Geological Strength Index Chart Research Articles

Characterization of a fractured rock mass using geological strength index: A discrete fracture network approach

Rock mass contains siginificant heterogeneity due to the presence of structural discontinuities. The failure in the rock mass may cause a serious concern to road and rail transportation. Rock mass characterization is a first step towards preliminary investigation for road or tunnel excavations. Different field-based methods have been developed to characterize the rock mass conditions. The Geological Strength Index (GSI) is a simple and commonly adopted method with wide applicability in rock engineering. Traditional approaches are limited to 2D exposures for mapping purposes, but block formation or joint intersection is a 3D parameter. The advancement in computational techniques led to significant involvement of numerical modeling techniques such as those backed with discrete fracture network (DFN). The remote sensing techniques render the data with high precession potential not accessible with conventional methods. The stochastic DFNs generated based upon the statistical distribution of the input parameters can represent the natural fracture system in 3D. The developed synthetic fracture network can be used to examine the rock mass characteristics. This work addresses the incorporation of the Discrete Fracture Network (DFN) in the estimation of the Geological Strength Index (GSI) of the rock mass. The work compares the results of DFN generated using aggregate and disaggregate approaches in block size distribution (BSD) and rock quality designation (RQD) measurements for a fractured slope. The calculated BSD and RQD using DFN and field-estimated joint condition parameter are used to estimate GSI of the rock mass. A machine learning based python GUI tool was developed to find GSI from block volume and joint condition parameters. The prediction of GSI from input parameters using machine learning has led to systematically digitizing the standard GSI chart.

Automatic Cutting Speed Control System of Boom-Type Roadheader Based on Geological Strength Index

The boom-type roadheader is the foremost mining equipment in coal mines. At present, the automatic cutting technology is still immature for adjusting cutting speed automatically in accordance with rock strength, resulting in energy dissipation. In this study, we put forward a method with respect to detecting the geological strength index of coal seam profile through visual inspection, as well as characterize the geological strength index and control the cutting head for adjusting speed automatically based on inspecting fracture features on coal rock’s surface, aiming at achieving energy conservation control of boom-type roadheader. The image processing algorithm is adopted for detecting joint characteristics of palisades fracture, and a quantitative model of the geological strength index is established. The fractal dimension is used to obtain the distribution of geological strength indicators of a coal seam, and the heading machine’s cutting head is controlled for adjusting speed automatically. A vision control platform of boom-type roadheader is built in the laboratory to perform ground simulation experiments. According to experimental results, the difference between the geological strength index of the coal seam detected through visual inspection and the set value in the geological strength index chart is up to 3.5%, and the results are basically consistent, so the quantification of geological strength index can be performed rapidly and effectively. The average energy consumption of boom-type roadheader decreases by 5.4% after adopting self-adaptation control, realizing energy conservation and consumption reduction as well as intelligent control of coal mine machinery.

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

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.

New GSI correlations with different RMR adjustments for an eastern mine of the Quadrilátero Ferrífero

Two of the major classification systems currently used to predict the behavior of rock masses are the RMR (Rock Mass Rating) and the GSI (Geological Strength Index).The RMR was developed with the aim of recommending conceptual engineering solutions, while the purpose of the GSI is to estimate the strength and deformability parameters of rock masses. In an attempt to quantify the GSI from, it was introduced a formula, based on RMR values. Additionally, it was defined the GSI as being 5 points lower than the RMR's 1989 proposal (RMR89). However, since this correlation was defined only for RMR89 values, it should not be employed with any RMR later versions. In this context, this paper presents the results obtained from quantitative and qualitative methods proposed for GSI determination applied to itabirite rock masses in different weathering grades. In addition, another paper's goal comprises the proposal of correlations for defining GSI values from different adjustments suggested for the RMR classification. These proposals were applied to the rock masses of Tamanduá and Sapecado mines, both located in the eastern part of the Quadrilátero Ferrífero, in the state of Minas Gerais, Brazil. Even though the qualitative and quantitative methods of defining GSI values resulted in similar zones in the GSI chart, they have also presented difficulties for defining good correlations specifically for soft rock masses. The new correlations found on this paper resulted in satisfactory results and showed the potential of aiding in the decision-making process in projects involving soft (poor) rock masses, composed by low-strength lithotypes and similar weathering grades.

