Rock Mass Classification – an overview

Rock mass classifications form the backbone of the empirical design approach and are widely employed in rock engineering.

From: Engineering Rock Mass Classification, 2011

Related terms:

Bhawani Singh, R.K. Goel, in Engineering Rock Mass Classification, 2011

The classification

The science of classification is called “taxonomy”; it deals with the theoretical aspects of classification, including its basis, principles, procedures, and rules. Knowledge tested in projects is called the “practical knowledge.” Surprisingly the rating and ranking systems have become popular in every part of life in the twenty-first century.

Rock mass classifications form the backbone of the empirical design approach and are widely employed in rock engineering. Engineering rock mass classifications have recently been quite popular and are used in feasibility designs. When used correctly, a rock mass classification can be a powerful tool in these designs. On many projects the classification approach is the only practical basis for the design of complex underground structures. The Gjovik Underground Ice Hockey Stadium in Norway was designed by the classification approach.

Engineering rock mass classification systems have been widely used with great success in Austria, South Africa, the United States, Europe, and India for the following reasons:

1.

They provide better communication between planners, geologists, designers, contractors, and engineers.

2.

An engineer’s observations, experience, and judgment are correlated and consolidated more effectively by an engineering (quantitative) classification system.

3.

Engineers prefer numbers in place of descriptions; hence, an engineering classification system has considerable application in an overall assessment of the rock quality.

4.

The classification approach helps in the organization of knowledge and is amazingly successful.

5.

An ideal application of engineering rock mass classification occurs in the planning of hydroelectric projects, tunnels, caverns, bridges, silos, building complexes, hill roads, rail tunnels, and so forth.

The classification system, in the last 60 years of its development, has been cognizant of the new advances in rock support technology starting from steel rib supports to the latest supporting techniques such as rock bolts and steel fiber reinforced shotcrete (SFRS).

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Bhawani Singh, R.K. Goel, in Engineering Rock Mass Classification, 2011

One of the reasons rock mass classifications is popular over the years because they are easy to use and provide vital information about rock mass characteristics. Classification also leads in making fast decisions during tunneling. Thus, rock mass classification is an amazingly successful approach. There are several correlations that are based on measured support pressures and other related parameters from several Indian tunnels that have steel rib support. Detailed field studies have been carried out for eight tunneling projects located in the Himalayas and peninsular India. Two sets of empirical correlations for estimating support pressure for tunnel sections under non-squeezing and squeezing ground conditions have been developed using N and the measured values of support pressures, the tunnel depth (H), the tunnel radius (a), and the expected tunnel closure (ua) from 25 tunnel sections. In addition, prediction of support pressures in tunnels and the effect of tunnel size on support pressure are the two most important problems in tunnel mechanics and have attracted the attention of many researchers.

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R.K. Goel, … Jian Zhao, in Underground Infrastructures, 2012

Estimating Support Pressure

Various classifications are currently in vogue and may be used for the preliminary support design. The rock mass classification of Barton and colleagues [31] may be used in most cases. It is worthwhile to map the major geological discontinuities that may traverse the proposed cavity. The geometry of the zones traced by these discontinuities should also be marked out to estimate the dead weight of blocks and wedges that may slide and consequently exert dead weight on the supports. The map of the discontinuities will also help in preparing plans for rock bolting and anchoring. According to Barton and co-workers [31], the roof support pressure is independent of the span of the cavern or tunnel in competent rock masses.

The vertical support pressure under most circumstances may be below 0.1 MPa. Due to the enormous height of the side walls, some lateral constraints may also have to be applied with the help of prestressed cable anchors where bedded and jointed rocks are encountered.

It is essential to undertake a finite element analysis of the cavern assuming the rock to be nonlinear. The ratio between horizontal and vertical stresses may be assumed according to measured in situ stresses. The analysis will give some idea on the areas of stress concentration, zones of tension and zones of high shear stresses, compressive stresses in pillars, and the possible mechanism of failure of the caverns. This study will be useful in designing the external support system. The colored graphics of displacements and stresses give an insight into the mechanics of interaction of underground openings with the rock slope, if any, and the effect of the sequence of excavations.

