IC 9498

INFORMATION CIRCULAR/2007

Department of Health and Human Services

Centers for Disease Control and Prevention

National Institute for Occupational Safety and Health

Proceedings of the International

Workshop on Rock Mass

Classification in Underground

Mining
Information Circular 9498 

Proceedings of the International Workshop on 

Rock Mass Classification in Underground Mining 

Edited by Christopher Mark, Ph.D., P.E., Rimas Pakalnis, P.Eng., and Robert J. Tuchman

DEPARTMENT OF HEALTH AND HUMAN SERVICES 

Centers for Disease Control and Prevention 

National Institute for Occupational Safety and Health 

Pittsburgh Research Laboratory 

Pittsburgh, PA 

May 2007
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CONTENTS Page 

Abstract .........................................................................................................................................................................1 

Rock mass characterization for excavations in mining and civil engineering, by N. Barton.........................................3 

Mechanized excavability rating for hard-rock mining, by Z. T. Bieniawski and B. Celada........................................15 

Rock mass characterization of primary copper ore for caving at the El Teniente mine, Chile, by A. Brzovic and 

 E. Villaescusa ..........................................................................................................................................................25 

Stope design considerations using rock mass classification tools at the Xstrata Zinc George Fisher mine, 

 by G. W. Capes, J. P. Doolan, and L. B. Neindorf ..................................................................................................37 

An integrated approach to support design in underground coal mines, by M. M. Gadde, J. A. Rusnak, and 

 C. Mark....................................................................................................................................................................49 

A rock mass rating scheme for clastic sediments based on geophysical logs, by P. J. Hatherly, T. P. Medhurst, 

 and S. A. MacGregor...............................................................................................................................................57 

Practical experiences with application of the coal mine roof rating (CMRR) in Australian coal mines, 

 by D. Hill.................................................................................................................................................................65 

Use of the mining rock mass rating (MRMR) classification: industry experience, by J. Jakubec and 

 G. S. Esterhuizen .....................................................................................................................................................73 

Classification-based probabilistic design of ground support, by R. B. Langston, H. A. D. Kirsten, G. C. Howell, 

 and J. A. Marjerison ................................................................................................................................................79 

The geological strength index (GSI): a characterization tool for assessing engineering properties for rock 

 masses, by P. G. Marinos, V. Marinos, and E. Hoek...............................................................................................87 

Development and application of the coal mine roof rating (CMRR), by C. Mark and G. M. Molinda.......................95 

Problems with rock classification for empirical and numerical design, by D. Milne ................................................111 

Weak rock mass design for underground mining operations, by R. Pakalnis, T. M. Brady, P. Hughes, 

 C. Caceres, A. M. Ouchi, and M. M. MacLaughlin...............................................................................................119 

Application of the Q-system to Australian underground metal mines, by W. A. Peck and M. F. Lee ......................129 

Rock mass classification in German hard-coal mining: standards and application, by H. Witthaus and 

 N. Polysos..............................................................................................................................................................141 

Numerical modeling procedures for practical coal mine design, by R. K. Zipf, Jr....................................................153 
UNIT OF MEASURE ABBREVIATIONS USED IN THIS REPORT

cm centimeter m3 cubic meter 

ft foot m/day meter per day 

g/cm3 gram per cubic centimeter m/s meter per second 

g/t gram per ton min minute 

GPa gigapascal mm millimeter 

hr hour MN/m meganewton per meter 

in inch MPa megapascal 

kg kilogram mt metric ton 

kJ/m3 kilojoule per cubic meter mt/m metric ton per meter 

km kilometer Mt million tons 

km2 square kilometer N/m newton per meter 

km/s kilometer per second N/m/m newton per meter per meter 

kN kilonewton N/mm2 newton per square millimeter 

kN/m kilonewton per meter psi pound-force per square inch 

kN/m2 kilonewton per meter squared rps revolution per second 

ksi 1,000 psi t/m ton per meter 

L/min liter per minute t/m2 ton per square meter 

L/sec liter per second tnf ton of force 

lb pound tnf/m2 ton of force per meter squared 

m meter μsec/m microsecond per meter 

m2 square meter 
PROCEEDINGS OF THE INTERNATIONAL WORKSHOP ON 

ROCK MASS CLASSIFICATION IN UNDERGROUND MINING 

Edited by Christopher Mark, Ph.D., P.E.,1 Rimas Pakalnis, P.Eng.,2 and Robert J. Tuchman3

ABSTRACT 

 Rock mass classification is widely used throughout the underground mining industry—in both coal and hardrock mines. It is used in all stages of the mining process, from site characterization to production operations. The 

goal of the International Workshop on Rock Mass Classification in Underground Mining was to provide a forum for 

leading practitioners of rock mass classification to come together and share their methods and experiences with the 

technique. The workshop was held in Vancouver, British Columbia, Canada, on May 31, 2007. It was co-chaired by 

Christopher Mark, Ph.D., P.E., National Institute for Occupational Safety and Health, Pittsburgh, PA, and Rimas 

Pakalnis, P.Eng., University of British Columbia, Vancouver, Canada. 

 The proceedings of the workshop contain 16 invited papers from 9 countries, reflecting the international depth 

and breadth of current practice. Applications in both hard-rock and coal mining are well represented. Some of the 

topics that were addressed at the workshop include: 

 • Major rock mass classification systems used in mining and their variants 

 • Collection of input data through observation, rock testing, and geophysics 

 • Design of mine layouts and rock support systems using classification 

 • Estimation of rock mass strength and other input parameters for numerical models from classification 

 • Applications in weak rock, raise boring, cavability assessment, and other special topics 

 • Risk assessment using rock mass classification 

 

 1Principal research mining engineer, Pittsburgh Research Laboratory, National Institute for Occupational Safety and Health, 

Pittsburgh, PA. 

 2

Associate professor of mining engineering, University of British Columbia, Vancouver, British Columbia, Canada. 

 3

Technical writer-editor, Writer-Editor Services Branch, Division of Creative Services, National Center for Health Marketing, 

Coordinating Center for Health Information and Service, Centers for Disease Control and Prevention, Pittsburgh, PA. 

3

ROCK MASS CHARACTERIZATION FOR EXCAVATIONS 

IN MINING AND CIVIL ENGINEERING 

By Nick Barton, Ph.D.1

ABSTRACT 

 When the Q-system was launched in 1974, the name 

referred to rock mass classification, with focus on tunnel 

and cavern support selection. Besides empirical design 

of support, the Q-value, or its normalized value Qc, has 

been found to correlate with seismic P-wave velocity, 

with deformation modulus, and with deformation. The 

Q-system provides temporary or permanent support for 

road, rail, and mine roadway tunnels and for caverns for 

various uses. It also gives relative cost and time for 

tunnel construction for a complete range of rock 

qualities. There are also indications that Q has captured 

important elements of the cohesive and frictional 

strength of rock masses, with Qc resembling the product 

of rock mass cohesion and rock mass friction coefficient. 

ROCK MASS VARIABILITY 

 From the outset, the Q-system has focused on sound, 

simple empiricism that works because it reflects practice 

and that can be used because it is easily remembered. It is 

appropriate to start by illustrating the widely contrasting 

rock mass qualities that may challenge both the civil and 

mining professions, fortunately not on a daily basis, but 

therefore also unexpectedly. 

 Figure 1 shows a core box from a project that has not 

been completed during 10 years of trying. The massive 

core is from a project that may not be started for at least 

10 years. The first should already have passing high-speed 

trains; the other may have high-level nuclear waste some 

time in the future. They are both from the same country, 

but may have six orders of magnitude contrast in Q-value. 

A second pair of examples shown in Figure 2 requires a 

cable car for access on the one hand, and successive boat 

trips to fault-blocked flooded sections of tunnel on the 

other. 

 The contrasting stiffness and strength of intact rock 

and wet clay is easy to visualize. One may be crushed by 

one and drowned in the other. There are sad and multiple 

examples of both in the tunneling and mining industries. 

They merit a widely different quality description, as for 

instance given by the wide range of the Q-value. 

 

1

President, Nick Barton & Associates, Høvik, Norway. 

 Figure 2.—Respective access by cable car and by boat, 

emphasizes the need for radically different magnitudes of 

rock quality and also radically different magnitudes of 

seismic quality, the inverse of attenuation [Barton 2006]. 

A single project beneath Hong Kong harbor demonstrated a 

length of core of 57 m without a joint and an even wider 

regional fault zone. With such extremes, RQD values of 100% 

and 0% are clearly inadequate, too, but can clearly be 

improved by using local Q-values of, for example, 1000 and 

0.001.

 Figure 1.—The contrast shown by these two core boxes 

suggests orders of magnitude differences in quality. Quantitative descriptions of shear strength and deformation modulus would vary by orders of magnitude as well. Quality 

descriptors like RMR or GSI that suggest qualities differing 

from only 5 to 95, or 10 to 90, cannot then be as appropriate 

as the 0.001–1000 range of Q seen in these examples. 

Increasing the range of Q to Qc adds further reality, since 

Qc might range from 0.0005 to 2500 in these two cases. 
4 

 The term Q is composed of fundamentally important 

parameters (Figure 3) that were each (besides Deere’s 

Rock Quality Designation (RQD)) quantified by exhaustive case record analysis. The six orders of magnitude 

range of Q is a partial reflection of the potentially enormous variability of geology and structural geology. It is 

probably because of the relative sensitivity of a classification that can show wide numerical variation (i.e., 10–3 to 

103

, or an even wider range using Qc = Q × σc/100) that 

correlation with a very varied geologic and hydrogeologic 

reality is achieved, using rather simple correlations. 

Without this range of Q (approximately 106

) or Qc

(approximately 109

), correlation would be more complex, 

as it seems to be with the Geological Strength Index (GSI) 

in particular, since this is based on the limited numerical 

range of the Rock Mass Rating (RMR) system. 

 

COMMON ASPECTS OF OVERBREAK 

AND CAVABILITY IN MINING 

 Figure 4 shows the fundamental importance of the 

number of sets of joints and their roughness using the Qsystem parameters Jn (number of sets) and Jr (roughness). 

This figure also shows overbreak (and therefore large-scale 

cavability, in principle) caused by three sets of joints, but 

with the important proviso that without a degree of joint 

surface planarity, neither overbreak in a tunnel/roadway, 

nor block caving are likely to occur without significant 

assistance. 

 It is quite likely that, whatever the overall Q-value at a 

given (potential) block caving locality in an ore body, the 

actual combination Jn/Jr will need to be ≥6 for successful 

caving (e.g., 6/1, 9/1.5, 12/2), while such combinations as 

9/3 might prove to be too dilatant. Even four joint sets 

(Jn = 15) with too high Jr (such as 3) would probably 

prejudice caving due to the strong dilation and need for a 

lot of long-hole drilling and blasting. Significantly, this 

last ratio (15/3 = 5) is also <6. 

 The simple Jr description shown in Figure 4, when 

combined with Ja, also gives a realistic estimate of the 

interblock friction angle, as illustrated in Figure 5. These 

parameters form part of the first and second pairs of 

parameters describing the Q-value. (RQD/Jn = relative 

block size, Jr/Ja = interblock friction coefficient). Obviously, a combination of Jn = 6 to 9 (or more), and Jr = 2, 

1.5, or less, and Ja ≥1 would be ideal attributes for block 

caving and equally unfavorable for overbreak and tunnel 

or cavern support needs, where permanent, or temporary, 

stability was required. 

 Figure 3.—A pictorial representation of the Q-parameters, 

from Hutchinson and Diederichs [1996]. The modern application of the Q-system [Barton 2002] includes both indirect and 

direct use of UCS. When the rock strength-to-stress ratio is 

unfavorable, in the case of massive (high RQD/Jn) rock 

masses, the SRF value will need to be very high to represent 

excavation difficulties, i.e., deep-shaft excavation, due to 

potential stress slabbing or minor bursting. The high SRF 

and correspondingly low Q-value require heavy yielding 

support. In the case of jointed rock under high stress, SRF 

will not need to be so high. 