Comparison of different approaches and development of improved formulas for estimating GSI

The Geological Strength Index (GSI) is an important parameter for estimating rock mass properties and can be estimated either through direct observations or by using a quantitative GSI chart or equations. This study aims to discuss the applicability of the RMR89 method and the latest version of GSI|2013 by applying them to a case study and to the data published in the earlier papers. The relevant rock mass properties, including blockiness and discontinuity conditions, of the walls of 15 exploratory adits with a total length longer than 1100 m in a hydropower project in China have been assessed to provide data for the analysis. In addition, a repeat analysis of the earlier published data has been conducted to validate the results derived from the case study. The results show that the GSI values calculated with the RMR89 are generally larger than those obtained from the qualitative GSI chart, with an average magnitude of about five. The GSI values predicted from the values of JCond89 and RQD are consistent with those derived through the qualitative observational method for a medium-quality rock mass but are relatively smaller or larger for a poor-quality (i.e., GSI 65) rock mass, respectively. By comparing the results of the RMR89 method and the qualitative GSI chart method and analyzing the classification factors considered by the two methods, two new equations based on the RMR89 system have been developed for estimating the GSI. Their application to the case study and the published data prove that these two equations have higher predictive accuracy than the original RMR89 method and the latest version of GSI|2013. It is expected that the acquisition of more engineering examples will enable the two formulas to be further verified and improved.

An analytical model for strength of jointed rock masses

An analytical model of rock mass with non-persistent joints is utilized to develop linear and nonlinear failure criteria based on intact rock strength, joint shear strength and joint persistence. The analytical model matches the solutions for the strength of fracture-free and fully jointed rocks and provides a reasonable transition of strength for rocks with non-persistent joints. Consistent with empirical evidence, the model predicts that increasing joint intensity and decreasing joint shear strength cause a reduction in the cohesive and frictional components of rock mass strength. Joint persistence in the analytical model has not only a physical definition as the normalized jointed area, but also a mechanical interpretation as the normalized reduction in strength due to the presence of joints. The relationship between the analytical model and the empirical Hoek-Brown failure criterion with Geological Strength Index (GSI) is explored. The analytical model of rock mass is utilized to develop and calibrate a quantitative GSI chart. The analytical model is validated using the results of comprehensive tests on undisturbed samples of naturally jointed Panguna andesite and thermally granulated Carrara marble. Practical application of the analytical model is illustrated by numerical back analysis of displacements around the Nathpa Jhakri powerhouse cavern.

Composite Geological Strength Index Approach with Application to Hydrothermal Vein Networks and Other Intrablock Structures in Complex Rockmasses

Conventional rockmass characterization and analysis methods for geotechnical assessment in mining, civil tunnelling, and other excavations consider only the intact rock properties and the discrete fractures that are present and form blocks within rockmasses. As modern underground excavations go deeper and enter into more high stress environments with complex excavation geometries and associated stress paths, healed structures within initially intact rock blocks such as hydrothermal veins, veinlets and stockwork (termed intrablock structures) are having an increasing influence on rockmass behaviour and should be included in modern geotechnical engineering design. Field observations indicate the conventional Geological Strength Index (GSI) does not accurately estimate rockmass strength and behaviour of complex rockmasses. A modified GSI chart to include intrablock structures and a new Composite Geological Strength Index (CGSI) methodology to combine multiple suites of rockmass structure using a weighted harmonic average are presented as tools to evaluate complex rockmasses that contain multiple suites of structure for application to geomechanical numerical models. CGSI is introduced and numerically validated using implicit and explicit finite element method numerical simulations of an underground excavation and a case study of field observations in an adit at the El Teniente mine in Chile. In both cases, the CGSI approach using the modified GSI chart results in an improved estimate of rockmass behaviour in implicit equivalent continuum numerical models when compared to a conventional GSI approach.

A new and unified approach to improved scalability and volumetric fracture intensity quantification for GSI and rockmass strength and deformability estimation