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Shili Qiu, … Xia-Ting Feng, in Rockburst, 2018

11.4.3 Stress Problem Classification

Barton et al. (1974) of the Norwegian Geotechnical Institute (NGI) proposed the Q-system of rock mass classification, which got a major revision in 1993 with the inclusion of the database from more than 1000 tunnel cases (Grimstad & Barton, 1993). This system is based on a numerical assessment of six different input parameters defined by Eq. (11.4.1):

(11.4.1)Q=RQDJn×JrJa×JwSRF

where RQD is the rock quality designation, Jn is the joint set number, Jr is the joint roughness number, Ja is the joint alteration number, Jw is the joint water reduction factor, and SRF is the stress reduction factor.

The numerical estimation of each of these six input parameters of the Q-system is explained in a separate table given in Grimstad and Barton (1993), Barton (2002) and others. One of the parameters in Q-system called SRF is associated with stress-induced instability. Part of the SRF table gives classification on rock spalling/rockburst potential in a tunnel built in hard-rock conditions. Table 11.4.1 is the reworked version of the table that classifies rock burst intensity.

Table 11.4.1. Stress Problems Classification in Hard and Competent Rock Mass Based on Q-System

Stress class Description of potential stress induced instability Ratio between intact rock strength and major principle stress (σci/σ1) Ratio between maximum tangential stress and intact rock strength (σθ− max/σci)
SC 1 Low stress, near surface, open joints > 200 < 0.01
SC 2 Medium stress, favorable stress conditions 200–10 0.01–0.3
SC 3 High stress, very tight structure, usually favorable to blasting except for wall 10–5 0.3–0.4
SC 4 Moderate spalling after > 1 h 5–3 0.5–0.65
SC 5 Spalling and rockburst after few minutes 3–2 0.65–1
SC 6 Heavy rockburst and immediate strain failure < 2 > 1

As can be seen in Table 11.4.1, the stress problems classification method mainly considers three input variables consisting of intact rock strength (σci), maximum principle stress (σ1), and maximum tangential stress (σθ− max). This means that to make an assessment, one should have laboratory-tested intact rock strength and knowledge about the in situ stress conditions of the area in question.

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A.J. Hooper, in Geological Repository Systems for Safe Disposal of Spent Nuclear Fuels and Radioactive Waste, 2010

5.4 Rock mechanics and geotechnical properties

Information is required on the mechanical properties of the rock mass in which the waste deposition will occur and through which access tunnels and/or shafts will be constructed. In particular, it is required to understand the strength of the rock, its deformation properties under loading and the in situ stress regime. Given the occurrence of deformation zones and fractures in many crystalline rocks, this information is required on each of the main structural elements within the rock mass and has to be in a form that allows analysis of the response of the overall rock mass at a range of different length scales according to the element of repository design that is of interest.

5.4.1 Mechanical properties

The strength properties, such as uniaxial compressive strength and tensile strength, of the intact rock not intersected by fractures can be determined by a suite of well-known laboratory tests on drillcore samples. Similarly, the mechanical properties of fractures in the rock can be determined by carrying out tilt tests and direct shear tests on drillcore samples containing a fracture. The deformation properties of the intact rock, classically expressed in terms of Young’s modulus and Poisson’s ratio, can be obtained from the results of uniaxial and triaxial compression tests on drillcore samples.

The results from the laboratory tests are typically used in empirically based rock mass classification systems to determine the rock mass deformation and strength properties that are relevant to repository design and to determining the response to mechanical or thermal loading. The empirically based rock mass classification systems draw on a large database generated from geotechnical engineering projects and are widely used in underground tunnelling and civil engineering works in other industries. There is an increasing trend to use theoretically based numerical modelling to determine the relevant rock mass properties and such an approach has been used alongside the empirical approach in SKB’s site characterisation programme, for example (Glamheden et al., 2007).

5.4.2 In situ stress

The in situ stress can be determined by using various techniques. It is resolved into maximum horizontal stress, minimum horizontal stress and vertical stress, so that both the magnitudes of these and the orientation of the maximum and minimum stress direction are required. Commonly applied methods include overcoring, where a small diameter borehole is drilled at the base of a cored borehole. Strain gauges are fixed to the wall of this borehole and the instrumented rock is then retrieved to the surface by overcoring, allowing the direct measurement of the stress. Techniques involving hydraulic pressure are also commonly used to measure in situ stress where the hydraulic pressure on an identified fracture in a borehole wall is increased and decreased to determine the pressure at which it just opens and closes. Hydraulic fracturing is also used where the hydraulic pressure in a section of a borehole is increased until fracturing of the rock is induced.

