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ABSTRACT ABSTRACT Predictions of creep closure for periods ranging up to more than 1,000 years are needed for designing a radioactive waste repository in a salt formation. Such long-term predictions must be based on the use of laboratory and field test results extrapolated by numerical modeling. Laboratory test results are presented which show highly variable creep behavior for salts from different sites as well as salts from the same site. Comparisons of measured creep closures against predicted closures are presented for two deep boreholes in Louisiana salt domes and for an exploratory shaft and drift in southeastern New Mexico bedded salt (WIPP). These data illustrate the significant uncertainties involved in long-term closure predictions and indicate the critical need for site-specific, in situ closure measurements over extended periods of time for repository design. INTRODUCTION The design of a radioactive waste repository in salt must consider the effects of creep closure on waste emplacement operations and on postclosure behavior. Prediction of creep closure during the repository operations period is needed to plan for overexcavation or reexcavation to maintain openings at the minimum required dimensions for waste emplacement activities and to provide for possible retrieval. The time period of interest for repository operations ranges from a few years to as much as 80 years for certain parts of the repository. Prediction of creep closure rates for the period after sealing of the repository is needed to assess the degree to which the salt mass will converge on seal components such as bulkheads and backfill, thus enhancing the effectiveness of these man-made barriers against waste migration. Long-term closure rates are particularly important to the use of crushed salt for backfill material because of the ultimate consolidation of the crushed salt backfill into an essentially impermeable salt monolith (Kelsall et al., 1984). The stresses placed on waste packages due to creep closure are also important to waste package design. The period of interest for these aspects of postclosure behavior is hundreds of year s. These long time periods of interest, together with the complexity and variability of creep behavior, make it impossible to perform full scale tests that model the full time period and simulate the full range of anticipated repository conditions. Current creep closure predictions are thus based on analytical models and laboratory-derived materials properties. It is the purpose of this paper to suggest that high confidence in such predictions is obtained only when 1) laboratory test durations are sufficiently long to characterize steady-state creep, 2) sufficient laboratory tests are run to characterize the in situ variability of the site, and 3) field tests are conducted to validate the models. LABORATORY CREEP MEASUREMENTS Many different analytical expressions have been used by various researchers to describe the creep behavior of salt, frequently indicating very different types and magnitudes of creep strain. It appears that a creep equation of the following form is gaining acceptance as a reasonably accurate expression to describe primary and secondary creep (Hansen and Carter, 1982): (mathematical equation) (available in full paper)
- North America > United States > New Mexico (0.36)
- North America > United States > Louisiana (0.25)
- Water & Waste Management > Solid Waste Management (0.88)
- Energy > Power Industry > Utilities > Nuclear (0.70)
- Energy > Oil & Gas > Upstream (0.48)
ABSTRACT ABSTRACT The resultant load vector is the representation of the forces applied to a longwall roof support element by caving strata into a single, quantifiable measure of support resistance. The relatively complex kinematics of the shield support prohibit a determination of support resistance simply by summation of leg forces. A method is being investigated by the Bureau of Mines to determine the resultant load vector by instrumenting supports with pressure transducers and strain gages to measure leg, canopy capsule, and lemniscate link forces. This concept has been laboratory tested in the Bureau's Mine Roof Simulator. Functional relationships among variables have been assessed, and confidence intervals have been established for prediction of the resultant load vector parameters. RESULTANT LOAD VECTOR CONCEPT Magnitude - Magnitude of loading. Location - Position of load vector on the canopy (as measured from canopy hinge pin). Angle - Inclination of load vector relative to the shield axis (plane) of symmetry measured in a clockwise direction, with 90 ยฐ representing pure vertical loading, and angles less than 90ยฐ describing face-to-waste horizontal loading. Because of inadequate roof control and subsequent failures of several early longwall attempts in the United States, which utilized low-capacity European support equipment, there has been a tendency to increase support capacity with little regard to expected support loadings. Since the cost of a support is related to its capacity, use of excessively large supports represents an unnecessary capital investment and may cause unnecessary fracturing of the roof strata, thereby being detrimental to good roof control. Another major trend, which has occurred in American longwall mining practice during the past decade, has been the dominance of the shield support. The shield design, with its A-frame-type structure, offers the advantage of being able to resist horizontal loading with approximately 25 pct of the shield structure dedicated solely to this purpose (Peng, 1978). Although horizontal loading is a primary design consideration, the degree of horizontal loading and its association with geological conditions is relatively unknown. / Factors such as the uncertainty of horizontal and vertical loading demonstrate the need for additional in-mine support loading information to provide more effective design and utilization of longwall roof support systems. The Bureau is conducting research to provide a better understanding of support loading and caving phenomena by developing a technique to measure the resultant load vector on shield supports. The resultant load vector is the representation of the forces acting upon a support element by a single, quantifiable measure of support resistance. Being a vector, this measure possesses not only a magnitude, but also spatial parameters of location and direction as depicted in Figure 1. Reference will be made to three resultant load vector parameters: From these parameters, critical design information can be ascertained. For example, by knowing the magnitude and angle of the resultant load vector, the magnitude of horizontal (face-to-waste) shield loading can be assessed. Likewise, insight into caving behavior of the strata may be gained by examination of vector location as it moves forward or rearward during the mining cycle.
