into a step-by-step methodology that allows mine planners to evaluate each potential interaction and take steps to reduce the risk of ground control failure. Such measures could include installing cable bolts or other supplemental support, increasing the pillar size, or changing the mine layout to avoid the remnant structure entirely. These Proceedings also contain several previously published papers that cover other facets of multiple-seam mining research. Two papers describe the LaModel family of software developed by Professor Keith A. Heasley of West Virginia University. The LaModel programs were designed for calculating the stresses and displacements in coal mines or other thin, tabular seams in layered media. The original three-dimensional version of LaModel is essential for detailed analyses of complex multiple-seam scenarios. LaM2D, by contrast, implements a simplified two-dimensional model that is suitable for quick approximations of the multiple-seam stresses and strains. Three additional papers in these Proceedings describe the extensive multiple-seam experience of the Harris Mine, examples of extreme multiple-seam mining from the central Appalachian coalfields, and longwall mine experiences in Pennsylvania, West Virginia, and Utah. The final paper reports on a numerical modeling study that provided some insight into the mechanics of multiple-seam mining.

Jan 5, 2007

By Rimas Pakalnis, Andrea M. Ouchi, Thomas M. Brady, Mary M. MacLaughlin, Cristian Caceres, Paul Hughes

A major focus of ground control research presently being conducted by the Geomechanics Group at the University of British Columbia, Canada, in conjunction with the National Institute for Occupational Safety and Health’s (NIOSH) Spokane Research Laboratory, is the development of design guidelines for underground mining within weak rock masses. The study expands upon the span design curve for man-entry operations and the stability graph for nonentry operations developed at UBC by extending the application to weak rock masses. The original database has been augmented by weak rock mass information from mines throughout the United States, Canada, Australia, Indonesia, and Europe. The common factor in all of these mines is the presence of a weak back and/or walls. This paper expands on the North American database and how the design curves have been employed at mining operations throughout the world. The definition of a weak rock mass for this study has been defined as having an RMR76 under 45% and/or a Q-value under 1.0.

Jan 5, 2007

By Ernesto Villaescusa, Andres Brzovic

Rock masses of the primary copper ore at the El Teniente Mine are very competent and massive. The rock mass contains almost no open discontinuities. Nevertheless, there is a high frequency network of small discontinuities coupled with widely spaced faults. A data collection campaign designed to characterize the rock structure was recently implemented. The results show that conventional analysis in terms of discontinuity frequency does not predict differences between two studied sectors. However, when an empirical definition of a weak discontinuity is applied, appreciable differences between the two sectors appear in terms of discontinuity frequency and predicted in situ block size distributions. These differences are in accordance with actual observations at the mine site. Due to the geological features of the primary copper ore, rock mass classification schemes cannot be readily applied to characterize these rock masses.

Jan 5, 2007

By Murali M. Gadde, John A. Rusnak

Rock mass classification systems are extremely useful for site characterization and have been employed by the rock mechanics community for several decades. While empirical in nature, the classification systems provide a viable means to quantify the nature of rock mass, which is necessary for stability analyses. In U.S. coal mines, the Coal Mine Roof Rating (CMRR) is the most widely used classification system for several purposes, including sup-port selection, chain pillar design, assessing the stability of extended face cuts, etc. The Analysis of Roof Bolt Systems (ARBS) is an empirical method developed from the CMRR to guide selection of roof bolts as the primary support system in U.S. coal mines. In this paper, the experience of Peabody Energy in applying ARBS to sup-port design is discussed. In general, data from the Peabody mines show that ARBS predictions match well with field conditions. Peabody, however, does not use ARBS as the stand-alone methodology for support selection. Peabody uses a two-pronged approach in which the support requirement is initially estimated from the classification method, and then numerical modeling is used to select the proper reinforcement system. Such an integrated approach is necessary, as ARBS suggests only the “amount of steel” that may be used to support the roof and does not specify which type of roof bolt to use. A case study is used to demonstrate the usefulness of ARBS and Peabody’s integrated approach to support design. Also, the application of ARBS at several Peabody mines showed a very good correlation with support cost. The correlation indicated a direct relation between bolting cost and the ARBS value.