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Raoof Gholami, Nikoo Fakhari, in Handbook of Neural Computation, 2017

27.7.2 Rock Mass Classification (RMR) System

Rock mass characterizations of complex structures are crucial to recognize their vulnerable regions. However, when it comes to large structures such as tunnels, obtaining information about rock mass properties is not an easy task. There are generally two methods, known as destructive and non-destructive, which can be used on these occasions considering different aspects of structures [13]. Destructive is a common term used for methods which can accurately determine the mechanical properties of rocks using direct mechanical tests in the lab. These methods are, however, time consuming and expensive [39]. As a result, non-destructive methods such as Ground Penetrating Radar (GPR) system, X-ray Radiography, Impact Echo (IE) have been developed [20,28]. Although non-destructive methods are cost-effective and faster compared to destructive ones, they not often can yield meaningful results because of not measuring the rock mass properties directly. Alimoradi et al. [6] carried a study on the estimation of Rock Mass Classification (RMR) system using the Tunnel Seismic Prediction (TSP-203) method which could provide compressional and shear wave related data including velocity, orientation, and polarity. They trained a conventional Back-Propagation Neural Network (BPNN) for such estimation but did not get a better correlation coefficient than 0.88 at the testing stage.

Gholami et al. [24] compared the application of an empirical correlation, SVR, and RVR in prediction of the RMR using the data of two tunnels located in the North of Iran. Linking the velocity of P-wave and Rock Quality Control (Q) to have an empirical correlation for the RMR, it was found that the empirical correlation either overestimates or underestimates the value of RMR along the route of the tunnels. Knowing that ANNs are not able to provide sophisticated results based on the earlier study, the SVR and RVR were integrated by the GA for prediction of the RMR. The results provided by the GA highlighted that P– and S-wave velocities together with their magnitudes and reflection depths are the best input parameters to train the machines. The available data was then normalized using the min–max approach and the Gaussian kernel function was chosen for the both machines. The parameters of each machine were then tuned by the K-fold cross-validation technique. Fig. 27.9 highlights the efficiency of the machines in the testing stage.

Figure 27.9. Comparing the application of the empirical correlation with the SVR and RVR in prediction of RMR [24].

Having a promising result from the machines based on the data of one tunnel, the same machines were used to estimate the RMR of the second tunnel where the real RMR was not available but two crushing sites were observed between 3100 and 3300 m of the tunnel route. Fig. 27.9 displays the performance of the SVR and RVR in recognizing two crushing sites and predicting the RMR of the second tunnel.

Looking at Fig. 27.9, one may conclude that the SVR and RVR are a better option compared to ANNs in prediction of RMR. Although the RVR seems to be a better machine, the results obtained from the SVR were still very promising.

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Petr Konicek, in Rockburst, 2018

14.4.3 Engineer Approaches for Stress Release Evaluation

Despite the extended use of destressing or preconditioning in many mines across the world, there is no well-established engineering technique or methodology for this practice. There are now two possible engineering methodologies for the stress release evaluation of destress blasting. The first is the destressability index methodology for the assessment of the likelihood of success of a large-scale confined destress blast in an underground mine pillar, presented by Andrieux and Hagigeorgiou (2008). In many cases, this method is still a trial-and-error procedure based on the previous mining experience (Andrieux & Hagigeorgiou, 2008). However, some approaches at establishing a scientific basis for destress blasting have been carried out successfully. Different attempts can be used to evaluate the effectiveness of destress blasting (before and after it is performed). For instance, Andrieux and Hagigeorgiou (2008) developed an empirical method to evaluate in advance the effectiveness of large-scale destress blasting in a deep underground mine pillar, which was investigated in Canada. This method evaluates nine different parameters of the rock mass and blasting by using the “rock engineering system” (i.e., RES; Hudson, 1992) in the “destressability index.” In this way, the overall destress-blasting behavior is calculated in the same way as the rock mass classification systems, assigning values to each parameter and gaining a final normalized score ranging from 0 to 1, which indicates the destress capacity of the blasting (i.e., low, medium, good, or excellent). The blasting parameters (e.g., blasthole diameter, spacing, and explosive charge), can be evaluated in advance using this method to adjust them to obtain the maximum destress capacity. The nine parameters assessed by this method are as follows:

