Abstract
More and more often, and on an increasingly large scale, the geological resistance index (GSI) system is used for the design and practice of the mining process. The GSI, is a unique system for classifying the mass of rocks, linked to the parameters of rock strength and mass distortion, based on the generalized criteria of Hoek-Brown and MohrCoulomb. The GSI can be estimated using standard and in situ tables by direct surface observations in underground or surface mining. The GSI value provides a numerical representation of the overall Geotechnical quality of the rock mass. The method for determining GSI using photographic images of the in situ rock mass, with image processing technology, fractal theory and artificial neuronal network (ANN), is already known and successfully applied in several projects.
Highlights
Knowledge of the mechanical properties of the rock mass is a prerequisite for numerical simulation and the design of underground mining work
The standard geological resistance index (GSI) diagram shall take into account the qualitative surface condition and the blocking of a rock mass and shall be used to estimate a value between 0 and 100 representing the overall Geotechnical quality of the rock mass (Figure 1)
Several researchers have shown that GSI can be co-quantified using the rock quality Index (RQD-Rock quality design, proposed by Deere in 1964), the Q-rock quality Index, proposed by Barton and its contributors (1974), the RMR (Rock mass rating, proposed by Bieniawski (1974), or RSR (Rock structure rating) Proposed by Wickham (1972) [4]
Summary
Knowledge of the mechanical properties of the rock mass is a prerequisite for numerical simulation and the design of underground mining work. The method of quantitative determination of GSI involves taking three steps: Detection of discontinuities (fractures, cracks, gaps, etc.) in two-dimensional (2D) photographs of the rock surface using image processing technology;. Details are explained in William (2007) [6] In this way, we automatically collect the properties of the fracture system, such as fracture length and intensity, spacing and roughness, from discontinuity tracking images in an underground mining job on the 3 sides (left, right, and ceiling walls). We automatically collect the properties of the fracture system, such as fracture length and intensity, spacing and roughness, from discontinuity tracking images in an underground mining job on the 3 sides (left, right, and ceiling walls) Image analysis for this purpose (determination of fractures), refers to the extraction of measurements, data or information from an image using automatic or semi-automatic methods.
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