Jan 5, 2007

By Gregory M. Molinda

The Coal Mine Roof Rating (CMRR) was developed 10 years ago to fill the gap between geologic characterization and engineering design. It combines many years of geologic studies in underground coal mines with world-wide experience with rock mass classification systems. Like other classification systems, the CMRR begins with the premise that the structural competence of mine roof rock is determined mainly by the discontinuities that weaken the rock fabric. However, the CMRR is specifically designed for bedded coal measure rock. Since its introduction, the CMRR has been incorporated into many aspects of mine planning, including longwall pillar design, roof support selection, feasibility studies, extended-cut evaluation, and others. It has also become truly inter-national, with involvement in mine designs and funded research projects in South Africa, Canada, and Australia. This paper discusses the sources used in developing the CMRR, describes the CMRR data collection and calculation procedures, and briefly presents a number of practical mining applications in which the CMRR has played a prominent role.

Jan 5, 2007

By Jeremy P. Doolan, Leigh B. Neindorf, Geoff W. Capes

The development and use of rock mass classification tools have been a key component for improving stope design over the past 4 years at the Xstrata Zinc George Fisher Mine in northern Queensland, Australia. In 2003, stope extraction data from 3 years of open-stope mining provided an excellent situation to review the assumptions in the feasibility study. Extracted stope profile information, drillhole geotechnical data, underground observations, and oral and written communication were used to develop a thorough stope reconciliation performance database. With-out collecting the back analysis data and presenting the data in a usable format, engineers are left to debate opinion instead of engineered judgment. This can lead to biased and uninformed design parameter choices with the potential to repeat poor design. This paper demonstrates some effective, practical examples of empirical data collection where rock mass classifications tools were developed and used to create improved confidence in predicting stope stability and failure profiles. The work contributed to design changes that resulted in a reduction in stope hangingwall dilution and an increase in head grade while continuing to ramp up production from 2003–2005.

Jan 5, 2007

By Douglas Milne

Most empirical and numerical approaches to design in rock mechanics incorporate rock mass classification. Numerical design methods generally use classification values to calculate input parameters for stress-based failure criteria. Empirical methods use classification to allow comparisons between similar rock mass conditions, generally based on a graphical design technique that differentiates stable and failed opening geometries. Classification systems are the best tool available for assessing rock mass properties; however, there are problems with classification systems that should be high-lighted. Rock mass performance can only be realistically estimated by coupling a unique description of the rock mass with known loading conditions. Current classification systems cannot provide a unique classification value. The weightings applied to quantify rock mass properties for classification can result in significantly different rock masses having the same classification values. These weightings have been proven effective for tunnel design and support, but classification systems are now used for many more applications. Rock classification systems evolved from a quick and easy field tool for estimating tunnel stability and support requirements. The need for a rapid field tool means that rock mass classification is relatively insensitive to improved methods of measuring rock mass properties. Problems with classification systems and their application are highlighted in this paper. These problems must be recognized and documented before improvements can be made. An understanding of the evolution of classification systems and their application for both numerical and empirical design approaches is invaluable in highlighting current shortcomings.

Jan 5, 2007

By Paul G. Marinos, Vassilis Marinos, Evert Hoek

The Geological Strength Index (GSI) is a system of rock mass characterization that has been developed in engineering rock mechanics to meet the need for reliable input data related to rock mass properties required as input for numerical analysis or closed-form solutions for designing tunnels, slopes, or foundations in rocks. The geological character of the rock material, together with the visual assessment of the mass it forms, is used as a direct input for the selection of parameters for predicting rock mass strength and deformability. This approach enables a rock mass to be considered as a mechanical continuum without losing the influence that geology has on its mechanical properties. It also provides a field method for characterizing difficult-to-describe rock masses. Recommendations on the use of GSI are given and, in addition, cases where the GSI is not applicable are discussed.