SRF

J

J

J

J

RQD Q w

a

r

n

= × ×

 Figure 4.—Two of the most important components of Q 

and of rock mass stability are the number of joint sets (or 

degree of freedom for block definition and fallout) and the 

joint roughness (or interblock release-or-hold mechanism). 

The general level of overbreak and ease of carrying out 

characterization in tunnels are also fundamentally affected 

by these two parameters. In the case of block caving in 

mining, the ratio Jn /Jr is fundamental for initiating such 

mechanisms. 
5 

CONSEQUENCES OF OVERBREAK IN A CAVERN 

AND IN A MINE 

 The 62-m span Gjøvik Olympic cavern in Norway 

typically had Jn/Jr of 9/2, and it was stable with the help of 

bolting and shotcrete and a favorable (horizontal) stress 

situation. Although it suffered significant overbreak, Jn/Jr

of 9/2 prevented caving, i.e., locally excessive overbreak. 

With Jn/Jr of 9 (three sets) and Jr of 1 (planar), the project 

would have had another degree of complexity, i.e., it 

would have caved at the very wide face, without close-tothe-face rock support. Of course, the magnitude of RQD 

(where RQD/Jn represents relative block size) and the 

details of Ja (possible clay-filling or soft mineralization) 

will modify the above simplicity; hence the use of Q. 

 The typical Gjøvik cavern Jn/Jr ratio (9/2 = 4.5), 

although not allowing caving (or uncontrolled overbreak) 

during blasting, did allow overbreak of 1 to 2 m, as seen 

through the 10 cm of S(fr) in the 62-m span arch seen 

about 20 m above the camera location in Figure 6. In such 

overbreak locations, there was invariably a local Jn = 12–15 

(up to four sets) character, with Jr of about 2, i.e., a ratio of 

Jn/Jr of ≥ 6. This implies the likely need for support in civil 

engineering excavations and mine roadways, while in the 

case of block caving, could signal relative ease of caving 

initiation, as shown in the following example. 

 In the steeply inclined Kiruna ore body in Sweden, the 

Q-system parameters were evaluated by systematic logging in >2 km of upper-level long-hole drilling galleries 

and in corresponding ore-loading galleries at the base of 

the proposed LKAB Oscar (long-hole block caving) project [Barton 1988]. Roughness characterization of the 

jointing exposed by failed zones (extreme overbreak) in 

the drifts due to inadequate temporary support was used to 

evaluate the possibility of larger-scale block release and 

caving disruption, as illustrated schematically at the top of 

Figure 7 (see “J1” and “J2” areas). 

 As may be noted from the Q-histogram logging 

illustrated in Figure 7, the most frequent Jn/Jr ratings were 

9/2. This proved insufficient for unassisted caving. Drift 

failures (few) tended to have occurred with Jn/Jr of, for 

example, 12/1.5, sometimes with the additional facilitation 

of Ja = 3, i.e., mineralized joints. 

 The above ratings for the various joint and rock mass 

parameters for the two example projects illustrate behavior 

with respect to overbreak and cavability—the former at 

stress levels of 3–5 MPa (Gjøvik Olympic cavern), the 

latter at 15–20 MPa (Kiruna’s LKAB Oscar project). 

Clearly, high initial and developing stress levels expected 

in a deep mine may give a necessary “boost” to block 

fracturing and interblock friction mechanisms, thereby 

demonstrating the need for block stress-fracturing and joint 

stress-propagation mechanisms, if initiation of caving 

should be modeled. 

 Figure 5.—A graphic demonstration of the workings of Jr

and Ja in the context of joint or clay-filled discontinuity 

friction angles. For minimal overbreak and tunnel support 

needs, Jr /Ja needs to be as large as possible. For block 

caving, a mostly “category a” (rock-to-rock joint wall contact) 

friction angle is expected. The Jr values (the vertical columns 

of ratings on the left side) will need to be 2 or less; otherwise, dilation during shear will stop block caving from occurring, unless block size is small enough for block rotation to 

occur, e.g., Barton [2004]. 

 Figure 6.—Example of a well-jointed rock mass with most 

typically Jn = 9 (three sets of joints) and Jr = 2, seen in the 

Gjøvik Olympic cavern of 62-m span. Note the deep overbreak in the 25-m-high arch and the use of B + S(fr) permanent support [Barton et al. 1994]. 
6 

 These important aspects that are fundamental in 

caving initiation, can be studied by suitable combinations 

of block modeling (UDEC–BB) and block fracture modeling (FRACOD). There are also intimate cross-disciplinary 

links between this modelable “blockiness” and the resulting permeability, or “connectedness,” and the resultant 

effects on seismic attenuation and its inverse Qseis [Barton 

2006]. 

EXTRAPOLATING Q USING SEISMIC 

REFRACTION PROFILES 

 Since there is a limit to how many boreholes can be 

drilled, how many cores can be logged, and how many 

permeability tests can be performed, it is useful to have 

alternative ways of estimating and extrapolating these 

point sources of information. This opinion applies, of 

course, to tunnels and to mining declines that can be 

reached, or almost reached, by boreholes or by deeply 

penetrating seismic refraction, with less constraints on 

energy sources than will be the case with civil engineering 

tunnels near population centers. 

 One may start by looking at correlation between 

velocity and measures of quality. Sjøgren et al. [1979] 

used seismic profiles (totaling 113 km) and local core 

logging results (totaling 2.9 km of core) to derive these 

helpful mean trends for hard rocks (Figures 8–11). 

 Figure 7.—Kiruna’s LKAB Oscar long-hole caving project. 

Some details of rock mass Q-characterization, observations 

of drift collapse and overbreak, and a pre-1993 Q-system 

temporary support assessment (after Barton [1988]). Note the 

early use of Q-parameter histogram logging, a simple method 

of field logging used in the last 20 years. 

 Figure 8.—Hard-rock, shallow seismic refraction. 

Sjøgren et al. [1979] combined 113 km of seismic profiles 

and 2.9 km of core logging to derive these mean trends 

for shallow tunnels. 

 Figure 9.—Hard-rock, shallow seismic refraction, mean 

trends from Sjøgren et al. [1979]. The Q-scale was added 

by Barton [1995] using the hard-rock correlation Vp ≈ 3.5 + 

log Q. By remembering Q = 1: Vp ≈ 3.5 km/s, and Vp = 

3 km/s: Q ≈ 0.3, the Q–Vp approximation to a wide range of 

near-surface qualities is at one’s fingertips (e.g., for hard, 

massive rock: Q = 100: Vp ≈ 5.5 km/s, and when Vp = 

5 km/s: Q ≈ 30). 
7 

 

 Figure 10.—An integrated empirical model for linking Q-value (via Qc) to P-wave velocity, depth, matrix porosity, deformation modulus, and approximate support pressure (based on a mean Jr = 2). With this simplification, the independently derived 

Barton et al. [1974] support pressure formulation and the Barton [1995] deformation modulus formulation suggest inverse 

proportionality between support pressure and deformation modulus. This is logical, but the simplicity is nevertheless surprising [Barton 2002].

 Figure 11.—The depth-velocity trends for different Qc values. This graph explains why faulted rock ahead of a deep tunnel 

may sometimes be invisible or of such high velocity, like 4 km/s, that it is misinterpreted. It may subsequently cause tunnel 

collapse or trap a tunnel boring machine. In fact, such rock is still probably displaying an important contrast to the surrounding rock mass. In the case of soft rock, acoustic closure prevents such differentiation. In general, “Q-jumping” will be 

experienced when progressing downward to greater depth, i.e., rock qualities tend to improve, giving steeper Vp /depth (s–1) 

gradients [Barton 2006]. 
8 

TUNNEL AND DECLINE SUPPORT 

WHEN UNDER HIGH STRESS 

 The Q-system was developed from mostly civil engineering case records. Nevertheless, there are many tens of 

kilometers of semipermanent drifts and declines in most 

mines that have fundamentally similar needs to civil 

engineering tunnels, at least in their early years of life, 

before they become seriously affected by subsequent stress 

changes caused by the advancing mining front. In principle, one needs to design support (or select the correct 

support class) by classifying each round as the permanent 

mining drift or decline is driven. This is also a familiar 

task in civil engineering tunnels. 

 In mining situations, however, one may need to allow 

for future stress changes and deformations if a present 

location will soon become close to the mining front. The 

civil engineering approach of B+S(fr) for permanent 

roadways may need to be supplemented with longer fibers 

and probably the addition of mesh and cable bolts. 

 One must be prepared to reclassify and resupport if or 

when stress changes cause the need for rehabilitation due 

to observed deformation and cracking. The advancing 

mining fronts will tend to change the stress reduction 

factor (SRF), possibly to a dramatic level, causing apparent reductions to RQD and apparent or even real increases 

to Jn due to stress-fracturing effects. The addition of mesh 

and longer cable bolts for tolerating larger deformations 

will usually be a part of this subsequent phase. Longer 

fibers from the start that tolerate larger strains would be a 

logical difference of approach between civil and mining 

applications of B+S(fr) that might delay rehabilitation 

requirements. 

 Increases of SRF due to increased ratios of σ1/σc with 

the advancing mining front might follow the changes suggested by Grimstad and Barton [1993] for the case of 

mining drifts in massive rock surrounding an ore body. 

The 1993–1994 (slightly) updated SRF ratings for high 

stress are shown in Table 1. If, on the other hand, the high 

stresses caused by mining depths or advancing mining 

fronts are acting on distinctly jointed rock, as experienced 

for instance in Western Australia, then the equations proposed by Peck [2000] are recommended. These are based 

on the original SRF values of 1974. 

 The exponential relationship derived by Peck [2000] 

from the original SRF ratings of Barton et al. [1974] is as 

follows: 

 SRF = 34(σc /σ1)

–1.2 (1) 

For strongly anisotropic stress fields, if measured, Peck 

[2000] derived the following best-fit equation from the 

Barton et al. [1974] suggestion of downgrading of σc with 

strong stress anisotropy: 

 SRF = 31(σ1 /σ3)

0.3(σc /σ1)–1.2 (2) 

Table 1.—Excerpt from updated SRF ratings based on Grimstad and Barton [1993], with additional notes from Barton [2002]. 

Case Competent rock, rock stress problems σc /σ1 σθ /σc 

Stress reduction

factor (SRF) 

H ............ Low-stress, near-surface, open joints. >200 <0.01 2.5 

J ............. Medium stress, favorable stress condition. 200–10 0.01–0.3 1 

K ............ High-stress, very tight structure. Usually favorable to stability, 

may be unfavorable for wall stability. 

10–5 0.3–0.4 0.5–2 

L............. Moderate slabbing after >1 hr in massive rock. 5–3 0.5–0.65 5–50 

M............ Slabbing and rock burst after a few minutes in massive rock. 3–2 0.65–1 50–200 

N ............ Heavy rock burst (strain burst) and immediate dynamic 

deformations in massive rock. 

<2 >1 200–400 

NOTES: 

1. For strongly anisotropic virgin stress field (if measured): When 5 ≤ σ1 /σ3 ≤ 10, reduce σc to 0.75 σc. When σ1 /σ3 > 10, reduce σc

to 0.5 σc, where σc = unconfined compression strength, σ1 and σ3 are the major and minor principal stresses, and σθ = maximum 

tangential stress (estimated from elastic theory). 

2. Few case records available where depth of crown below surface is less than span width. Suggest an SRF increase from 2.5 to 5

for such cases (see case H). 

3. Cases L, M, and N are usually most relevant for support design of deep tunnel excavations in hard massive rock masses, with 

RQD/Jn ratios from about 50 to 200. 

4. For general characterization of rock masses distant from excavation influences, the use of SRF = 5, 2.5, 1.0, and 0.5 is recommended as depth increases from, say, 0–5 m, 5–25 m, 25–250 m, to >250 m. This will help to adjust Q for some of the effective 

stress effects, in combination with appropriate characterization values of Jw. Correlations with depth-dependent static deformation modulus and seismic velocity will then follow the practice used when these were developed. 
9 

STANDUP TIME USING Q–RMR CONVERSION 

 There is such widespread use of RMR, often in 

parallel with Q, that it is appropriate to address a possible 

interrelationship between the two. This, of course, has 

been the subject of many publications. One camp uses the 

“ln” (natural logarithm) format shown in Equation 1 in 

Figure 12; the other uses the “log” format shown in 

Equation 2 in this figure. Since the latter format is simpler 

and probably gives a more logical range of RMR in relation to the Q-scale (avoiding the negative values that occur 

below Q = 0.01), it has been used by the author also in 

relation to standup time and in relation to deformation 

modulus conversion between the two systems. Since we 

are engineers and not scientists, our craft is the ability to 

make realistic approximations, leaving unnecessary decimal places on the calculator. 