stiffness of the rock mass

brittleness of the rock mass

degree of fracturing of the rock mass

proximity to failure of the rock mass

orientation of the destress blast

width of the destress blast

unit explosive energy

confinement of the explosive charges

result of the destress blast

The second one is a calculation of the SE for the evaluation of the stress release of the rock mass due to destress blasting, presented by Konicek, Soucek, Stas, and Singh (2013) and Konicek, Ptacek, et al. (2013). This methodology has shown acceptable results for the evaluation of destress blasting in the hard coal mines of the Czech Republic by monitoring the changes of stress magnitudes near destress blastings and calculating the seismic effect from the available seismic monitoring data and the weight of the explosive. The methodology is used in the rockburst prevention system from the 1980s, when it was established by Knotek (1985) and subsequently verified by Konicek, Soucek, Stas, and Singh (2013). The methodology was successfully tested in Polish coal mines as well (e.g., Wojtecki & Konicek, 2016). This methodology is based on SE calculations and their evaluation considering the success of destress blasting in light of stress release. SE is typically defined as the ratio of seismic energy released in the rock mass when blasting to the considered energy of the particular detonated charge (further details can be found in Konicek, Soucek, Stas, & Singh, 2013) and can be calculated according to the following formula:

(14.1)SE=EICMKICMQ

where EICM is seismic energy in J in the investigated coal mine, Q is the weight of the explosive charge in kg, and KICM is the coefficient of the natural and mining conditions of the rock mass in the investigated coal mine (Konicek, Soucek, Stas, & Singh, 2013). Coefficient KICM must be determined for the investigated rock mass conditions, in which seismic monitoring is carried out and where the seismic energy of recorded seismic events is calculated in the same way.

And finally, numerical modeling techniques can be used for the evaluation of the destressing effect. It is suggested that numerical modeling techniques be employed in conjunction with field measurements. In fact, extensive studies have already been undertaken with a variety of numerical simulation techniques to investigate the effect of destress blasting (e.g., Blake, 1972; Mitri, 2001; Saharan & Mitri, 2009; Tang & Mitri, 2001; Zhu, Wei, Li, Wei, & Zhang, 2013). Blake (1972) proposes a parameter α, which represents the reduction in the modulus of the elasticity of a rock mass preconditioned by destress blasting. Tang and Mitri (2001) additionally develop a stress dissipation factor β, which represents the instantaneous stress release in the preconditioned rock mass. These parameters are applied to a numerical model, whereby stress redistribution induced by destress blasting is simulated, giving a clue as to the efficiency of destress blasting.

Numerical simulations for rock fracturing by destress blasting are given in Saharan and Mitri (2009) in detail. Their work comes from the ore mining industry; the more important selected conclusions can be summarized as follows:

The critical review of field destress blasting applications indicates that the prevalent destress blasting practice renders more psychological advantages than effective factual destressing. More evidence is available to show that destress blasting efforts do not induce sufficient fracturing in confined brittle rocks.

The analyses of simulations conducted to explore the fracturing pattern involving various differential stresses lead to the conclusion that the fractures align along the principal stresses. The extent of fracturing is greater along the major principal stress directions.

The analyses indicate that the extent of fracturing was ineffective due to the high confinement associated with higher depths (2000 m).

Simulations of recommended borehole spacing predict the extents of fractures are insufficient to meet the neighboring boreholes, even at the half borehole spacing suggested by the South African practice (i.e., 1.5 m in a 38-mm borehole diameter).

The analyses of the role of rock properties on rock fracturing predict that the elastic modulus and static tensile strength play a negligible role in the extent of fracturing. These results agree with the previously documented observations. Also, it is observed that the effects of differential stress on the rock fracturing tend to reduce with decreasing rock modulus values. However, this observation could not be corroborated in the absence of available studies.

Simulations validate that the emulsion-type explosive is better suited to stiff brittle rocks than the ANFO-type explosive, though the explosive energy utilization is better with the latter type.

Analyses have shown that decoupling and a directional fracture growth technique of notched boreholes could enhance the explosive energy utilization for effective destressing. Also, the results showed that longer fracture lengths, in a direction other than parallel to the major principal stress, can be obtained by the application of the directional fracture growth technique, such as a notched borehole.

A nondimensional parameter βij (i.e., comparison of stress before destress blasting to after destress blasting) was introduced. Employing the results of this study, it was possible to explain the hitherto inexplicable reported field results, which indicated an increase in the major principal stress values and the elastic modulus as opposed to the expected relaxation. These measurements do not only agree with the reported field observations, but they also explain the stress concentration and rock stiffening effects.

Some limitations to the studies are presented, resulting from the nonevaluation of thermomechanical process and from the selected method of mathematical modeling.

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