Jan 5, 2007

By Vijia K. Karra, John J. Sammarco, Robert Paddock, Edward F. Fries

Workers in hazardous environments such as mining are constantly exposed to the health and safety hazards of dynamic and unpredictable conditions. One approach to enable them to manage these hazards is to provide them with situational awareness: real-time data (environ-mental, physiological, and physical location data) obtained from wireless, wearable, smart sensor technologies deployed at the work area. The scope of this approach is limited to managing the hazards of the immediate work area for prevention purposes; it does not include technologies needed after a disaster. Three critical technologies emerge and converge to support this technical approach: smart-wearable sensors, wireless sensor networks, and low-power embedded computing. The major focus of this report is on smart sensors and wireless sensor networks. Wireless networks form the infrastructure to support the realization of situational awareness; therefore, there is a significant focus on wireless networks. Lastly, the “Future Research” section pulls together the three critical technologies by proposing applications that are relevant to mining. The applications are injured miner (person-down) detection; a wireless, wearable remote viewer; and an ultrawide band smart environment that enables localization and tracking of humans and resources. The smart environment could provide location data, physiological data, and communications (video, photos, graphical images, audio, and text messages).

Jan 4, 2007

By Jamie Girard Dwyer, Edward McHugh, Stanley Miller, Jeffrey Whyatt

This user's guide covers the operation of a suite of three computer programs-Bplane, Bstepp, and Bwedge. These programs can be used to evaluate the potential for plane shear, step-path, and wedge failures along the crest of a slope bench. Such failures reduce the width of a catch bench and may compromise the bench's ability to catch rolling or sliding material before it reaches miners working below. The Bplane and Bwedge programs address sliding of blocks defined by continuous planar joints. The Bstepp program examines sliding of blocks defined by more complex failure surfaces that include steeply dipping cross joints and may even include breaks across small bridges of intact rock. The programs are applicable to jointed rock masses where the joints are small relative to the overall slope and form a number of sets with uniform statistical characteristics within a slope sector. The theoretical basis, application, and operation of these programs are described.

Jan 3, 2007

To evaluate the effectiveness of dust collector bags for reducing dust liberation from a roof bolter dust collection system.

Jan 2, 2007

By G. Luttrell, S. Keles, M. Eraydin, R. Yoon

The dewatering of fine particles is energy intensive and often requires costly capital expenditures. To address these issues, a series of advanced dewatering technologies have been developed at Virginia Tech. These include novel dewatering aids that enhance the performance of existing filtration equipment and an innovative hyperbaric centrifuge that utilizes both air pressure and centrifugal force for dewatering. The moistures obtained using these processes are 30-50% lower than those obtained using conventional methods. Another dewatering technology, known as the hydrophobic displacement process, can reduce product moistures to a level previously attainable only through the use of thermal dryers. This article presents test results obtained with these various advanced dewatering technologies along with estimates of the power consumption for each process.