 The conversion between RMR and Q used to estimate 

standup time is based on Figure 12 (Equation 2). Figure 13 

suggests that when the Q-value is as low as, for example, 

0.01, or RMR is as low as 20, the standup time for a <1-m 

advance (beyond the last support) may be a matter of minutes, with collapse imminent or immediate if an advance of 

2–3 m was made by an excessive length of blasting round. 

SOME CHARACTERIZATION LESSONS FROM 

SITE INVESTIGATIONS 

 Lessons learned and Q-logging techniques applied 

when investigating ground conditions and modeling 

planned excavation and support of a mine-size cavern will 

now be reviewed. The case record is the 62-m span Gjøvik 

Olympic cavern in Norway. Reference will also be made 

to some of the Q-correlations given earlier in this paper. 

 Figure 12.—The Q-support chart from the Grimstad and Barton [1993] update for S(fr) in place of S(mr). 

(NOTE: Equation 2 (inset) avoids unwanted negative values of RMR when Q < 0.01.) ESR = excavation support ratio.
10 

 A start will be made with the comparison of Qhistogram-based core logging (see cross-hatched area in 

Figure 14) and the Q-logging performed in existing nearby 

excavations (see black area in Figure 14). Note the “tail” 

on the RQD distribution, as logged in the case of the core 

logging, and the lack of a tail when Q-logging in existing 

excavations. This was caused by lack of Q-parameter data 

where shotcrete covered the poorest rock when logging the 

existing excavations. 

 The boreholes used for core recovery were permeability tested (K mostly ≈ 10–7 to 10–8 m/s) and were also 

used for crosshole seismic tomography. Two examples are 

shown in Figure 15. The expected increase in velocity with 

depth, from about 3.5 to 5.0 km/s, is shown. What was 

 Figure 13.—Bieniawski [1989] standup time estimations. Note the Q-value approximations (large numbers 

next to small RMR numbers). “Roof span” refers to distance from the last support to the (new) tunnel face. 

 Figure 14.—Q-histogram logging of core (four holes) 

and existing local excavations (black), performed by 

different Norwegian Geotechnical Institute Q-loggers at 

different times [Barton et al. 1994]. 

 Figure 15.—Crosshole seismic tomography between two pairs of holes at the Gjøvik cavern site prior to cavern location 

decisions. An expected increase in velocity with depth is indicated, but in this particular case, the rock quality of the gneiss 

did not noticeably improve. 
11 

unexpected was that the rock quality (RQD, Fm–1, and Q) 

did not show a corresponding general increase in quality, 

as can be ascertained by studying Figure 16, which shows 

the velocities interpreted close to one of the boreholes. 

 The Q–Vp depth models shown earlier in Figures 10–

12 indicated how velocity increase can occur without the 

need for Q-value increase. However, between 25 and 

50 m, the predicted increase in velocity is relatively minor, 

such as 4.5–5.0 km/s. The increase of closer to 2 km/s 

between 10 and 60 m depth, shown in Figure 16, may be 

explained by the measured horizontal stress increase, 

which was as much as 5 MPa over this same limited depth 

range. 

 This increase, with little assumed change in rock 

quality, is possible due to the increased interlock of the 

rough conjugate jointing (high Jr and joint roughness 

coefficient (JRC)) and the relatively sound tectonized 

gneiss, with UCS about 90 MPa and joint compressive 

strength (JCS) about 75 MPa. In softer rock like chalk, 

acoustic closure (in relation to Vp) would occur at much 

shallower depths than this [Barton 2006]. 

INPUT DATA FROM 

ROCK MASS CLASSIFICATION 

 In the late 1960s, there was a movement in some rock 

mechanics circles to try to move beyond the confines of 

continuum modeling and focus on the possible effects of 

jointing on the performance and reinforcement needs of 

rock excavations, whether they be tunnels, slopes, or dam 

abutments. Thanks to the late-1960s modeling developments of R. E. Goodman and his colleagues with joint 

elements in finite-element codes, followed by P. A. 

Cundall in the early 1970s, first with μDEC, then UDEC, 

and later with 3DEC, this focus could be fulfilled by an 

increasing number of rock mechanics practitioners around 

the world. However, using these codes correctly, with 

realistic input data, needs experience, time, and therefore 

budgets to match. Ironically, input data for some 

continuum codes now seem to be considerably more 

complex than for discontinuum codes, as suggested in 

Figure 17. 

 GSI-based Hoek-Brown formulations for “simple” 

geotechnical input data for the rock mass, shown in Figure 

17, such as deformation modulus, cohesion, and friction 

angle, have reached “black box” levels of complexity, 

which seems to be detrimental to the idea of rock engineering if engineering judgment is still to be exercised in 

this rewarding field of engineering. 

 There is no possibility to have any feel for the influence of local rock quality on the rock mass compression 

strength, friction angle, or cohesion when formulations 

require software rather than estimation for their evaluation. 

The formulas on the left of Figure 17 cannot be considered 

“empirical” anymore, with the exception of the first 

equation for estimating modulus. 

 Presumably as a result of time and budgetary pressures, as well as the developing need to model large-scale 

mining problems, there has been a marked trend for using 

“convenient” continuum codes, which also have particularly good graphic representation of results. Simple 

software packages for handling the complex input data 

calculations (Figure 17) are also provided so that a smart 

user might theoretically need only limited understanding of 

rock mechanics principles to use the codes successfully. 

 The author has often used the method of rapidly leftthumbing from the back of a consultant’s report to the 

front, whereby the colored appendices of endless stress 

distributions and deformation patterns can be read almost 

as in a film. Does all this color represent anything real? 

 Figure 16.—Note the lack of a general rock quality 

improvement with depth compared to the consistent rise 

in P-wave velocity. The Q-value logged down the holes 

mostly varied between 1 and 30, with a mean of 10–12 and 

showed no tendency for improved quality below about 5 m 

[Barton et al. 1994]. 

 Figure 17.—The extraordinarily complex formulas (left)

for developing input data for some recent continuum 

models compared with some of the less developed and 

equivalent Q-based formulas. 
12 

Would the numerical modelers know how to input a 

neglected clay seam without “smoothing it out” in a continuum approximation? Would the complex estimates of c′

and φ′ in Figure 17 change very much? 

CC AND FC: THE COHESIVE AND FRICTIONAL 

COMPONENTS OF QC 

 On the right side of Figure 17, simple Q-based equations for c and φ are shown that are actually found to be 

composed of each half of the Qc-formulation. They have 

the advantage of not requiring software for their calculation—they already exist in the calculation of the Qc value. 

They are defined as follows: 

 cohesive component (CC) = RQD/Jn × 1/SRF × σc /100 

 frictional component (FC) = tan–1[Jr /Ja × Jw] 

Examples of these rock mass component strengths are 

given in Table 2 for a range of possible Q-values for 

increasingly jointed rock masses. 

 The P-wave velocity and (pseudostatic) deformation 

modulus estimates in Table 2 are from the central 

diagonal, near-surface (25-m depth) interrelationships 

given in Figure 10. They could equally well be quoted for 

greater depths, if more relevant. Some physical examples 

of rock masses with different CC and FC characteristics 

are shown in Figure 18. 

 Plate loading tests taken to such high stress levels that 

rock mass failure occurs are rare. However, measurement 

of P-wave velocity at such sites may allow tentative 

extrapolation to other sites through a common rock mass 

quality estimate. Such data can then be a source of tentative rock mass strength (σc mass) estimation. 

 Table 3 suggests compressive (and cohesive) strengths 

in rock masses somewhat higher than those usually 

assumed. They also show some implicit variation from the 

values set up in Table 2 (from specific Q-parameter 

combinations), but reinforce the idea of potentially very 

high cohesive strengths (e.g., tens of MPa) in competent 

rock masses. This table of values seems to imply very 

different values of cohesion from some of the earlier 

RMR-based estimates of cohesion for rock masses, where 

c was generally given as <1 MPa for a wide range of 

RMR. 

 Figure 18.—Examples of rock masses with particularly low CC (left) and particularly low FC (right). These 

require relatively more shotcrete (left) and relatively more bolting (right). The original Q-system case records have 

apparently reflected these different needs, and the Q-parameter ratings developed have given the possibility of 

realistic CC and FC values. 

Table 2.—Five hypothetical rock masses with reducing quality from top to bottom of the table. 

(Note the difference between Q and Qc due to normalization by σc /100. The sensitive, logical values of FC and CC 

already exist in the Qc calculation, requiring no further empiricism.) 

RQD Jn Jr Ja Jw SRF Q σc Qc FC, ° CC, 

MPa 

Vp, 

km/s 

Emess, 

GPa 

100........................ 2 2 1 1 1 100 100 100 63 50 5.5 46 

90.......................... 9 1 1 1 1 10 100 10 45 10 4.5 22 

60.......................... 12 1.5 2 0.66 1 2.5 50 1.2 26 2.5 3.6 10.7 

30.......................... 15 1 4 0.66 2.5 0.13 33 0.04 9 0.26 2.1 3.5 

10.......................... 20 1 6 0.5 5 0.008 10 0.0008 5 0.01 0.4 0.9 
13 

CONCLUSIONS 

1. Q-system linkages to parameters useful for design 

are based on sound, simple empiricism that works 

because it reflects practice and that can be used 

because it can be remembered. It does not require 

black-box software evaluation. 

2. The wide range of Q-values (0.001–1000) reflects to 

some degree the very wide range of geological conditions and is probably responsible for the fact that 

empirical equations based on the Q-value or on Qc

are particularly simple. 

3. The Q-parameters Jn and Jr are very useful for 

evaluating overbreak potential and cavability in mining. When Jn/Jr ≥ 6, significant overbreak will tend 

to occur, unless limited by timely support close to 

the excavation face. Caving is also likely to occur 

relatively unassisted. A modifying factor is, of 

course, the ratio Jr/Ja, representing frictional strength. 

Stress and water pressure are final modifiers. 

4. An integration of the Q-value with seismic and 

permeability data has been developed because there 

is a limit to how many boreholes can be drilled, how 

many cores can be logged, and how many permeability tests can be performed. The ability to 

extrapolate these point sources of information helps 

to project rock quality classes along a tunnel or to 

different parts of a large cavern or mine. 

5. Due to the effect of increased stress at greater tunnel 

or cavern depth, it must be expected that deformation modulus and seismic velocity will increase. 

Eventual sonic logging or crosshole tomography 

ahead of a tunnel face may therefore give a higher 

velocity than the rock quality may suggest. 

6. Strength criteria of the form “c + tan φ” used in 

continuum codes, with links to GSI, have recently 

acquired remarkable complexity and require software for evaluation of their components. The terms 

CC and FC from the Q-calculation show promise in 

giving a direct preliminary estimate of the magnitudes of rock mass cohesive and frictional strength. 

Logic would suggest that these components should 

also not be added in an eventual failure criterion. 

REFERENCES 

 Barton N [1995]. The influence of joint properties in 

modelling jointed rock masses. Keynote lecture at the 

Eighth International Society for Rock Mechanics Congress 

(Tokyo, Japan), 3:1023–1032. Rotterdam, Netherlands: 

Balkema. 

 Barton N [1988]. Project Oscar – LKAB/LuH: Estimation of shear strength parameters for mine design 

studies. Oslo, Norway: Norwegian Geotechnical Institute, 

NGI report 87659–1. 

 Barton N [2002]. Some new Q-value correlations to 

assist in site characterization and tunnel design. Int J Rock 

Mech Min Sci 39(2):185–216. 

 Barton N [2004]. Failure around tunnels and boreholes 

and other problems in rock mechanics [letter]. ISRM News J 

8(2):12–18. 