Several investigators have attempted to quantify the Geological Strength Index (GSI) chart, with the latest modification of the chart (2013) utilizing RQD as a measure of blockiness. This approach has limitations where discontinuity spacing is wide (typically greater than 0.3 m) and RQD alone cannot adequately characterize the degree of blockiness, since it remains static at 100%. This paper introduces a new approach to quantifying widely spaced jointed rockmasses that is not dependent on RQD alone. At the core of the approach is a bias free volumetric fracture count (VFC) parameter (fractures/m3), that is integrated into the newly defined GSI chart as an aid to alleviate scalability and bias concerns related to the use of RQD in the quantification process. While the new GSI chart builds on the work of many, it is unique in the sense that not only is it fully quantifiable for a full range of block sizes, including block sizes much larger that possible with RQD alone, but it provides a unique approach linking the VFC parameter with P32, a parameter frequently used in DFN modelling. The correlation of VFC with P32 in particular, is possible because the VFC parameter has no constraints of a limited number and/or assumed orthogonality of discontinuity sets or rectilinear block shapes or the need for block shape correction factors. The new chart also includes correlated scales on both the vertical and horizontal axis using both the RMR and the Q-systems, providing a unified approach that is both scalable and easily quantifiable and allows for the use of all three major rock mass classification systems along with P32 within one chart, something not attempted before. Data from a dam foundation rockmass in the Lesotho Highlands are introduced and are used to validate the quantification process for the overall GSI ratings for the foundation rock mass. These ratings have been used to estimate strength and deformability parameters for the foundation rock mass using the Hoek-Brown empirical failure criteria equations and then they were compared to the large-scale in-situ test results to validate the use of the V-GSI chart and system as a new tool for use in rock engineering.

An Improved Rock Mass Characterization Method Using a Quantified Geological Strength Index and Synthetic Rock Mass Model

In this paper, an improved rock mass characterization method is presented. This method used a quantified geological strength index (GSI) and synthetic rock mass (SRM) model based on a discrete fracture network (DFN) and bonded particle model. A quantitative method is initially introduced to assist in the use of the conventional GSI chart, employing the rock block volume as the quantitative factor. The block volume for a given DFN model is calculated by a volumetric joint count, which is defined as the number of fractures intersecting a cube with unit length. The peak strength and deformation modulus of the rock mass are estimated based on the derived GSI, and then selected as references to constrain mechanical parameters of joints in the SRM model. A parametric study shows a positive correlation between the joint stiffness/friction coefficient and both the peak strength and deformation modulus. The joint friction coefficients of 0.5, 1.0, and 1.5 are suggested to correspond to the surface quality of ‘FAIR’, ‘GOOD’, and ‘VERY GOOD’ in the GSI chart. An advantage of the SRM over the GSI lies in the characterization of the post-peak strain softening behavior. The numerical results show that lower joint stiffness and friction coefficient result in a more ductile post-peak behavior. Additional SRM models with varied joint distributions show the complexities of rock mass characterization, where the rock mass strength, modulus, and post-peak strain softening rate may vary significantly with only one aspect of distributions of the joints, such as intensity or diameter. Triaxial testing is also conducted, and the SRM peak strengths are compared with the Hoek–Brown strength criterion based on the GSI system. The strengths of the regular triaxial models show general convergence with the Hoek–Brown criterion, but the strengths of the true triaxial models are less than those of the regular triaxial models due to deviatoric confining stresses. Meanwhile, ductile post-peak behavior is generally simulated in the triaxial testing. Cracking models are finally investigated and presented, behaving as additional information to explain the changes in the rock mass strength and deformability with different confining stresses. The method presented in this paper is meant to help geologists and engineers to quantify the GSI for jointed rock mass, and characterize the rock mass modulus and strength. Meanwhile, the synthetic rock mass method provides an alternative for rock mass characterization in addition to the traditional GSI method.

Maintaining geological reality in application of GSI for design of engineering structures in rock

In rock engineering design, significant advances have occurred in recent years in numerical modelling capability with increasing trends to ever greater use of synthetic rock mass models and discrete fracture network (DFN) modelling, but all too often with little comparative improvement in geologic base data. As a consequence, there is even more need that reliable estimates be available of strength and deformation characteristics of the rock masses on which or within which engineering structures are to be created, be it a tunnel, a foundation or a slope. Geological Strength Index (GSI) characterization, linked with Hoek-Brown strength determination as a basis for modelling has been widely adopted by engineers and geologists involved in design and construction of engineering structures. The need for geological definition of rock mass properties required as inputs into numerical analysis, constitutes one of the greatest reasons for application of the GSI chart, allowing characterization of even difficult-to-describe rock masses, including tackling even the most problematic of weak and complex rock masses. Back-analyses of tunnels, slopes and foundation behaviour using GSI and its reliable application in rock engineering designs attest to its reliability. With continuing use worldwide, the GSI system has continued to evolve, but greater understanding is needed in the definition of input constants, for establishing both GSI and intact rock properties. This need for improved evaluation, particularly from a geological perspective, is addressed in this paper. Geological processes of tectonism, weathering and alteration all affect GSI. Evaluation of these factors, which are each critical to proper GSI definition, are analyzed based on real rock mass cases. Suggested ranges in variability of intact rock parameters uniaxial compressive strength σci and material constant mi for common rock masses are presented in the context of a composite new GSI chart. This chart allows selection of appropriate GSI ranges for any specific rock suite. Specific key engineering geological characteristics that differentiate igneous, metamorphic and sedimentary one from each other are highlighted through discussion of various example rock units (including gneisses, granites, ophiolites, limestones, schists, siltstones/mudstones/shales, and molassic and flysch formations). Illustrations are given of how geological differentiation dictates variability in geotechnical properties of most common rock masses.

