The Experts below are selected from a list of 4263 Experts worldwide ranked by ideXlab platform
Peter Hatherly - One of the best experts on this subject based on the ideXlab platform.
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overview on the application of geophysics in coal mining
International Journal of Coal Geology, 2013Co-Authors: Peter HatherlyAbstract:Abstract Since their introduction to the coal mining industries of the United Kingdom and West Germany in the 1970s, geophysical methods are now utilised in coal mining around the world. The range of applications in both surface and underground mining is extensive. Applications include coal seam mapping and geological fault detection, lithological mapping, geotechnical evaluation, assessment of the Rock Mass Response to mining, detection of voids, location of trapped miners and guidance of drills and mining equipment. The range of techniques that can be employed is also extensive and includes geophysical borehole logging, the potential field methods, seismic reflection (2D and 3D), resistivity, electromagnetics and microseismic monitoring using active and passive sources. This paper discusses the major applications and the geophysical methods that can be applied. It also discusses future trends and suggests that the future motives for applying geophysics will not only include the current motivations of mine safety and productivity but will also include an increased emphasis on environmental management, the monitoring of sequestration activities and the provision of sensors to enable autonomous mining.
A. Mammino - One of the best experts on this subject based on the ideXlab platform.
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Reliability analysis of Rock Mass Response by means of Random Set Theory
Reliability Engineering and System Safety, 2000Co-Authors: Fulvio Tonon, Alessandro Bernardini, A. MamminoAbstract:When the parameters required to model a Rock Mass are known, the successive step is the calculation of the Rock Mass Response based on these values of the parameters. If the latter are not deterministic, their uncertainty must be extended to the predicted behavior of the Rock Mass. In this paper, Random Set Theory is used to address two basic questions: (a) is it possible to conduct a reliable reliability analysis of a complex system such as a Rock Mass when a complex numerical model must be used? (b) is it possible to conduct a reliable reliability analysis that takes into account the whole amount of uncertainty experienced in data collection (i.e. both randomness and imprecision)? It is shown that, if data are only affected by randomness, the proposed procedures allow the results of a Monte Carlo simulation to be efficiently bracketed, drastically reducing the number of calculations required. This allows reliability analyses to be performed even when complex, non-linear numerical methods are adopted. If not only randomness but also imprecision affects input data, upper and lower bounds on the probability of predicted Rock Mass Response are calculated with ease. The importance of imprecision (usually disregarded) turns out to be decisive in the prediction of the behavior of the Rock Mass. Applications are presented with reference to slope stability, the convergence-confinement method and the Distinct Element Method.
Fulvio Tonon - One of the best experts on this subject based on the ideXlab platform.
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Reliability analysis of Rock Mass Response by means of Random Set Theory
Reliability Engineering and System Safety, 2000Co-Authors: Fulvio Tonon, Alessandro Bernardini, A. MamminoAbstract:When the parameters required to model a Rock Mass are known, the successive step is the calculation of the Rock Mass Response based on these values of the parameters. If the latter are not deterministic, their uncertainty must be extended to the predicted behavior of the Rock Mass. In this paper, Random Set Theory is used to address two basic questions: (a) is it possible to conduct a reliable reliability analysis of a complex system such as a Rock Mass when a complex numerical model must be used? (b) is it possible to conduct a reliable reliability analysis that takes into account the whole amount of uncertainty experienced in data collection (i.e. both randomness and imprecision)? It is shown that, if data are only affected by randomness, the proposed procedures allow the results of a Monte Carlo simulation to be efficiently bracketed, drastically reducing the number of calculations required. This allows reliability analyses to be performed even when complex, non-linear numerical methods are adopted. If not only randomness but also imprecision affects input data, upper and lower bounds on the probability of predicted Rock Mass Response are calculated with ease. The importance of imprecision (usually disregarded) turns out to be decisive in the prediction of the behavior of the Rock Mass. Applications are presented with reference to slope stability, the convergence-confinement method and the Distinct Element Method.
Manfred Blümel - One of the best experts on this subject based on the ideXlab platform.
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Experimental Study of the Brittle Behavior of Clay shale in Rapid Unconfined Compression
Rock Mechanics and Rock Engineering, 2011Co-Authors: Florian Amann, Edward Alan Button, Keith Frederick Evans, Valentin Samuel Gischig, Manfred BlümelAbstract:The mechanical behavior of clay shales is of great interest in many branches of geo-engineering, including nuclear waste disposal, underground excavations, and deep well drilling. Observations from test galleries (Mont Terri, Switzerland and Bure, France) in these materials have shown that the Rock Mass Response near the excavation is associated with brittle failure processes combined with bedding parallel shearing. To investigate the brittle failure characteristics of the Opalinus Clay recovered from the Mont Terri Underground Research Laboratory, a series of 19 unconfined uniaxial compression tests were performed utilizing servo-controlled testing procedures. All specimens were tested at their natural water content with loading approximately normal to the bedding. Acoustic emission (AE) measurements were utilized to help quantify stress levels associated with crack initiation and propagation. The unconfined compression strength of the tested specimens averaged 6.9 MPa. The crack initiation threshold occurred at approximately 30% of the rupture stress based on analyzing both the acoustic emission measurements and the stress–strain behavior. The crack damage threshold showed large variability and occurred at approximately 70% of the rupture stress.
Alessandro Bernardini - One of the best experts on this subject based on the ideXlab platform.
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Reliability analysis of Rock Mass Response by means of Random Set Theory
Reliability Engineering and System Safety, 2000Co-Authors: Fulvio Tonon, Alessandro Bernardini, A. MamminoAbstract:When the parameters required to model a Rock Mass are known, the successive step is the calculation of the Rock Mass Response based on these values of the parameters. If the latter are not deterministic, their uncertainty must be extended to the predicted behavior of the Rock Mass. In this paper, Random Set Theory is used to address two basic questions: (a) is it possible to conduct a reliable reliability analysis of a complex system such as a Rock Mass when a complex numerical model must be used? (b) is it possible to conduct a reliable reliability analysis that takes into account the whole amount of uncertainty experienced in data collection (i.e. both randomness and imprecision)? It is shown that, if data are only affected by randomness, the proposed procedures allow the results of a Monte Carlo simulation to be efficiently bracketed, drastically reducing the number of calculations required. This allows reliability analyses to be performed even when complex, non-linear numerical methods are adopted. If not only randomness but also imprecision affects input data, upper and lower bounds on the probability of predicted Rock Mass Response are calculated with ease. The importance of imprecision (usually disregarded) turns out to be decisive in the prediction of the behavior of the Rock Mass. Applications are presented with reference to slope stability, the convergence-confinement method and the Distinct Element Method.