 Barton N [2006]. Rock quality, seismic velocity, 

attenuation and anisotropy. London and Netherlands: 

Taylor & Francis. 

 Barton N, By TL, Chryssanthakis P, Tunbridge L, 

Kristiansen J, Løset F, et al. [1994]. Predicted and measured performance of the 62-m span Norwegian Olympic 

ice hockey cavern at Gjøvik. Int J Rock Mech Min Sci 

Geomech Abstr 31(6):617–641. 

 Barton N, Lien R, Lunde J [1974]. Engineering classification of rock masses for the design of tunnel support. 

Rock Mech 6(4):189–236. 

 Bieniawski ZT [1989]. Engineering rock mass classifications: a complete manual for engineers and geologists 

in mining, civil and petroleum engineering. John Wiley & 

Sons, Inc. 

 Grimstad E, Barton N [1993]. Updating of the Qsystem for NMT. In: Proceedings of the International 

Symposium on Sprayed Concrete – Modern Use of Wet 

Mix Sprayed Concrete for Underground Support. Oslo, 

Norway: Norwegian Concrete Association. 

 Hutchinson DJ, Diederichs MS [1996]. Cable bolting 

in underground mines. Bitech Publishers, Ltd.: Richmond, 

British Columbia, Canada. 

 Peck W [2000]. Determining the stress reduction factor in highly stressed jointed rock. Aust Geomech 35(2). 

 Savich AI, Koptev VI, Zamakhaiev AM [1974]. 

In situ ultrasonic investigation of failure of limestone. In: 

Proceedings of the Third International Society for Rock 

Mechanics Congress (Denver, CO), IIA:418–423. 

 Sjøgren B, Øfsthus A, Sandberg J [1979]. Seismic 

classification of rock mass qualities. Geophys Prospect 27: 

409–442. 

Table 3.—Plate load tests driven to failure, with 

corresponding velocity and modulus data 

for the different rock masses 

(Savich et al. [1974]; see Barton [2006] for other data sets)

Velocity Vp (km/s) ...................... 2.3 3.7 4.0 

Modulus Emass (GPa) ................. 1 3 15 

Rock mass σcm (MPa) ............... 4 20 50 

15

MECHANIZED EXCAVABILITY RATING FOR HARD-ROCK MINING 

By Z. T. Bieniawski, D.Sc. (Eng),1

 and Benjamín Celada, Ph.D.2

When you can measure what you are speaking about, 

and express it in numbers, you know something about it; 

but when you cannot measure it, when you cannot 

express it in numbers, your knowledge is of a meager 

and unsatisfactory kind. 

 —Lord Kelvin (1824–1907)

ABSTRACT 

 After emphasizing the importance of quantitative rock 

mass classifications in mining, originally directed to selection of rock support measures, but subsequently to estimates of rock mass properties such as rock mass strength 

and rock mass modulus of deformation, current attention 

calls for a classification specifically for rock mass 

excavability by tunnel boring machines (TBMs), which are 

used extensively in tunneling as well as in the mining 

industry. 

 This paper introduces the Rock Mass Excavability 

(RME) index for predicting excavability of rock masses by 

TBMs using a quantification of machine performance and 

rock mass conditions. The RME index is based on five 

input parameters aimed at relating rock mass behavior and 

machine characteristics: (1) uniaxial compressive strength 

of the rock material, (2) drillability/abrasivity, (3) rock 

mass jointing at mine drift face, (4) standup time of the 

excavation, and (5) groundwater inflow. 

 Development of the RME index entailed the collection 

of extensive data from more than 28 km of tunnels and 

some 400 case records from projects in Spain involving 

double-shield TBMs. In the process, a number of statistical 

correlations have been established between RME and such 

output parameters as degree of machine utilization, 

advance and penetration rates, thrust and torque of the 

cutterhead, and the specific energy of excavation. It was 

found that the RME index provides a particularly significant correlation for predicting the average rate of advance 

(m/day). 

 In essence, the RME index is a classification system 

that features interaction of rock mass conditions with boring machine characteristics for use in the early stages of 

a project. 

 It should be noted that the RME index does not 

replace the Rock Mass Rating (RMR) or Q-systems as 

used in mining and tunneling; indeed, one of the RME 

input parameters, standup time, is determined from the 

RMR. However, the approach presented introduces a 

specialized tool relevant to excavating tunnels and drifts. 

Possible applications to hard-rock mining are explored. 

INTRODUCTION 

 Rock mass classifications, although originally developed for rock tunneling in civil engineering, have been 

used in mining for some 35 years, going back to RMR 

applications in South African hard-rock and coal mining 

[Bieniawski 1972; Laubscher 1976]. In the United States, 

research investigations by Kendorski et al. [1983] for hardrock mining, based on RMR, as well as for coal mining 

by Unal [1983] for roof support and Kalamaras and 

Bieniawski [1995] for pillar design, also based on RMR, 

were highly innovative, and their results are used to this 

day. 

 More recently, attention has been paid to rock mass 

classifications aimed at determining rock mass properties, 

i.e., rock mass strength and the rock mass modulus of 

deformation. Examples of particularly useful charts for this 

purpose are presented in Appendix B of this paper. 

 Lately, as machine-bored excavations in tunneling 

become more common than drill-and-blast tunneling, 

a need emerged for predicting the performance of tunnel 

boring machines (TBMs) based on considerations of 

interaction of rock mass conditions and the TBM operational parameters. If successful, such findings would be of 

equal interest to mining applications. 

PREVIOUS STUDIES 

 When one considers the history of underground excavation technology, its development, and the major milestones, the emergence and increasing use of modern TBMs 

provided both spectacular advantages and achievements, as 

well as complex challenges and problems to designers and 

constructors who faced significant shortcomings in our 

understanding of the interaction of rock mass conditions 

and TBM design and performance. 

 In fact, when Terzaghi introduced his rock load concept in 1946, followed by Lauffer’s standup time concept 

in 1958 and Deere’s Rock Quality Designation (RQD) in 

1964, these design approaches were directed to selection of 

rock reinforcement for tunnel construction by drilling and 

 

 1Professor, Superior School of Mines, University of Madrid, 

Spain, and President, Bieniawski Design Enterprises, Prescott, 

AZ. 

 2

Professor, Superior School of Mines, University of Madrid, 

Spain, and President, Geocontrol, S.A., Madrid, Spain. 
16

blasting. The equipment selected for tunnel excavation was 

left to the discretion of the contractor, with little input by 

the designer. Even subsequent modern rock mass classification methods [Bieniawski 1973; Barton 1974] were predominantly directed to drill-and-blast tunnels, independent 

of TBM characteristics. 

 Today, this is no longer the case. TBMs have 

increased in power, size, and type to such an extent that 

they directly influence tunnel design. Moreover, their 

selection is a source of tremendous satisfaction due to 

increased safety and higher performance, as well as deep 

despair when unexpected ground conditions are encountered and the TBM may be immobilized for months and 

sometimes has to be rescued by old-fashioned hand mining 

or conventional drill-and-blast excavation. 

 A major problem emerged: how to assess effectively 

the interaction between rock mass conditions, as described 

by the RMR or Q classification systems, and the design 

and performance characteristics of the TBM. Certainly, 

some attempts to solve this problem have been made, as 

reviewed below, but the state of the art still rests on the 

TBM manufacturers and tunnel contractors that must rely 

on their experience, ingenuity, and even the will to survive 

many adverse conditions. 

STATE OF THE ART IN ROCK EXCAVABILITY 

 Excavability is defined as the ease of excavation and 

was investigated as early as Kirsten [1982]. TBM 

excavability or performance prediction models were 

studied by Barton [2000], Alber [2001], Bieniawski 

[2004], Blindheim [2005], and others. 

 In essence, it is recognized that the choice between a 

TBM and drilling and blasting can be quantified based on 

rock mass quality and machine characteristics. An example 

of an interdependence function is the QTBM formulation 

[Barton 2000]: 

 QTBM = RQD0/Jn × Jr /Ja × Jw /SRF 

 × σMASS/F × 20/CLI × q/20 (1) 

where CLI = cutter life index (Norwegian Institute of 

Technology), SRF = stress reduction factor, F = average 

cutter load (tnf), q = quartz content (%). 

 Equation 1 received much attention, but was also 

severely criticized [Blindheim 2005]. In this research, the 

above relationship was also tested, but without success 

because of the problem with the definition of rock mass 

strength, σMASS, which is based on “inversion of σc to a 

rock mass strength, with correction for density,” rendering 

it unacceptable. Nevertheless, Abrahão and Barton [2003] 

applied this equation with all 21 parameters (“for which no 

apology is made,” declared the authors), emphasizing that 

the rock-machine interaction in tunneling is very complex. 

 Subsequently, the key objection to QTBM was provided 

by a major study from Norway (where the Q-system was 

invented) published by Palmström and Broch [2006]. They 

concluded: 

QTBM is complex and even misleading and shows 

low sensitivity to penetration rate; the correlation 

coefficient with recorded data is even worse than 

conventional Q or RMR or with other basic parameters like the uniaxial compressive strength of the 

intact rock. It is recommended that the QTBM should 

not to be used. 

This finding is clearly supported by Figure 1. 

 Other attempts were reported by Alber [2001] concentrating on contracting practice and probabilistic estimates of advance rates and project economics. The RMR 

system was used by Grandori et al. [1995] to demonstrate 

ranges of effectiveness for TBM performance in different 

rock mass quality as a function of machine type: open 

TBM or double-shield. Bieniawski [2004] reviewed the 

concept of rock mass excavability based on the RMR as 

adjusted for TBMs. 

 However, there is convincing evidence that complex 

equations combining rock mass quality RMR or Q with 

additional parameters related to TBM characteristics are 

not an effective approach. In other words, it is doubtful 

that one formula can include all the factors pertinent to 

rock mass quality, as well as those influencing TBM 

choice and performance. 

 In fact, expert opinion holds that the RMR and Qsystems are most effective as they are commonly used, 

consistent with the purposes for which they were developed. Thus, adjusting these systems for TBM-sensitive 

parameters, such as rock abrasivity and cutter thrust, may 

be counterproductive and may only create confusion. 

 Figure 1.—Advance rates for three TBM tunnels plotted 

against QTBM [Sapigni et al. 2002]. 
17

THE CONCEPT OF THE ROCK MASS 

EXCAVABILITY (RME) INDEX 

 After much overwhelming evidence, such as shown in 

Figure 1, we concluded that modifying an existing rock 

mass quality classification, be it the RMR or Q, for 

determining rock mass excavability was not an effective 

approach for modern engineering practice. Accordingly, 

research devoted to rock mass excavability was initiated in 

2004 with the objective of establishing an index, similar to 

the RMR, but which was specifically directed to predicting 

rock mass excavability, rather than rock mass quality. This 

work was aimed at selecting the appropriate method of 

tunnel excavation, having considered rock mass-machine 

interaction, using TBMs or conventional mechanized excavation. The RME concept proposed first by Bieniawski et 

al. [2006] was based on analyses of 387 sections of three 

Spanish tunnels comprising 22.9 km in length. In each 

case, the tunnels studied included detailed data on rock 

mass characteristics and TBM parameters, as shown in the 

RME input data form in Figure 2. 

INPUT DATA FORM for Rock Mass Excavability

Name of Tunnel ............................................................................................................................. 

Initial chainage of section:..............................Final chainage of section......................................... 

Length of section:..............................m (should be > 40 m) 

Duration of excavation (days):.......................................................... (number + 1 decimal) 

 Average Rate of Advance ARA = ...................m/day 

Lithology:..........................................................................................Average depth:............. .......m 

ROCK MASS PARAMETERS 

 Uniaxial compressive strength of intact rock (? c):........................................MPa 

 Drilling Rate Index DRI:................. Type of homogeneity at excavation face:..................... ......... 

 N° of joints per meter:...............Rock Mass Rating RMR: range.........................average.............. 

 Orientation of discontinuities with respect to tunnel axis 

 (perpendicular, parallel or oblique):.............................................................. 

