The geological strength index(GSI) system,widely used for the design and practice of mining process,is a unique rock mass classification system related to the rock mass strength and deformation parameters based on the...The geological strength index(GSI) system,widely used for the design and practice of mining process,is a unique rock mass classification system related to the rock mass strength and deformation parameters based on the generalized Hoek-Brown and Mohr-Coulomb failure criteria.The GSI can be estimated using standard chart and field observations of rock mass blockiness and discontinuity surface conditions.The GSI value gives a numerical representation of the overall geotechnical quality of the rock mass.In this study,we propose a method to determine the GSI quantitatively using photographic images of in situ jointed rock mass with image processing technology,fractal theory and artificial neural network(ANN).We employ the GSI system to characterize the jointed rock mass around the working in a coal mine.The relative error between the proposed value and the given value in the GSI chart is less than 3.6%.展开更多
Discrete fracture network(DFN) models have been proved to be effective tools for the characterisation of rock masses by using statistical distributions to generate realistic three-dimensional(3 D) representations of a...Discrete fracture network(DFN) models have been proved to be effective tools for the characterisation of rock masses by using statistical distributions to generate realistic three-dimensional(3 D) representations of a natural fracture network. The quality of DFN modelling relies on the quality of the field data and their interpretation. In this context, advancements in remote data acquisition have now made it possible to acquire high-quality data potentially not accessible by conventional scanline and window mapping. This paper presents a comparison between aggregate and disaggregate approaches to define fracture sets, and their role with respect to the definition of key input parameters required to generate DFN models. The focal point of the discussion is the characterisation of in situ block size distribution(IBSD) using DFN methods. An application of IBSD is the assessment of rock mass quality through rock mass classification systems such as geological strength index(GSI). As DFN models are becoming an almost integral part of many geotechnical and mining engineering problems, the authors present a method whereby realistic representation of 3 D fracture networks and block size analysis are used to estimate GSI ratings, with emphasis on the limitations that exist in rock engineering design when assigning a unique GSI value to spatially variable rock masses.展开更多
The main objective of this paper is to examine the influence of the applied confining stress on the rock mass modulus of moderately jointed rocks（well interlocked undisturbed rock mass with blocks formed by three or ...The main objective of this paper is to examine the influence of the applied confining stress on the rock mass modulus of moderately jointed rocks（well interlocked undisturbed rock mass with blocks formed by three or less intersecting joints）. A synthetic rock mass modelling（SRM） approach is employed to determine the mechanical properties of the rock mass. In this approach, the intact body of rock is represented by the discrete element method（DEM）-Voronoi grains with the ability of simulating the initiation and propagation of microcracks within the intact part of the model. The geometry of the preexisting joints is generated by employing discrete fracture network（DFN） modelling based on field joint data collected from the Brockville Tunnel using LiDAR scanning. The geometrical characteristics of the simulated joints at a representative sample size are first validated against the field data, and then used to measure the rock quality designation（RQD）, joint spacing, areal fracture intensity（P21）, and block volumes. These geometrical quantities are used to quantitatively determine a representative range of the geological strength index（GSI）. The results show that estimating the GSI using the RQD tends to make a closer estimate of the degree of blockiness that leads to GSI values corresponding to those obtained from direct visual observations of the rock mass conditions in the field. The use of joint spacing and block volume in order to quantify the GSI value range for the studied rock mass suggests a lower range compared to that evaluated in situ. Based on numerical modelling results and laboratory data of rock testing reported in the literature, a semi-empirical equation is proposed that relates the rock mass modulus to confinement as a function of the areal fracture intensity and joint stiffness.展开更多
文摘The geological strength index(GSI) system,widely used for the design and practice of mining process,is a unique rock mass classification system related to the rock mass strength and deformation parameters based on the generalized Hoek-Brown and Mohr-Coulomb failure criteria.The GSI can be estimated using standard chart and field observations of rock mass blockiness and discontinuity surface conditions.The GSI value gives a numerical representation of the overall geotechnical quality of the rock mass.In this study,we propose a method to determine the GSI quantitatively using photographic images of in situ jointed rock mass with image processing technology,fractal theory and artificial neural network(ANN).We employ the GSI system to characterize the jointed rock mass around the working in a coal mine.The relative error between the proposed value and the given value in the GSI chart is less than 3.6%.
基金NSERC (Natural Sciences and Engineering Research Council of Canada) for the financial support provided to this research through a Collaborative Research Development grant (Grant No. 11R74149 Mine-to-Mill Integration for Block Cave Mines)
文摘Discrete fracture network(DFN) models have been proved to be effective tools for the characterisation of rock masses by using statistical distributions to generate realistic three-dimensional(3 D) representations of a natural fracture network. The quality of DFN modelling relies on the quality of the field data and their interpretation. In this context, advancements in remote data acquisition have now made it possible to acquire high-quality data potentially not accessible by conventional scanline and window mapping. This paper presents a comparison between aggregate and disaggregate approaches to define fracture sets, and their role with respect to the definition of key input parameters required to generate DFN models. The focal point of the discussion is the characterisation of in situ block size distribution(IBSD) using DFN methods. An application of IBSD is the assessment of rock mass quality through rock mass classification systems such as geological strength index(GSI). As DFN models are becoming an almost integral part of many geotechnical and mining engineering problems, the authors present a method whereby realistic representation of 3 D fracture networks and block size analysis are used to estimate GSI ratings, with emphasis on the limitations that exist in rock engineering design when assigning a unique GSI value to spatially variable rock masses.
基金the Nuclear Waste Management Organization (NWMO) of Canadathe National Science and Engineering Research Council (NSERC)+1 种基金the Canadian Ministry of National Defence (DND)the RMC Green Team for funding this research
文摘The main objective of this paper is to examine the influence of the applied confining stress on the rock mass modulus of moderately jointed rocks（well interlocked undisturbed rock mass with blocks formed by three or less intersecting joints）. A synthetic rock mass modelling（SRM） approach is employed to determine the mechanical properties of the rock mass. In this approach, the intact body of rock is represented by the discrete element method（DEM）-Voronoi grains with the ability of simulating the initiation and propagation of microcracks within the intact part of the model. The geometry of the preexisting joints is generated by employing discrete fracture network（DFN） modelling based on field joint data collected from the Brockville Tunnel using LiDAR scanning. The geometrical characteristics of the simulated joints at a representative sample size are first validated against the field data, and then used to measure the rock quality designation（RQD）, joint spacing, areal fracture intensity（P21）, and block volumes. These geometrical quantities are used to quantitatively determine a representative range of the geological strength index（GSI）. The results show that estimating the GSI using the RQD tends to make a closer estimate of the degree of blockiness that leads to GSI values corresponding to those obtained from direct visual observations of the rock mass conditions in the field. The use of joint spacing and block volume in order to quantify the GSI value range for the studied rock mass suggests a lower range compared to that evaluated in situ. Based on numerical modelling results and laboratory data of rock testing reported in the literature, a semi-empirical equation is proposed that relates the rock mass modulus to confinement as a function of the areal fracture intensity and joint stiffness.