Determination of geological strength index of jointed rock mass based on image processing

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%.

Determination of the mechanical parameters of rock mass based on a GSI system and displacement back analysis

It is very important to obtain the mechanical paramerters of rock mass for excavation design, support design, slope design and stability analysis of the underground structure. In order to estimate the mechanical parameters of rock mass exactly, a new method of combining a geological strength index (GSI) system with intelligent displacment back analysis is proposed in this paper. Firstly, average spacing of joints (d) and rock mass block rating (RBR, a new quantitative factor), surface condition rating (SCR) and joint condition factor (J c) are obtained on in situ rock masses using the scanline method, and the GSI values of rock masses are obtained from a new quantitative GSI chart. A correction method of GSI value is newly introduced by considering the influence of joint orientation and groundwater on rock mass mechanical properties, and then value ranges of rock mass mechanical parameters are chosen by the Hoek–Brown failure criterion. Secondly, on the basis of the measurement result of vault settlements and horizontal convergence displacements of an in situ tunnel, optimal parameters are estimated by combination of genetic algorithm (GA) and numerical simulation analysis using FLAC3D. This method has been applied in a lead–zinc mine. By utilizing the improved GSI quantization, correction method and displacement back analysis, the mechanical parameters of the ore body, hanging wall and footwall rock mass were determined, so that reliable foundations were provided for mining design and stability analysis.

Alternative Quantification of the Geological Strength Index Chart for Jointed Rocks

The paper suggests an alternative quantification of the Geological Strength Index (GSI) Chart published by Hoek et al. in Paper prepared for presentation at the 47th US Rock mechanics/geomechanics symposium held in San Francisco (2013). The engineering parameters proposed for the horizontal and vertical axes of the Chart are the most commonly used in routine field investigations on rock masses by means of standard exposure surveys and borehole logging. They mainly include parameters traditionally used for block size characterization (i.e., volumetric joint count Jv, joint spacing S and RQD/Jn factor) as well as other parameters purposely defined in the present study and based on combinations of the traditional ratings of the Bieniawski’s RMR89 classification. Adopting these options, new empirical equations for calculating the GSI have been fine tuned and tested on a real rock mass dataset. The comparison between the GSI values calculated using the new equations and those directly mapped during underground excavations has demonstrated the potentiality of the proposed methodology. The new quantification of the GSI Chart can be regarded as a useful and practical tool suitable to integrate the Hoek et al. (2013) approach. The complementary use of different and independent methods represents an effective practice to properly check and validate the final estimation of the GSI.

Evaluating Variability and Uncertainty of Geological Strength Index at a Specific Site

Geological Strength Index (GSI) is an important parameter for estimating rock mass properties. GSI can be estimated from quantitative GSI chart, as an alternative to the direct observational method which requires vast geological experience of rock. GSI chart was developed from past observations and engineering experience, with either empiricism or some theoretical simplifications. The GSI chart thereby contains model uncertainty which arises from its development. The presence of such model uncertainty affects the GSI estimated from GSI chart at a specific site; it is, therefore, imperative to quantify and incorporate the model uncertainty during GSI estimation from the GSI chart. A major challenge for quantifying the GSI chart model uncertainty is a lack of the original datasets that have been used to develop the GSI chart, since the GSI chart was developed from past experience without referring to specific datasets. This paper intends to tackle this problem by developing a Bayesian approach for quantifying the model uncertainty in GSI chart when using it to estimate GSI at a specific site. The model uncertainty in the GSI chart and the inherent spatial variability in GSI are modeled explicitly in the Bayesian approach. The Bayesian approach generates equivalent samples of GSI from the integrated knowledge of GSI chart, prior knowledge and observation data available from site investigation. Equations are derived for the Bayesian approach, and the proposed approach is illustrated using data from a drill and blast tunnel project. The proposed approach effectively tackles the problem of how to quantify the model uncertainty that arises from using GSI chart for characterization of site-specific GSI in a transparent manner.