Stand up time:...................hours Groundwater inflow at tunnel face: .............liters/sec 

 Rock Mass Excavability RME range.....................................average.................... 

TBM PARAMETERS 

 Average speed of cutterhead rotation: .......... ................rpm Applied Thrust:...................m . kN 

 Specific Penetration:.....................................mm /rev 

 Rate of Penetration:...................................... mm /min 

 N° cutters changed:....................................... Rate of TBM utilization: .................. ...............%

Table 1.—Input ratings for Rock Mass Excavability (RME) index 

UCS OF INTACT ROCK (0–25 points) 

σc (MPa).................... <5 5–30 30–90 90–180 >180 

Average rating .......... 4 14 25 14 0 

DRILLABILITY (0–15 points) 

Drilling Rate Index .... >80 80–65 65–50 50–40 <40 

Average rating .......... 15 10 7 3 0 

DISCONTINUITIES AT TUNNEL FACE (0–30 points) 

Homogeneity Number of joints per meter Orientation with respect to tunnel axis 

Homogeneous Mixed 0–4 4–8 8–15 15–30 >30 Perpendicular Oblique Parallel 

Avg. 

rating 10 0 2 7 15 10 0 5 3 0 

STANDUP TIME (0–25 points) 

Hours ........................ <5 5–24 24–96 96–192 >192 

Average rating .......... 0 2 10 15 25 

GROUNDWATER INFLOW (0–5 points) 

L/sec ......................... >100 70–100 30–70 10–30 <10 

Average rating .......... 0 1 2 14 5 

1

Zero for argillaceous rocks. 

 Figure 2.—Input data form for determining the Rock Mass 

Excavability (RME) index. 
18

SELECTION OF RME INPUT PARAMETERS 

 The RME index is based on the five input parameters 

listed in Table 1, together with the ratings associated with 

each. Selecting the five parameters involved a Linear 

Discriminant Analysis using the R code developed by the 

Institute of Statistics and Probability Theory of the Vienna 

University of Technology, Austria. As a result of this 

analysis, it was found that the parameters with stronger 

influence in the average rate of advance (ARA), expressed 

in m/day, are: drillability/abrasivity, discontinuity spacing, 

and standup time. In addition, it was decided to include the 

two basic rock mechanics parameters: uniaxial compressive strength (UCS) of the rock material and water inflow 

because these two factors are known to strongly influence 

the TBM advance. Once the five parameters were selected, 

a weighted distribution was performed. These weights 

have been statistically analyzed, minimizing the error in 

the ARA prediction and resulting in the ratings shown in 

Table 1. 

 In practice, four of the input parameters are determined from standard site exploration programs: UCS of 

the rock material, rock drillability, rock mass jointing 

(spacing, orientation, and condition of discontinuities at 

the tunnel front), and groundwater inflow. The fifth parameter, standup time, is estimated from the well-known RMR 

chart (Figure 3), which depicts standup time versus 

unsupported active span as a function of RMR (after 

Bieniawski [1989]; see also Appendices A and B of this 

paper). As the case studies on that chart were derived from 

drill-and-blast tunnels, a correlation obtained by Alber 

[1993] is used for TBM tunnels. The following equation is 

applicable: 

 RMRTBM = 0.8 × RMRD&B + 20 (2) 

CORRELATION BETWEEN AVERAGE RATE OF 

ADVANCE (ARA) AND RME 

 The average rate of advance (ARA), expressed in 

m/day, is the most significant parameter to compare 

performances from several tunnel or drift construction 

projects. The statistical analyses carried out provided the 

correlation depicted in Figure 4 between the ARA and 

RME for single- and double-shield TBMs. 

 These findings were derived for tunnels with diameters close to 10 m. In order to take into account the influence of other tunnel diameters, D, the coefficient kD is 

used. The values of kD can be calculated from the following expression: 

 kD = –0.007D3 + 0.1637D2 – 1.2859D + 4.5158 (3) 

CORRELATIONS OF RME WITH OTHER 

PARAMETERS 

 A number of significant correlations were obtained in 

this study in addition to those discussed above. 

Specific Energy of Excavation 

 The concept of specific energy of excavation (Es) for 

mechanized tunneling and mining is “borrowed” from the 

petroleum and gas drilling industry, where it has been used 

for many years [Teale 1965]. Most recently, this concept 

was applied to assess the ease of mechanical excavation 

involving this expression: 

Es = F/A + 2π N T/ A × ARA (4) 

where Es = specific energy of excavation (kJ/m3); 

 F = total cutterhead thrust (kN); 

 A = excavated face area (m2

); 

 N = cutterhead rotation speed (rps); 

 T = applied torque (kN·m); 

and ARA = average rate of advance (m/s). 

 Figure 3.—Standup time as a function of RMR and 

unsupported span [Bieniawski 1989]. 

 Figure 4.—Correlation between the RME index and the 

average rate of advance (m/day) for single- and doubleshield TBMs. 
19

 The above equation consists of two terms. The first 

represents the specific energy of the cutterhead thrust from 

static loading, while the second is the specific energy of 

rotation incurred by the rotating cutterhead. In this study, 

the specific energy of rotation (Er) was related to the RME 

in Figure 5. 

Cutterhead Thrust (FC) and Torque (T) 

 Figures 6–7 show the correlation of RME with both FC

and T values, providing acceptable coefficients of R=0.64 

and R=0.71, respectively, for single- and double-shield 

TBMs. 

LATEST FINDINGS 

 The construction of the famous Guadarrama tunnels 

involving two tubes, each 9.5 m in diameter and 28 km 

long, using four double-shield TBMs, led to the introduction of an adjustment to the predicted ARA obtained 

from a given RME, incorporating the effect of the length 

of the tunnel excavated and the influence of the crew skills 

when dealing with the TBM and the terrain. This can be 

represented as 

 

 

L C

R

T F F

ARA ARA

× = (5) 

where ARAT = predicted true value of ARA from the 

correlation with RME; 

 ARAR = recorded average rate of advance, 

m/day, achieved in a tunnel section; 

 FL = factor of experience as a function of 

tunnel length excavated; 

and FC = factor of effectiveness by the crew 

handling the TBM and the terrain. 

 Based on the results obtained during construction of 

the Guadarrama and Abdalajís Tunnels, Tables 2–3 show 

the values appropriate for the coefficients FL and FC. 

Table 2.—RME adjustment factor (FL) 

Tunnel length 

excavated (km) 

Adjustment 

factor (FL) 

0.5............................................ 0.50 

1.0............................................ 0.86 

2.0............................................ 0.97 

4.0............................................ 1.00 

6.0............................................ 1.07 

8.0............................................ 1.12 

10.0.......................................... 1.15 

12.0.......................................... 1.20 

 Figure 5.—Correlation between the RME index and the 

specific energy of excavation. 

 Figure 6.—Correlation between the RME index and 

TBM torque. 

 Figure 7.—Correlation between the RME index and 

cutterhead thrust. 
20

Table 3.—RME adjustment factor (FC) 

Effectiveness of the crew 

handling TBM and terrain 

Adjustment 

factor (FC) 

Less than efficient....................... 0.88 

Efficient....................................... 1.00 

Very efficient ............................... 1.15 

 This produces a refined RME07 correlation depicted in 

Figure 8 devoted specifically to double-shield TBMs. 

SPECIAL CONSIDERATIONS FOR APPLICATIONS 

IN MINING 

 Applications of rock mass classifications in mining 

require some special considerations compared to civil 

engineering for a number of reasons. The three most 

important are— 

1. The effect of in situ stresses, since mines are usually 

deeper than tunnels; 

2. The effect of the induced stresses, because in mining 

the stress field changes as mining advances and also 

due to adjacent excavations; and 

3. The effect of blasting damage, because in hard-rock 

mining drilling and blasting, unless smooth blasting 

is used, may have an adverse effect on stability compared to machine boring. 

 As a matter of fact, all of the above effects were incorporated into the Mining Basic RMR (MBR) classification 

proposed by Kendorski et al. [1983]. 

 There are various types of excavating machines used 

in mining. In modern hard-rock mines, machine excavation 

is used to construct access drifts and chambers, while in 

coal mines, continuous miners and shearers are common. 

In each case, to access mineral deposit production, mines 

employ roadheaders and/or open-type TBMs. 

 The RME index can be applied directly to evaluate 

excavability of mine drifts and chambers. However, at the 

time of writing, work on correlations between the RME 

and ARA is still in progress for roadheaders and open 

TBMs. In fact, investigations to determine a correlation 

between the RME and ARA for open-type TBMs began 

last year, with results expected to be presented by June 

2007. 

 As far as applications to roadheaders and similar 

machines are concerned, we are still in the process of data 

collection and would welcome any case histories of 

RME applications in this respect by interested parties. In 

ARAT = 0,813RME07 - 32,56

ARAT = 0,597RME07 - 24,88

0

10

20

30

40

50

60

50 55 60 65 70 75 80 85 90 95 100

RME07

ARAT (m/día)

σci>45 MPa

σci≤45 MPa

 Figure 8.—Correlation between the RME07 and the average rate of advance for double-shield TBMs. For RME<50, TBMs in 

double-shield mode are not recommended. 
21

addition, applications in room-and-pillar and longwall 

mining will require modifications to the actual structure of 

the RME index due to the specific nature of such mining 

operations. For example, the ratings for the standup time 

parameter may require an adjustment factor due to the 

degree of fracturing in the roof strata and due to the effect 

of the induced stress in order to better assess the stability 

of the rock mass in these types of mining operations. 

CONCLUSIONS 

 After 3 years of studies and analyses of more than 400 

case histories, RME seems to provide a tool that enables 

tunnel designers and constructors to estimate the performance of TBMs. Future work will focus on extending the 

RME to all types of TBMs and improving the existing 

correlations with the significant operational output parameters. Extending this work for more applications to mining 

provides challenging opportunities. 

ACKNOWLEDGMENTS 

 This paper was made possible by the dedicated work 

of the professional staff at Geocontrol, S.A., Madrid, 

Spain, including the first two recipients of the Bieniawski 

Scholarship for tunneling research at the Superior School 

of Mines, Universidad Politécnica de Madrid: Doña María 

Álvarez Hernández and currently Don José Carballo 

Rodrígez. These Bieniawski Scholarships were generously 

funded by Geocontrol, S.A. 

REFERENCES 

 Abrahão RA, Barton N [2003]. Employing TBM 

prognosis model. Tunnels & Tunnelling Int 35(12):20–23. 

 Alber M [1993]. Classifying TBM contracts. Tunnels 

& Tunnelling Int Dec:41–43. 

 Alber M [2001]. Advance rates for hard-rock TBMs 

and their effects on project economics. Tunnelling Undergr 

Space Technol 15(1):55–60. 

 Barton N [1974]. Engineering classification by the 

Q-system. Rock Mech 6(4):183–236. 

 Barton N [2000]. TBM tunneling in jointed and 

faulted rock. Rotterdam, Netherlands: Balkema. 

 Bieniawski ZT [1972]. Engineering classification for 

rock masses. Rep S Afr Coun Sci Ind Res, ME 327/Rock 

Mech, Pretoria, South Africa. 

 Bieniawski ZT [1973]. Geomechanics classification of 

jointed rock masses. J S Afr Inst Civ Eng Dec:382–398. 

 Bieniawski ZT [1989]. Engineering rock mass classifications: a complete manual. New York: John Wiley & 

Sons. 

 Bieniawski ZT [2004]. Aspectos clave en la elección 

del método constructivo de túneles (in Spanish). In: Proceedings of Jornada Técnica (Madrid, Spain), pp. 1–37. 

Also in: Ingeopress, No. 126, pp. 50–68. 

 Bieniawski ZT, Celada B, Galera JM, Álvares M 

[2006]. Rock mass excavability (RME) index: a new way 

to select the optimum tunnel construction method. In: Proceedings of the ITA World Tunnelling Congress (Seoul, 

South Korea). 

 Blindheim OT [2005]. A critique of QTBM. Tunnels & 

Tunnelling Int 6:32–35. 

 Grandori R, Jager M, Vigl, L [1995]. The Evinos 

TBM tunnel. In: Proceedings of the Rapid Excavation and 

Tunneling Conference. American Society of Civil 

Engineers, pp. 132–146. 

