Variability of the GSI Index Estimated From Different Quantitative Methods

Morelli, Gian Luca

doi:10.1007/s10706-015-9880-x

Cited by 20 publications

(6 citation statements)

References 30 publications

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“…Overall, the fracture protocols performed well when compared to expected values, photographs of the fractures or, in the case of orientations, measured values. A comparative study by Morelli (2015) evaluated the relative influence that each input parameter used in the RMR and Q-index calculations has on the final calculated value. This study determined that the RMR is most heavily influenced by the RQD and UCS values.…”

Section: Implications For Rmr and Q-indexmentioning

confidence: 99%

“…It may be possible, however, to collect fracture roughness, fracture spacing, fracture condition, and fracture alteration on whole, un-oriented drill core. The work by Morelli (2015) shows that the number of fracture sets contributes less than 10% to the variability of the RMR and Q-index calculations, so it may be possible to calculate partial RMR or Q-index values (without calculating the number of fractures sets) using whole, un-oriented drill core. This potential should be investigated, as it may provide meaningful geotechnical information without the cost and logistics of obtaining oriented drill core.…”

Section: Advantages Of Automated Hyperspectral Imaging For Geotechnic...mentioning

confidence: 99%

See 1 more Smart Citation

Automated Core Logging Technology for Geotechnical Assessment: A Study on Core from the Cadia East Porphyry Deposit

et al. 2019

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The Cadia East porphyry deposit, located approximately 20 km south of Orange, New South Wales, Australia, contains a significant resource of copper and gold. This resource is hosted within the Forest Reefs Volcanics and is spatially and temporally associated with the Cadia Intrusive Complex. To extract ore, the underground mine currently uses the block cave mining method. The Cadia East geotechnical model provides data inputs into a range of numerical and empirical analysis methods that make up the foundation for mine design. These data provide input into the construction of stress models, caveability models, ground support design, and fragmentation analysis. This geotechnical model encompasses two commonly used rock classification systems that quantify ground conditions: (1) rock mass rating (RMR) and (2) rock tunneling quality index (Q index). The RMR and Q index are calculated from estimates of rock quality designation (RQD), number of fracture sets, fracture roughness, fracture alteration, and fracture spacing. Geologists and geotechnical engineers collect information used to produce these estimates by manually logging sections of drill core, a time-consuming task that can result in inconsistent data. Modern automated core scanning technologies offer opportunities to rapidly collect data from larger samples of drill core. These automated core logging systems generate large volumes of spatially and spectrally consistent data, including a model of the drill core surface from a laser profiling system. Core surface models are used to extract detailed measurements of fracture location, orientation, and roughness from oriented drill core. These data are combined with other morphological and mineralogical outputs from automated hyperspectral core logging systems to estimate RMR and the Q index systematically over contiguous drill core intervals. The goal of this study was to develop a proof-of-concept methodology that extracts geotechnical index parameters from hyperspectral and laser topographic data collected from oriented drill core. Hyperspectral data from the Cadia East mine were used in this case study to assess the methods. The results show that both morphological and mineralogical parameters that contribute to the RMR and Q index can be extracted from the automated core logging data. This approach provides an opportunity to capture consistent geologic, mineralogical, and geotechnical data at a scale that is too time-consuming to achieve via manual data collection.

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Section: Implications For Rmr and Q-indexmentioning

confidence: 99%

Section: Advantages Of Automated Hyperspectral Imaging For Geotechnic...mentioning

confidence: 99%

Automated Core Logging Technology for Geotechnical Assessment: A Study on Core from the Cadia East Porphyry Deposit

et al. 2019

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“…Han et al [21], Poulsen et al [22], and Wang et al [23] also suggested different methods focusing on quantifying the GSI chart to facilitate the use of the system to determine the strength and deformation parameters of the rock mass. Morelli [24] analyzed different GSI calculation methods based on the Monte-Carlo simulations. He found the highest and the lowest GSI values from the equations which applied the conventional RMR 1989 values and the RMI method, respectively.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Geological Strength Index (GSI) in Regional Seismic‐Landslide Zonation Using the Empirical Regression Model

Mirabedini

Haghshenas

Ganjian

2022

Advances in Civil Engineering

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The assessment of the strength parameters of geological formations in regional scale which encounters thousands of slopes is a complicated approach and time consuming and needs huge field work. This issue is an important research topic concerning the regional seismic-landslide susceptibility analysis or hazard zonation. An empirical regression model was presented to estimate the Geological Strength Index (GSI) with an implication on geological quadrangle of Gorgan region at Alborz mountains range (north of Iran). Two main sets of data were applied in this study: (1) geomorphological data including the slope height, aspect, and distance from faults and distance from thrusts and (2) the physical and mechanical properties of rocks including the unit weight, uniaxial compressive strength (σci), and the petrographic constant (mί) of intact rock. The first group was extracted from a 1 : 100,000 digital geologic map and 10 m digital elevation model (DEM) and the second group was obtained from the Hoek–Brown failure criterion recommended tables. Linear regression equations were generated applying data collected from 294 studied stations using SPSS software. The regression equation predicted GSI in terms of (1) the distance from faults, (2) the distance from thrusts, and (3) the uniaxial compressive strength (σci). The equation had an R2 value of 0.739 and thus fit well to the data. The new method in its present state was recommended for the estimation of the GSI values in regional scale conditions for the assessment of landslide susceptibility and hazard mapping or post events landslide occurrence prediction in the case of probable big earthquakes in Alborz area that is required for emergency responses. The results indicated that the estimation error was about ±30 while the average error was within +5 and −5 and average error percentage was about 3%.

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“…Several authors [26,[38][39][40] have theoretically shown that significant differences i For practical engineering application, the Hoek-Brown failure criterion should only be applied when the potential for structurally controlled failures has been eliminated, as it treats the rock mass as an isotropic material.…”

Section: Introductionmentioning

confidence: 99%

“…Several authors [26,[38][39][40] have theoretically shown that significant differences in GSI estimation result from applying different calculation methods for different rock types.…”

mentioning

confidence: 99%

Study of Rock Mass Rating (RMR) and Geological Strength Index (GSI) Correlations in Granite, Siltstone, Sandstone and Quartzite Rock Masses

Somodi¹,

Bar²,

Kovács³

et al. 2021

Applied Sciences

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A comprehensive understanding of geological, structural geological, hydrogeological and geotechnical features of the host rock are essential for the design and performance evaluation of surface and underground excavations. The Hungarian National Radioactive Waste Repository (NRWR) at Bátaapáti is constructed in a fractured granitic formation, and Telfer Gold Mine in Australia is excavated in stratified siltstones, sandstones and quartzites. This study highlights relationships between GSI chart ratings and calculated GSI values based on RMR rock mass classification data. The paper presents linear equations for estimating GSI from measured RMR89 values. Correlations between a and b constants were analyzed for different rock types, at surface and subsurface settings.

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Variability of the GSI Index Estimated From Different Quantitative Methods

Cited by 20 publications

References 30 publications

Automated Core Logging Technology for Geotechnical Assessment: A Study on Core from the Cadia East Porphyry Deposit

Automated Core Logging Technology for Geotechnical Assessment: A Study on Core from the Cadia East Porphyry Deposit

Estimating the Geological Strength Index (GSI) in Regional Seismic‐Landslide Zonation Using the Empirical Regression Model

Study of Rock Mass Rating (RMR) and Geological Strength Index (GSI) Correlations in Granite, Siltstone, Sandstone and Quartzite Rock Masses

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