1、翻译部分英文原文Development of a Statistical Technique For a Ssessing Sandstone Escarpment Stability Hamid Maleki, PrincipalMaleki Technologies, Inc.Spokane,Kenneth FleckChuck SemborskiCarl PollastroLarry LaFrentzEnergy West Mining Company, Huntington, UtahAbstract:During the last decade, a significant amou
2、nt of research has been conducted by Energy West Mining Company, government agen-cies, academia, and consulting companies to develop predictive tools for assessing the stability of the Castlegate Sandstone, which is found approximately 250 m above multiple-seam coal reserves. Energy West Mining uses
3、 longwall mining techniques in its opera-tions near Huntington, Utah, and these studies were initiated to satisfy requirements for maintaining the stability of the Castlegate Sandstone and resource recovery.In this study, the authors have used multiple-regression analysis techniques and a wealth of
4、data collected over many years on geol-ogy, mining, and escarpment stability. The volume of failed rocks is used as the response variable after several other factors were con-sidered, including measurement of surface deformation and fre-quency of mining-induced surface fractures. Geologic and geome-
5、tric variables were obtained along 3.7 km of escarpment exposure at 130 study locations. Regression analysis of data for the first 29 study locations, which had been fully undermined, showed that sur-face topography played a critical role in influencing escarpment sta-bility. Preliminary regression
6、analysis results from 70 study loca-tions identified several other important geologic and mining factors that influence the stability of the sandstone escarpment. These are canyon slope, sandstone thickness, and mining influence angle. 1 IntroductionThis paper presents progress being made in develop
7、ing a predic-tive statistical model as a tool for assessing the stability of escarp-ments in the vicinity of Energy Wests longwall operations near Huntington, Utah. Such models are ideal for probabilistic risk analysis so that the economic benefits of extracting coal reserves can be compared to the
8、likelihood of escarpment instability.There are two methods routinely used by engineers and researchers to help predict what conditions will be in the future: statistical and computational. Starfield and Cundall (1) identify rock mechanics problems as “data-limited,” that is, one seldom knows enough
9、about a rock mass to use computational methods unambiguously. These methods, however, are extremely useful for studying failure mechanisms and testing different hypotheses about the cause of failure. Statistical methods, on the other hand, are uniquely capable of being applied where there are good d
10、ata, but a limited understanding of certain phenomena, such as the mechan-ism of escarpment failure (toppling, pure translation, or a combin-ation of these and other mechanisms).Various investigators from both the U.S. government and univer-sities have used computational techniques for analyzing sur
11、face subsidence and escarpment failure mechanisms. The results are in general agreement with studies in the Sydney Basin of Australia (2). A combination of two-dimensional, boundary-element (3), finite-element (4) and discrete-element formulations was used in the U.S. studies. To overcome the limita
12、tions of using small-strain, continuum finite-element methods, a hybrid approach was used. In this approach, finite-element deformation was imposed on a de-tailed discrete-element model of the escarpment and the mudstone foundation and incorporated both horizontal slip planes and vertical joints (5)
13、. Researchers from the U.S. Bureau of Mines (6) also completed a few preliminary three-dimensional, finite-element modeling studies. While successful in analyzing failure patterns and mechanisms, these studies have clearly identified the limitations of numerical modeling techniques in matching measu
14、red surface deformation because of the data-limited nature of these modeling efforts.Statistical and semi-analytical techniques have been used alter-natively for many rock mechanics problems where there are good data but limited understanding of some natural phenomena, such as rock bursts (7), creep
15、 (8), and ground support (9-10). Australian researchers (11) have used the results of comprehensive field investigations with other data analysis techniques to identify the influence of individual factors (such as horizontal movements and cliff heights) on cliff stability. Multivariate statistical e
16、valuations of these results are awaiting additional investigation.The technical approach in this study consisted of incorporating and digitizing data on geology, mining, and escarpment stability collected over many years in several mining areas into a statistical model. This model is being used by mine personnel for routine assessments of escarpment stability in new mining areas even as new data are being incorporated to enhance mo