 Kalamaras ES, Bieniawski ZT [1995]. A rock mass 

strength concept incorporating the effect of time. In: Proceedings of the ISRM Congress (Tokyo, Japan). Balkema, 

pp. 295–302. 

 Kendorski FS, Cummings RA, Bieniawski ZT, Skinner EH [1983]. Rock mass classification for block caving 

mine drift support. In: Proceedings of the 15th ISRM 

Congress (Melbourne, Victoria, Australia). Balkema, pp. 

B101–B113. 

 Kirsten HAD [1982]. A classification for excavation. 

Civ Eng S Afr 7:293. 

 Laubscher DH, Taylor HW [1976]. The importance of 

geomechanics of jointed rock masses in mining operations. 

In: Proceedings of the Symposium on Exploration for 

Rock Engineering (Johannesburg, South Africa), pp. 119–

128. 

 Palmström A, Broch E [2006]. Use and misuse of rock 

mass classification systems with particular reference to the 

Q-system. Tunnelling Undergr Space Technol 21(6):575–

593. 

 Sapigni M, Berti M, Bethaz E, Bustillo A, Cardone G 

[2002]. TBM performance estimation using rock mass 

classifications. Int J Rock Mech Min Sci 39:771–788. 

 Teale R [1965]. The concept of specific energy in rock 

drilling. Int J Rock Mech Min Sci 2:57–73. 

 Unal E [1983]. Design guidelines and roof control 

standards for coal mine roofs [Dissertation]. University 

Park, PA: The Pennsylvania State University. 
22

APPENDIX A.—EXAMPLE OF RME CALCULATIONS 

 The figure below presents an example of the actual procedure for calculating RME07 for one of the case histories 

plotted in Figure 8. 

 
23

 The Rock Mass Excavability classification features 

one parameter—standup time—depicted in Figure 3, 

which is determined from the Rock Mass Rating (RMR), 

as shown in Appendix A. Since RMR was first introduced 

in 1972 and published internationally in 1973, it is appropriate to briefly summarize some of the lessons acquired 

about rock mass classifications after 35 years of use 

throughout the world. 

 The most important aspect to remember is that the 

main application of RMR is not just recommendations for 

rock support (because they change as technology changes), 

but estimation of rock mass properties for design and 

numerical purposes, i.e., the modulus of deformation of the 

rock mass, rock mass strength, and standup time. Figures 

A–1 and A–2 depict these strength and deformation 

relationships. In fact, the RMR case histories for these 

purposes still remain the prime data for analyses and 

correlations and are published in full [Bieniawski 1989]. 

 There are three general guidelines to be observed for 

good engineering practice: 

1. Rock mass classifications, either quantitative systems, such as RMR and Q, or descriptive methods 

(New Austrian Tunneling Method (NATM) or Geological Strength Index (GSI)), are most effective if 

not used on their own, but incorporated within the 

overall engineering design process. 

2. Rock mass classifications on their own should only 

be used for preliminary planning purposes and not as 

final rock reinforcement. For preliminary design and 

planning purposes, the two quantitative RME and 

Q-systems are excellently suited. They quantify rock 

mass conditions, enable estimates of rock mass properties, and provide the reference bases for expected 

rock mass conditions. 

3. The two predominant quantitative rock mass classifications, RMR and Q, are particularly essential for 

monitoring rock conditions during construction or 

mining to enable effective comparison of predicted 

conditions from site investigation with those encountered. For this purpose, descriptive classifications 

(those not based on quantitative input data) are deficient. They do not provide a continuous quantification of the encountered conditions, even if based 

on deformation measurements during construction, 

because contractual specifications in many countries 

prevent enough measurements to be taken since they 

interfere with the mining or tunneling schedule. 

 Most of all—users, please beware! It is not recommended to apply any rock mass classification system on its 

own, be it NATM, RMR, or Q. Instead, both RMR and Q 

should always be used to cross-check the results and compare recommendations, even if known correlations exist 

between these two systems, which sometimes turn out to 

be oversimplifications. 

 Figure A–1.—Correlation between the ratio of rock 

mass strength, σM, and UCS of rock material, σc, as a 

function of RMR [Kalamaras and Bieniawski 1995]. 

 Figure A–2.—Correlation between the modulus of 

deformation of the rock mass, EM, and RMR. 

APPENDIX B.—GENERAL GUIDELINES ON ROCK MASS CLASSIFICATIONS 

(BASED ON THE RMR SYSTEM: 35 YEARS LATER) 

25

ROCK MASS CHARACTERIZATION OF PRIMARY COPPER ORE FOR CAVING 

AT THE EL TENIENTE MINE, CHILE 

By Andres Brzovic1

 and Ernesto Villaescusa, Ph.D.2 

ABSTRACT 

 Rock masses of the primary copper ore at the 

El Teniente Mine are very competent and massive. The 

rock mass contains almost no open discontinuities. Nevertheless, there is a high frequency network of small discontinuities coupled with widely spaced faults. A data 

collection campaign designed to characterize the rock 

structure was recently implemented. The results show that 

conventional analysis in terms of discontinuity frequency 

does not predict differences between two studied sectors. 

However, when an empirical definition of a weak discontinuity is applied, appreciable differences between the two 

sectors appear in terms of discontinuity frequency and predicted in situ block size distributions. These differences are 

in accordance with actual observations at the mine site. 

Due to the geological features of the primary copper ore, 

rock mass classification schemes cannot be readily applied 

to characterize these rock masses.

INTRODUCTION 

 Rock mass classification systems have been developed 

and used since the 1950s as useful design tools in civil and 

mining projects. Initially developed for tunneling 

reinforcement [Terzaghi 1946], these have been extended 

to multiple civil engineering applications [Barton et al. 

1974; Hoek et al. 1995; Laubscher 1993]. 

 The rock mass classification schemes are based on the 

parameterization of the geological features of the rock 

mass. These systems simplify the rock mass into two main 

components: the intact rock properties and the discontinuity characteristics. The International Society for Rock 

Mechanics (ISRM) has suggested guidelines to characterize these geological features [ISRM 1978]. 

 Following ISRM’s guidelines, a data collection campaign designed to characterize the rock structure of the 

primary copper ore was implemented recently at the 

El Teniente Mine. This paper presents some results of this 

current research [Brzovic 2005]. Some data analyses 

previously published [Brzovic et al. 2006; Brzovic and 

Villaescusa 2007] are used here since they are relevant to 

consider when the rock mass classification schemes are 

applied. 

El Teniente Mine Overview 

 The El Teniente Mine, located in the Andes Mountains 

in central Chile, 70 km south of Santiago (Figure 1), is one 

of the largest underground mine operations in the world. 

Since 1906, more than 1.1 billion tons of ore has been 

mined. The mine is a stated-owned company (Codelco), 

and it is currently extracting 131,000 tons per day using 

block-caving methods. Panel (called conventional) and 

pre-undercut caving methods [Rojas et al. 2000b], variations of standard block caving, were introduced in 1982 

and 1994, respectively, to exploit the primary copper ore 

(Figure 2). 

 

 1CRC Mining, Western Australian School of Mines, 

Kalgoorlie, Australia, andCodelco Division El Teniente, 

Millan, Rancagua, Chile. 

 2CRC Mining, Western Australian School of Mines, 

Kalgoorlie, Australia. Figure 1.—Location of the El Teniente Mine. 
26 

Cave Front 

Cave

Pre-undercutting caving cross section 

Blast-holes

4 meters high 

horizontal rings 

Draw points at 

production level 

Cave 

Conventional panel caving cross section 

Blast-holes 

13 meters high 

vertical rings 

Undercut level 

Advancing Cave Front

Figure 2.—Panel and pre-undercut caving methods at the El Teniente Mine. 

Dacite

MINE U.T.M.

NORTH NORTH

Diorite

Andesite

Major Structures

Undercut level of the Teniente Sub-6 mine sector

Sampling

Location Block Samples

 Figure 3.—Geology of the studied sectors and sampling locations for line sampling techniques and the collected caved 

rock blocks. 
27 

 Currently, almost all of the caving operations are being 

undertaken within primary copper ore. Such rock masses 

are very competent and massive, exhibiting brittle, often 

violent failure under high-stress conditions [Rojas et al. 

2000a]. Caving generally results in large rock fragments. 

Despite these unfavorable rock mass conditions, high rates 

of production have been achieved since primary copper ore 

extraction by the conventional panel caving method began 

in 1982. 

Geology and Rock Mass Properties 

 El Teniente is the largest known copper-molybdenum 

deposit in the world [Skewes et al. 2006]. It is hosted in a 

copper porphyry system [Cannell et al. 2005]. The main 

rock types include a mafic intrusive complex (andesite for 

this paper), felsic porphyry intrusive (dacite and diorite) 

and hydrothermal breccias (Braden breccia) of the 

Miocene Era (Figure 3). Two main structure types are 

observed within the primary copper ore: a system of largescale faults [Garrido et al. 1994] and a stockwork having a 

high frequency of small-scale vein features [Brzovic and 

Villaescusa 2007; Cuadra 1986], as shown in Figure 4. 

 The stockwork veins observed within primary copper 

ore are mainly filled and cemented (healed) with quartz, 

sulfides, and anhydrite (Figure 4). Different vein types 

have been identified, and these define alteration zones 

according to their local abundance. Main alteration zones 

are referred to as shown in Figure 3: the late hydrothermal 

(LH) zone and the principal hydrothermal (PH) zone 

[Cuadra 1986]. 

 Open joints are rarely found within the primary copper 

ore, and faults have a very low frequency of occurrence in 

line sampling (0.1 m–1) [Brzovic 2001]. Joints and faults 

are the only open discontinuities at the El Teniente Mine. 

Thus, the primary copper ore rock mass prior to caving can 

be described as very competent, massive, and impermeable. Intact rock mass properties and rating of rock mass 

quality used in current mine planning, for main rock types, 

are presented in Table 1 [Celhay et al. 2005]. 

Data Collection Design 

 In order to characterize the rock structure of the 

primary copper ore, line sampling techniques were 

undertaken in mine drives within two levels of the 

Teniente Sub-6 Mine sector (Figure 3). Two structural 

domains referred to as andesite Hw (hangingwall) and Fw 

(footwall) were characterized by applying different truncation and censoring biases [Brady and Brown 2003; 

Villaescusa 1990]. These sampling biases can be considered as different mapping scales (Table 2). A detailed 

description of the sampling regime adopted in this study is 

presented by Brzovic and Villaescusa [2007]. 

 Figure 4.—Stockwork veins (most white lines) and faults (middle of the left photo) recognized within the primary 

copper ore. The right photo is rock block (1.4-m-long base) showing the stockwork veins, which is used as a monument in Rancagua, Chile.
28 

ROCK STRUCTURE CHARACTERIZATION

 Although 8 of the 10 suggested parameters by ISRM 

were fully characterized in both sectors [Brzovic 2005], 

only the relevant ones are detailed in the following 

sections. 

Discontinuity Occurrence at Different 

Mapping Scales 

 Taking into account the main geological discontinuities found within primary copper ore [Brzovic 2001; 

Cuadra 1986; Garrido et al. 1994], the occurrence for main 

discontinuity types observed in line mapping is presented 

in Table 3. 

 Although the occurrence of discontinuities does not 

consider the natural anisotropy of the rock structure, some 

relevant aspects are deduced from Table 3. This is 

considering the fact that, in most cases, a similar sampling 

orientation with respect to discontinuity orientation was 

used for all of the mapping scales [Brzovic and Villaescusa 

2007]. Firstly, excluding faults, open joints are not found 

within the primary copper ore; instead, a large number of 

veins were recognized (Figure 4). Secondly, faults were 

better characterized using large-scale sampling. In contrast, veins were better characterized using small-scale 

sampling. In other words, faults and veins are present 

within the primary copper ore at different scales. These 

discontinuity characteristics do not depend on the sampling 

regime, as discussed by Brzovic and Villaescusa [2007]. 