- Materials > Metals & Mining (0.68)
- Energy > Oil & Gas > Upstream (0.35)
ABSTRACT ABSTRACT This paper presents an analysis of measurements of inter-burden deformations above six longwall faces. An attempt is made to demonstrate some correlation between the movements at the various sites, and to examine their importance with respect to predicting caving height, disruption of overlying seams, and disruption of aquifers. This analysis demonstrates some significant differences between predicted surface subsidence, and inter-burden deformation. In addition it is shown that the caving height above a longwall face is equal to 8 to 12 times the extraction height, with a zone of fractured rock extending to approximately 50 times the extraction height above the seam. INTRODUCTION As the easily accessible seams are exhausted, mining companies will be forced into extracting deeper, underlying seams. Due to increased depth, lower seams could well be extracted by the longwall method. To ensure the optimum longwall layout, and to minimize interaction effects, a better understanding of strata deformation above longwall faces is required. This is essential in areas of high extraction, so as to minimize surface subsidence, aquifer disruption, and interaction between superincumbent workings. Over the past twenty years this problem has attracted the attention of many investigators, however, due to the high cost of inter-burden instrumentation programs, the majority of these investigations were confined to surface and in-mine measurement. This paper presents an analysis of measurements of inter-burden deformations above 6 longwall faces, 5 in the United Kingdom, and 1 in the U.S.A. Details of the various sites are summarized in table 1, along with a summary of the instrumentation utilized. A previous paper by Styler and Dunham described the inter-strata movements, and arrived at the following conclusions: - No significant vertical movements were measured in boreholes located above, and near to the ribside, indicating that gateroad deformation is mainly caused by localized effects. - There was a significant difference between strata behavior in the British and American sites. This was due to the behavior of the strata in the American case histories being dominated by strong/competent beds, whilst in the British case histories, there were no strong beds to have a noticeable effect on the movements above the face. - Empirical methods of subsidence prediction, such as the Subsidence Engineers Handbook (S.E.H.), cannot be used for both countries without a more complete understanding of the influence of geology. - In order to model deformations above a longwall face, an area of fractured rock must be included, the extent of which is dependent upon the presence of any competent beds in the immediate roof. MAXIMUM SUBSIDENCE/HEIGHT ABOVE THE SEAM The absolute maximum anchor displacements have been plotted as a percentage of the extraction thickness against height above the seam, figure 1. It was only possible to plot the results from the British case histories, since only relative, and not absolute movements were reported for the American investigation. The plotted results were all obtained from boreholes near to the face centerline, and are therefore, comparable. As would be expected subsidence decreased with height above the seam, and the rate of reduction in subsidence also reduces with increasing height above the seam.
- North America > United States (1.00)
- Europe > United Kingdom (1.00)
- Research Report > New Finding (0.69)
- Research Report > Experimental Study (0.54)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Organic-Rich Rock > Coal (0.47)
ABSTRACT ABSTRACT Potential applications for boom tunnelling machines requires reliable assessment of in-situ performance. The majority of existing classification schemes concentrate on the prediction of excavation rate, neglecting the important influence of tool consumption rates. A statistical approach is taken to the analysis of the performance data, which was obtained first-hand through in-situ monitoring, and laboratory testing of representative rock samples. A statistical discrimination technique is used to produce a scheme for the prediction of performance groups, each of which covers a limited range of cutting rate and/or tool consumption rate. INTRODUCTION Boom-type tunnelling machines have been in use in the UK for nearly 20 years. Throughout that time, assessment of the performance of machines prior to installation has always been demanded by the client. Research in the University of Newcastle upon Tyne and other establishments has proven a number of existing and novel methods of assessing the rock materials properties pertinent to excavation which, together with in-situ monitoring of machines, led to procedures for predicting machine performance. However, these procedures were only applicable over the very small range of comparatively light- weight machines available, and for single-strata, massive face conditions. Recent increased interest worldwide in the use of boom-type tunnelling machines, for excavation in Public Utilities Tunnelling, Mine Development, Underground Caverns for materials storage, and even Surface Mineral Production Operations, is demanding increasingly confident assessments for the in-situ performance of these machines prior to installation. The increase in the complexity in assessing performance for the wide range of strata now being excavated is further compounded by the range of machines now available. Thus a number of additional factors need to be considered in the equation for machine performance. Table 1 indicates the complex nature of the problem, which was discussed in more detail by Fowell R.J. and Johnson S.T. (1982). The object of this paper is to present a rational approach to machine performance prediction, introducing a practical and flexible scheme for performance prediction, using an approach which is closer to the considered qualitative approach utilised by the experienced engineer. The present scheme aims to effectively quantify this assessment process. In order to attempt to produce such a scheme, data is needed. The data used in this work was obtained from in-situ monitoring of a number of machines, both trackmounted machines, and where the boom was mounted within a shield. The former configuration is preferred when arch-shaped girder supports are used, typical of mine development drives. The latter for smooth profile circular tunnels for sewage schemes, etc., where an immediate sectional concrete support/ lining is desired. Performance is predicted in terms of a machine cutting rate (m/h), which is independent of the operating system, the type of contractor, and the management of the workforce, and thus is dependent on the machine specification, the rock material and rock mass properties. Tool consumption also is effectively dependent on the same parameters, although results taken are presented as shift averages. APPROACHES TO MACHINE PERFORMANCE PREDICTION Current Methods A brief examination of some of the more popular methods in use for the prediction of machine performance resulted in a broad classification.
- Materials > Metals & Mining (0.55)
- Education > Educational Technology > Educational Software > Computer Based Training (0.40)
- Energy (0.34)