Jan 1, 2007

By G W. Scott, H M. Lukey

The Lake Superior Region has been a producer of iron ore for over 150 years. Early mining operations concentrated on direct shipping ores, which are the result of post-depositional upgrading principally by deep weathering. As direct shipping ore reserves became depleted, the focus changed to the production of iron ore pellets from taconite. The post-depositional processes responsible for upgrading taconite are regional metamorphism, contact metamorphism and hydrothermal processes (Clout and Simonson, 2005). While these processes are important in iron ore genesis they can also contribute to liberation problems and in some instances reduced pellet quality (Han, 2004). Low-grade metamorphic mineral assemblages can negatively impact liberation and pellet quality. Low-grade iron formation may contain a suite of carbonate minerals including ankerite, dolomite, kutnohorite, siderite and magnesiosiderite. Variation in the distribution and composition of siderite and magnesiosiderite makes controlling the MgO content of pellets difficult. Carbonate minerals also require calcination in the pellet induration process, which requires additional heat to be added to the system, slowing down the induration process and reducing throughput. Prediction of carbonate mineralogy and carbonate mineral chemistry is an important goal of ore characterisation for pelletising properties. Contact metamorphism causes changes in mineralogy and grain size (Klein, 1973; Gunderson and Schwartz, 1962). Increasing the grain size of magnetite should improve its liberation characteristics. However, plant grinding targets may not allow the coarse-grained, more easily liberating magnetite ores to increase throughput and can actually result in decreased throughput due to recirculation. While magnetite and martite ores are well understood (Lukey, Johnson and Scott, 2007) late open-space filling microplaty haematite ores are not. These ores, while composed of similar minerals, have a unique genesis that results in fine grain size and complex textures that present liberation challenges and often make interpreting laboratory metallurgical test results more difficult.

Jan 1, 2007

By H. Krogerus, P. Jokinen, T. Lintumaa

The energy consumption and recovery of chromium are important economic factors in ferrochrome production. Outokumpu has a lot of experience of the reduction behavior of the sintered pellets in industrial-scale furnaces and also on the grounds of numerous laboratory and pilot-scale investigations. Sintered pellets have shown a superiority over lumpy ore and briquettes made of the same chromite grade. The good performance of the pellets has also resulted in investigations on the reasons for such a performance. In this study, the factors affecting pellet behavior in smelting furnace were investigated. This study is divided into two sections, i.e. the investigations were on the factors affecting solid state reduction and on the factors affecting reduction under melting. These tests were performed by means of thermo gravimetry and by smelting tests. The correlation between the pellet characteristics and reduction behavior was shown and its influence on industrial-scale furnaces was discussed. Factors worth of consideration are for instance, the extent of oxidation during sintering, slag formation and chemistry in respect of molten state reduction, and the grain size of the chromite mineral in the raw material in respect of kinetic reasons (retention time in smelting).

Jan 1, 2007

By Merete Tangstad, Kai Tang

The primary aim of the present investigation is to develop a model which permits to calculate/predict the viscosities of ferromanganese slags, typically focus on the MnO-SiO2-Al2O3-CaO-MgO-FeO multicomponent system. The present viscosity model emphasizes the ability of prediction, i.e. using the binary parameters to extrapolate the viscosities in ternary and higher order systems. To this end, a physical sound model is essential. Temperature dependence of viscosity is described by the Eyring?s formalism. The well-known cell model is used to define the SiO44- tetrahedra in the silicate melts. Composition dependence is expressed in term of viscous activation energy, which is characterized by the binary structural units. The viscosities in ternary and higher order systems are hence predicted using the binary activation parameters. The model has been used to calculate the viscosities in the whole liquid composition range of ferromanganese slag.

Jan 1, 2007

By C Aldrich

In this paper, the use of topology preserving projections (Sammon maps) and neural networks are considered as a basis for the construction of multivariate statistical process control charts for a semi-autogenous grinding circuit on a gold plant. Comminution circuits are often operated in a quasi-steady state mode, where the mills tend to move rapidly from one state to another. This makes them difficult to monitor by conventional means designed for continuous steady state processes. As a first step, process data reflecting normal operating conditions are mapped with SammonÆs algorithm. Only one feature is extracted at a time and each mapping is captured by a neural network model to allow online application. Reverse neural network models are used to reconstruct the data prior to the extraction of subsequent features from the data residuals. The ensemble of neural network models can then be used for both fault detection and identification. The approach compared favourable with both linear and non-linear principal component models. It was able to detect two simulated fault conditions both with a reliability of 100 per cent, as opposed to 57.8 per cent and 95.2 per cent detected by an approach based on the use of linear principal component analysis and 84.1 per cent and 98.0 per cent detected by an approach based on non-linear principal components extracted with auto-associative neural networks.

Jan 1, 2007