Table 3.—Discontinuity type occurrence observed for each 

data collection technique 

Discontinuity 

Sampling method Sector Data occurrence (%) 

Faults Veins 

Major structures Hw 21 57.1 42.9 

 interpretation........... Fw 22 77.3 22.7 

Drive mapping .......... Hw 239 16.7 83.3 

Fw 206 30.5 69.5 

Lines mapping .......... Hw 521 1.9 98.1 

Fw 319 7.2 92.8 

Oriented drill core Hw 606 0.3 99.7 

 logging .................... Fw 565 1.9 98.1 

Hw Hangingwall. Fw Footwall. 

Discontinuity Orientation 

 Faults were found defining one or two subvertical sets 

at each sector (using large mapping scale). Although 

subhorizontal faults were not entirely defined by largescale sampling, the fault data are in agreement with the 

strike-slip fault characteristics described at the mine scale 

[Garrido et al. 1994]. In contrast, data from small-scale 

sampling show that, for both sectors, the veins comprise at 

least three semiorthogonal discontinuity sets. Figure 5 

shows examples of both fault and vein contour orientations 

from drive and line mapping at the Hw sector. 

Table 1.—Mean of the intact rock mass properties and rock mass quality used for main rock types [Celhay et al. 2005] 

Intact rock properties Rock mass rating Rock type Density (ton/m3

) E (GPa) ν UCS (MPa) RMRL Q′ GSI 

Braden breccias................... 2.61 21 0.19 70 — — — 

Dacite .................................. 2.63 30 0.18 90 59–66 22–32 75–95 

Diorite .................................. 2.77 45 0.21 140 64 19 75–90 

Andesite............................... 2.77 56 0.20 115 53–59 19–22 70–85 

 NOTE.—E, Young’s modulus; ν, Poisson’s ratio; RMRL from Laubscher [1993]; Q′ adapted from Barton et al. [1974]; and 

Geological Strength Index (GSI) from Hoek et al. [1995]. 

Table 2.—Censoring level, truncation biases, and sample length applied to 

different line sampling techniques 

Sampling method Censoring level1

(m) 

Truncation bias 

(m) 

Range of the horizontal 

sample length (m) 

Major structure interpretation........ — 30 150 

Drive mapping .............................. 4.4–4.9 4 30–150 

Line mapping ................................ 3.6–4 2

0.3 7–22 

Oriented drill core logging............. 0.1–0.14 2

0.1 4–12 1

Censoring level only considers the maximum high or width of the observation window. 2

Some discontinuities longer than the threshold value could not be collected during data collection. 
29 

Discontinuity Spacing 

 The mean normal fault set spacing determined only by 

large-scale sampling ranges mainly between 5 and 10 m, 

which is in agreement with previous work [Brooks et al. 

1996; Brzovic 2001]. Conversely, high vein frequency was 

found using small-scale sampling. However, the discontinuity spacing is strongly influenced by the truncation 

bias used, as Figure 6 shows. Assuming that each 

truncation level applied represents a mapping scale, the 

mean normal discontinuity set frequency may be 

represented as a fractal feature within the rock mass. 

Scaling law properties (fractal and lineal) for the spacing 

on other parameters of the geological discontinuities have 

been proposed by several authors [Cladouhos and Marrett 

1996; Gillespie et al. 1993; Hobbs 1993; Scholz 2002], 

which are consistent with this finding. It is important to 

recognize that the truncation bias used during data 

collection plays a relevant role in the spacing obtained, 

particularly if the rock mass classification schemes are 

applied. 

No Bias Correction 

_________________ 

Equal Angle 

Lower Hemisphere 

Fisher Concentrations 

% of total per 1.0 % area 

 < 1.0 % 

1.0 – 2.0 % 

2.0 – 3.0 % 

3.0 – 4.0 % 

4.0 – 5.0 % 

5.0 – 6.0 % 

6.0 – 7.0 % 

7.0 – 8.0 % 

8.0 – 9.0 % 

 > 9.0 % 

(a) 40 Faults (Drive mapping Hw) 

Max. Conc. 25.4% (b) 511 veins (Line mapping Hw) 

Max. Conc. 3.79% 

Figure 5.—Fault (a) and vein (b) contour orientations plotted in lower hemisphere projections.

Truncation bias length (m) 

Mean normal set discontinuity 

frequency (m-1) 

0

0

1

10

100

0.01 0.1 1 10 100

y = 0.8968 x-1.0256

Core logging 

Line mapping 

Drive mapping 

Major structures 

100 

10 

1 

0.1 

0.01 

 Figure 6.—Discontinuity frequency and truncation bias 

length.

Table 4.—Extremes in values of discontinuity frequency and their orientation using three-dimensional loci 

[Hudson and Priest 1983] 

Andesite Hw Andesite Fw 

Sampling method Global type Discontinuity 

frequency (m–1) Trend/Plunge Discontinuity 

frequency (m–1) Trend/Plunge 

Maxima 6.53 062/48 8.82 064/34 

Minima 2.34 197/07 2.23 257/68 

Mean 4.97 5.74 

Line mapping 

Coefficient of variation 17% 25% 

Maxima 29.70 036/17 28.56 342/02 

Minima 8.69 280/05 4.61 225/65 

Mean 19.32 16.90 

Oriented drill core 

logging 

Coefficient of variation 26% 32% 

Hw Hangingwall. Fw Footwall. 
30 

 Additionally, using the proposed discontinuity frequency diagram, three-dimensional loci [Hudson and 

Priest 1983], the global maxima and minima, and their 

orientations for discontinuity frequencies were estimated 

for both sectors (Table 4). This table also includes the 

mean discontinuity frequency as the average values 

obtained from more than 8,000 equidistant directions. This 

mean value would represent the discontinuity frequency 

normalized with respect to the orientation at this location. 

 Table 4 confirms the strong influence of the truncation 

bias in the discontinuity frequency gathered from a rock 

mass. It also shows that, in terms of structural anisotropy, 

both sectors are quite similar when considering the same 

mapping scale, but minor differences appear when comparing the same sectors at different mapping scales. Therefore, the data in Table 4 show negligible differences 

between the andesite sectors when all discontinuity types 

are considered. 

Vein Infill 

 The infill observed in veins was studied by a quantitative estimation of their mineral composition. For instance, 

a vein could be described as containing 25% quartz, 50% 

chalcopyrite, and 25% anhydrite. This quantitative infill 

description was only undertaken at small sampling scales, 

i.e., line mapping and oriented drill core logging. 

 The results show that more than nine mineral species 

were present within the vein infill. Rarely was one mineral 

present in all veins, and rarely did this mineral maintain 

the same proportion of abundance as infill throughout all 

veins (Tables 5–6). In fact, some particular mineral compositions and associated alteration halos have been used to 

classify vein types at the El Teniente ore deposit [Cuadra 

1986]. Nevertheless, four main mineral species were found 

to be the most common and abundant in all veins 

recognized during this study: chlorite, anhydrite, chalcopyrite, and quartz (Table 6). 

 In addition, in terms of veins infill, no substantial 

differences could be observed between discontinuity sets 

from the same mapping scale/sector. For example, Table 7 

presents discrete data for the sets observed in Figure 5b. 

In this table, a similar infill pattern is observed for each 

set. The individual values are similar to the mean of the 

sector. However, the mineralogical composition was found 

to have some particular differences between andesite 

sectors. 

Table 5.—Mean of mineral species observed as vein infill, by data collection technique and sector 

Mean of mineral specie observed as vein infill (%) Sampling method Sector Data 

Chlorite Biotite Anhydrite Chalcopyrite Pyrite Quartz Other 

Line mapping Hw 511 6.8 1.7 17.4 13.0 0.2 56.5 4.4 

Fw 296 10.0 1.6 21.7 29.7 2.8 31.5 2.7 

Drill core logging Hw 604 5.5 5.0 2.6 13.0 0.3 68.9 4.7 

Fw 554 13.2 3.7 16.5 26.8 3.9 32.4 3.5 

Table 7.—Mean of mineral species observed as vein infill for each family set (refer to Figure 5b) using line mapping 

at Hw sector 

Mean of mineral specie observed as vein infill (%) Sampling method Data 

Chlorite Biotite Anhydrite Chalcopyrite Pyrite Quartz Other 

Random 30 5.2 2.3 16.7 8.2 0.6 61.5 5.5 

Set 1 (subvertical E–W) 173 6.1 1.5 17.0 13.6 0.2 55.8 5.8 

Set 2 (subvertical N–S) 142 8.8 2.5 23.9 13.1 0.4 48.3 3.0 

Set 3 (subhorizontal) 166 6.2 1.0 12.4 13.2 0.2 63.3 3.7 

Total and mean 511 6.8 1.7 17.4 13.0 0.2 56.5 4.4 

Table 6.—Percentage of mineral species observed as infill within all veins, by data collection technique and sector 

Proportion of veins having the mineral specie as infill (%) Sampling method Sector Data 

Molybdenite Chlorite Biotite Anhydrite Chalcopyrite Pyrite Quartz 

Line mapping Hw 511 6.5 52.8 12.3 71.2 80.2 3.1 91.8 

Fw 296 6.4 58.4 12.8 84.8 94.6 17.9 86.8 

Drill core logging Hw 604 5.6 38.1 22.5 18.7 80.0 2.5 94.9 

Fw 554 3.1 67.5 25.4 70.6 96.6 31.9 86.3 
31 

Vein Thickness 

 Data analysis of the vein thickness distribution also 

showed that no differences between discontinuity sets from 

the same mapping scale at any sector could be observed. 

Similar to the vein infill, Table 8 shows almost the same 

vein thickness distribution for each set of data plotted in 

Figure 5b. The vein set thickness distribution is the same 

as the mean of the sector. 

 Nevertheless, similar to discontinuity spacing, Table 9 

suggests that the distribution of vein thicknesses observed 

in line sampling was strongly affected by the truncation 

level applied. Additional (thinner) discontinuities appeared 

as the truncation length value was decreased. In this table, 

data collected by standard unoriented drill core (46-mmdiam) are included [Brzovic 2005] and confirm this finding. This vein characteristic is in agreement with the linear 

relationship observed between vein length and aperture in 

several different geological environments [Vermilye and 

Scholz 1995]. 

 Therefore, two important facts can be deduced. Firstly, 

assuming a representative sample size, the observed vein 

thickness distributions (and also the vein infill pattern) can 

be considered to have an isotropic characteristic within the 

rock mass, i.e., a linear sampling scheme will define the 

same thickness distributions at any discontinuity orientation. Secondly, longer discontinuities recognized at the 

El Teniente Mine tend to be statistically thicker than 

shorter ones. Furthermore, data analysis indicated that the 

vein infill does not show changes as the vein thickness 

varies (Figure 7). 

In Situ Rock Block Size Distribution 

 In order to obtain the in situ rock block distribution 

and for illustrative purposes only, the data gathered from 

oriented drill core logging were simulated using a software 

program [Thompson 2002]. This program assumes a 

Fisher distribution for orientation, negative exponential 

distribution for spacing, and infinite trace length for 

discontinuities (Figure 8). Similar to the discontinuity frequency, Figure 8 shows that the in situ rock block size 

distributions are practically the same between the andesite 

sectors. 

Table 8.—Vein thickness distribution for each family set observed (refer to Figure 5b) using line mapping at 

Hw sector 

Thickness distribution observed (%) 

Family set Data <1 mm 1 to 

<2 mm 

2 to 

<3 mm 

3 to 

<5 mm 

5 to 

<10 mm ≥10 mm 

Random 30 16.7 56.7 10.0 6.7 3.3 6.7 

Set 1 (subvertical E–W) 172 13.4 40.1 19.8 18.6 7.0 1.2 

Set 2 (subvertical N–S) 139 10.1 61.9 10.1 8.6 5.8 3.6 

Set 3 (subhorizontal) 165 12.7 45.5 15.2 12.7 6.1 7.9 

Total and mean 506 12.5 48.8 15.0 13.2 6.1 4.3 

Table 9.—Vein thickness distribution occurrence observed in veins for each data collection technique 

Thickness distribution observed (%) 

Sampling method Truncation 

bias length Data 

<1 mm 1 to 

<2 mm 

2 to 

<3 mm 

3 to 

<5 mm 

5 to 

<10 mm ≥10 mm 

Drive mapping 4 m 342 — 5.5 11.7 41.9 25.4 15.5 

Line mapping 0.3 m 802 13.3 48.9 15.8 14.9 4.2 2.9 

Drill core logging (φ = 10–14 mm) 0.1 m 1126 26.3 44.2 13.6 11.7 2.6 1.6 

Drill core logging (φ = 4.6 mm) 1

0.03 m Many2 64.0 21.5 7.7 4.3 1.8 0.7 

 1

Although there was not a truncation bias length threshold criterion during drill core logging, the practical length threshold is estimated at around 0.03 m based on the ability to visually identify discontinuity during core logging. 

