A variety of methods are used in underground design, including empirical, observational, analytical and numerical modelling. All design methods require inputs, and these are based on data obtained from core logging, mapping, laboratory testing, field observations and monitoring. This data then has to be compiled and interpreted so that meaningful and reliable design inputs can be derived. Design inputs are required to a have reliability and confidence level that is commensurate with the level of design (scoping through to operational) and that will ultimately satisfy the design reliability and acceptable risk profile for the design. To obtain reliable design inputs, data of sufficient quantity and quality has to be collected and analysed. The variability of this data has to be understood so that reliable inputs can be derived. Currently, very little quantitative guidance exists in the literature on assessing the reliability and confidence of geotechnical studies and design, although there have been attempts by various authors (Haile 2004; Haines et al. 2006; Read & Stacey 2009; Dunn et al. 2011) to qualitatively describe what level of geotechnical data is required. Recently, a number of authors have outlined methods that could be applied to assess the reliability of geotechnical data. These methods have been discussed, and the application of some methods has been demonstrated on data from underground feasibility studies to assess the reliability of design inputs. A preliminary rating scheme to assess the reliability of geotechnical models and design inputs has been proposed. 2 Underground mine design In geotechnical engineering, a variety of design methods are used for underground mine design. These can broadly be classed as empirical, observational, analytical and numerical methods, and they are briefly described in this paper. Irrespective of what design method is used and whether the design approach is deterministic or probabilistic, the reliability of the design is largely influenced by the reliability of input parameters. 2.1 Empirical methods These include various rock mass classification systems such as the Q-system (Barton et al. 1974) and Bieniawski's (1976; 1989) rock mass rating (RMR), which are used both to classify the rock mass as well as for ground support design and excavation design.
In underground mining, there are a number of uncertainties in the ground support design process and during implementation of ground support designs. The minimisation of geological uncertainty is one of the rock engineering principles outlined by Bieniawski (1992) and by Stacey (2004, 2009). Generally, there are two main types of uncertainty; uncertainty due to naturally variable phenomena in time or space and that due to lack of knowledge or understanding (Baecher and Christian, 2003). McMahon (1985) listed six types of geotechnical uncertainty: risk of encountering unknown geological conditions; risk of using incorrect geotechnical criteria; bias and/or variation in estimated design parameters is greater than anticipated; human error; design changes and excessive conservatism. All of these types of uncertainty are encountered in the design of ground support. They occur in the form of uncertainties around: the design block size; loading conditions; spatial variability of ground conditions; rock mass strength; shear strength; discontinuity spacing; discontinuity orientations, etc. Uncertainty is not only present in the design of ground support but also in the implementation phase. This occurs in the form of variations in installation quality; adherence to patterns and spacing; human error in the application of the correct ground support standard; not identifying a change in ground conditions, etc. Conventional deterministic and empirical design methods do not adequately cater for uncertainties in the design of ground support. Probabilistic design methods such as the Point Estimate Method and Monte Carlo Simulation can be applied to better understand the uncertainties in the design process. Limiting uncertainties in ground support implementation can be achieved by: training of operators and supervisors; good supervision of implementation; and a well-considered and managed quality control programme. It is important to feed the results from the quality control and monitoring programmes back into the design. In this paper, the various uncertainties with ground support design and implementation will be reviewed and discussed in context of McMahon (1985) and Baecher and Christian (2003). The application of probabilistic design methods in ground support design and feedback of quality control data will be discussed. 2 Ground support design and implementation processes There are a number of well-documented design schemes for ground support design. Generally, they all require the following: Description of the rock mass and identification of likely failure mechanisms.
Uncertainty in mining geomechanics and geotechnical engineering is a broad term that accounts for natural variability, lack of data, and lack of knowledge. Reducing uncertainty is a key component of the mining study process and in managing geomechanical/geotechnical risk. Understanding and reducing uncertainty is also a key activity in the design process to ensure that designs are robust and resilient. A variety of methods are used in geomechanical design including empirical, analytical and numerical modelling. All design methods require inputs, and these are based on data from core logging, mapping, laboratory testing, field observations, and monitoring. This data then must be compiled and interpreted so that meaningful and reliable design inputs with a reliability that is commensurate with the level of design (scoping through to operational) can be derived. This includes the development of the geomechanical or geotechnical model. The uncertainty of the geotechnical model is often described in terms of confidence or reliability. Currently, very little quantitative guidance exists in the literature on assessing the confidence level of geotechnical studies and design, although there have been attempts by various authors (Haile 2004; Haines et al. 2006; Read 2009; Dunn et al. 2011) to qualitatively describe what level of geotechnical data is required. Several authors have outlined methods that could be applied to assess the reliability of geotechnical data
Evolution Mining's Frog's Leg underground mine experienced an increase in the number of seismic events and rockburst occurrences during 2015. This was due to increased stress levels due to increasing mining depth and unfavourable mining geometry (mining the sole remaining diminishing pillar) as well as interaction with some seismically active crosscutting geological structures. At the time, the extraction sequence was transitioning from central access to an end-on retreat sequence with stopes in the diminishing pillar being extracted as triple lifts. Initially, a triple lift extraction methodology was implemented to eliminate the exposure of personnel and equipment to potentially seismically active ground as the closure pillar was extracted. All production activities for the closure pillar were conducted outside the oredrives from a drive situated in the hanging wall. Intense episodes of seismic activity occurred during the third and fourth quarters of 2015 as well as early in the first quarter of 2016 while the closure pillar was being extracted. During this period, two rockbursts occurred; after the second rockburst, management decided to temporarily halt production activities in the area pending geotechnical investigation and the development of a new extraction strategy. A Geotechnical Review Board (GRB) was formed to review and evaluate the situation to date and provide guidance on: the extraction sequence, ground support design and implementation, and seismic monitoring requirements. Following the GRB evaluation, a program of works was initiated including assessment of various extraction sequences and dynamic ground support design. Subsequently, these have been implemented and mining activities have resumed. This paper provides an overview of the mining practices that led to increased seismicity and rockbursts as well as measures that were implemented to mitigate the hazard associated with increased mining-induced seismicity and increasing stress levels.
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