 2

Mean obtained from 108 core section samples 6.1 m long collected from multiple directions [Brzovic 2005]. 

Fw Sector

0

10

20

30

40

50

1< 1-2< 2-3< = 3

Veins thickness (mm)

Mean of the infill (%)

Chlorite Oc

Anhydrite Oc

Chalcopyrite Oc

Quartz Oc

Chlorite Lm

Anhydrite Lm

Chalcopyrite Lm

Quartz Lm

 Figure 7.—Relationship between thicknesses and the 

mean of the mineralogical infill in veins observed at the 

Fw sector. (Oc = oriented drill core; Lm = line mapping.)
32 

WEAK DISCONTINUITIES AT THE 

EL TENIENTE MINE 

 The data analysis presented so far included all discontinuities recognized within the rock mass of primary 

copper ore. However, it is not expected that veins having 

intermediate- or high-tensile strength (veins having abundant quartz as infill) would be dominant during rock mass 

disassembly by caving. 

 Laubscher and Jakubec [2000] have incorporated the 

effect of healed veins in their proposed methodology to 

estimate the quality of a jointed rock mass. They described 

veins as cemented filled joints. However, because of the 

lack of data regarding the mechanical properties of veins, 

they proposed the use of the Mohs scale of hardness 

[Laubscher and Jakubec 2000] to define their frictional 

properties. In this proposed methodology, veins filled with 

quartz are ignored due to their strong strength. However, 

this work does not specify how to proceed when a vein is 

filled with more than one mineral having different hardnesses, as is the case for most discontinuities recognized at 

the El Teniente Mine (Tables 5–7). 

 The lack of experimental determination of the vein 

infill strength [Willoner 2000] led the authors to study the 

characteristics of caved rock blocks at the draw points. The 

objective was to find a common mineralogical association 

ensemble in veins that defined caved rock block faces 

during the rock mass disassembly by caving. 

 This study was carried out at the production level of 

the Teniente Sub-6 Mine sector (Figure 3), and its major 

finding was explained in detail by Brzovic et al. [2006] 

and Brzovic and Villaescusa [2007]. The main conclusion 

reached was an empirical definition of weak discontinuities, which are those veins having less than 35% of hard 

minerals as infill. A hard mineral was particularly defined 

as having a Mohs scale of hardness greater than 4 [Hurlbut 

1941]. This finding was gathered from the commonly 

observed characteristics in 639 veins forming caved rock 

block faces (Figure 9). These veins were significant weaknesses within the rock mass during the caving and subsequent fragmentation process. 

 The following comparisons of the El Teniente rock 

structure are made, taking into account the overwhelming 

effect of weak discontinuities. 

ROCK STRUCTURE CHARACTERIZATION 

USING ONLY WEAK DISCONTINUITIES 

 The cumulative frequency of veins collected using 

oriented drill core logging in terms of their abundance of 

hard minerals as infill is shown in Figure 10. This figure 

shows that for the threshold value adopted here (veins 

filled up to 35% of hard infill), the weak discontinuities 

represent 16.3% and 49.9% of all discontinuities observed 

in andesite Hw and andesite Fw, respectively. Figure 10 

also shows that appreciable differences between these sectors still appear if a different threshold value of hard infill 

would be adopted. 

0

20

40

60

80

100

0.01 0.1 1 10

Mean unfractured distance of rock block (m)

Passing %

Oc Hw

Oc Fw

Data collection 

 Figure 8.—In situ block size distributions from rock 

structure simulation. (Oc = oriented drill core.)

10 cm long

 Figure 9.—Veins as faces of polyhedral caved rock blocks in draw points showing mineralogical infill such as 

chalcopyrite and quartz.
33 

Weak Discontinuity Spacing 

 Since weak discontinuities are a subgroup of all discontinuities, the mean normal weak discontinuity set spacing was estimated using the same set definition. Using the 

three-dimensional loci [Hudson and Priest 1983], the 

global maxima and minima and their orientations for weak 

discontinuity frequencies were estimated for both sectors 

(Table 10). No appreciable differences in structural anisotropy can be determined when only the weak discontinuities are considered and compared to when all 

discontinuities are considered. This can be concluded by 

comparing Tables 4 and 10. These conclusions reveal two 

important things. First, they confirm the isotropic characteristic of the vein infill within the rock mass. Second, the 

different thickness distributions observed at both mapping 

scales do not affect the vein infill characteristics, as suggested by Figure 7. 

 Table 10 clearly shows that at any mapping scale the 

andesite rock type from the Fw sector has a larger frequency of weak discontinuities. According to drill core 

data, the normalized frequency of weak discontinuities 

within the Fw sector is nearly three times that of the 

Hw sector. 

 In addition, using only data collected by oriented drill 

core and line mapping from both sectors, the ratio between 

weak discontinuities and all discontinuities per each 

discontinuity set is calculated and presented in Figure 11. 

The data shown in this figure suggest that weak 

discontinuities are homogeneously distributed within any 

discontinuity set. 

Weak Discontinuity Persistence 

 The discontinuity trace length features were only 

studied using line mapping at mine drives. Data analysis of 

the weak discontinuity trace length distribution showed 

that no differences between discontinuity sets could be 

observed (Table 11). This finding is consistent with a 

circular shape of discontinuities, which always present the 

same distribution pattern at any rock exposure [Warburton 

1980]. Then, the mean value of the trace length distribution for the whole sector can be calculated. Table 11 also 

shows that the trace length distribution of any set of the 

Hw sector (plotted in Figure 5b) is almost the same as the 

mean of the sector. Moreover, Figure 12 reveals that the 

mean trace length distribution of each sector for weak and 

all discontinuities are practically the same. 

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 100

Observed frequency (%)

Oc Hw

Oc Fw

Proportion of hard infill within veins (%) 

Criterion adopted 

 Figure 10.—Cumulative frequency of veins collected 

using oriented drill core logging (Oc) in terms of the hard 

minerals as infill. Dashed line shows the criterion of weak 

discontinuities used in this study.

y = 1.0752x

R2

 = 0.8838

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Mean normal set frequency of weak discontinuities 

Mean normal set frequency of all discontinuities 

Number of weak discontinuity per set

Total discontinuity per set 

Figure 11.—Ratio between weak and all discontinuities.

Table 10.—Extremes in values of discontinuity frequency and their orientation of weak discontinuities 

using three-dimensional loci [Hudson and Priest 1983] 

Andesite Hw Andesite Fw 

Sampling method Global type Discontinuity 

frequency (m–1) Trend/Plunge Discontinuity 

frequency (m–1) Trend/Plunge 

Maxima 3.29 066/47 4.88 062/36 

Minima 0.80 197/07 1.41 257/68 

Mean 2.44 3.24 

Line mapping 

Coefficient of variation 23% 24% 

Maxima 5.62 027/06 15.40 340/00 

Minima 1.04 280/05 2.88 225/65 

Mean 3.46 9.11 

Oriented drill core 

logging 

Coefficient of variation 35% 31% 
34 

 Therefore, the observed persistence distributions of the 

discontinuities irrespective of their infill features can be 

considered to have an isotropic characteristic within the 

rock mass. 

Weak Discontinuity Thickness 

 Data analysis of the weak vein thickness distribution 

also showed that no differences between discontinuity sets 

from the same mapping scale at any sector could be 

observed. For example, Table 12 shows almost the same 

weak vein thickness distribution for each set of the data 

plotted in Figure 5b, which do not differ from when all 

discontinuities are considered (Tables 8–9). Therefore, the 

observed thickness distributions can also be considered to 

have an isotropic characteristic within the rock mass. 

Block Size 

 The weak discontinuity data set from oriented drill 

core logging was used to stochastically simulate the rock 

structure and to determine in situ block size distributions 

[Thompson 2002]. The results are presented in Figure 13. 

The figure clearly shows that when the strength of the 

discontinuities is taken into account, the andesite rock type 

from the Hw sector has a coarser in situ rock block size 

distribution than the Fw sector. These predicted differences shown in Figure 13 are in accordance with the actual 

observations at the mine site. However, these differences 

could not be established by applying rock mass classification schemes (see Table 1). 

Table 11.—Weak discontinuity trace length distribution for each family set observed (refer to Figure 5b) 

using line mapping at Hw sector 

Trace length distribution observed (%) 

Family set Data 

<0.5 m 0.5 to 

<1.0 m 

1.0 to 

<1.5 m 

1.5 to 

<2.0 m 

2.5 to 

<3.0 m 

3.0 to 

<4.0 m ≥4 m 

Set 1 (subvertical E–W) 52 3.8 23.1 15.4 11.5 21.2 11.5 13.5 

Set 2 (subvertical N–S) 44 4.5 15.9 29.5 18.2 11.4 9.1 11.4 

Set 3 (subhorizontal) 34 5.9 8.8 14.7 17.6 11.8 11.8 29.4 

Total and mean of Hw sector 134 4.5 17.2 20.1 15.7 15.7 10.4 16.4 

NOTE.—Data collection by line mapping used a threshold value of 0.3 m as truncation bias. 

0.0

0.1

0.2

0.3

0 .5 1.0 1.5 2 .0 2.5 3 .0 3.5 4 .0 4.5 0ver

Discontinuity trace lenght (m)

Observed frequency (%)

All disc. Fw

Weak Fw

0.0

0.1

0.2

0.3

0.5 1.0 1.5 2 .0 2.5 3.0 3 .5 4.0 4 .5 0 ver

Discontinuity trace length (m)

Observed Frequency (%)

All disc. Hw

Weak Hw

 Figure 12.—Discontinuity trace length from data collected in line mapping.

Table 12.—Weak discontinuity thickness distribution for each family set observed (refer to Figure 5b) 

using line mapping at Hw sector 

Thickness distribution observed (%) 

Family set Data 

<1 mm 1 to 

<2 mm 

2 to 

<3 mm 

3 to 

<5 mm 

5 to 

<10 mm ≥10 mm 

Set 1 (subvertical E–W) 53 13.2 35.8 24.5 13.2 9.5 3.8 

Set 2 (subvertical N–S) 46 4.3 65.2 17.4 8.8 — 4.3 

Set 3 (subhorizontal) 33 6.1 39.4 24.2 21.2 6.1 3.0 

Total and mean 136 9.6 46.3 22.1 13.2 5.0 3.7 

Discontinuity trace length (m) 
35 

DISCUSSION 

 Since conventional rock mass classification systems 

parameterized mainly open joints to assess rock mass quality [Barton et al. 1974; Hoek et al. 1995; Laubscher 1993], 

these schemes cannot be readily applied to the rock masses 

of the primary copper ore at the El Teniente Mine. Even 

the last upgrade of the Mining Rock Mass Rating (MRMR) 

[Laubscher and Jakubec 2000] cannot be applied because 

it does not take into account the multiple mineral ensembles of the vein infill. 

 Moreover, other discontinuity characteristics that are 

not clearly included in the classification schemes seem to 

play a relevant role during rock mass behavior. This is the 

case for discontinuity thicknesses, which have been found 

to control rock mass disassembly during caving and the 

fragmentation process [Brzovic et al. 2006; Brzovic and 

Villaescusa 2007]. 

 In addition, the discontinuity trace length plays a relevant role in rock mass characterization when a truncation 

bias length during data collection is adopted. This characteristic must be taken into account if comparisons between 

different mines are made. 

 The discontinuity features that have been found to be 

variable between mine sectors at the El Teniente Mine are 

the occurrence of veins and their infill characteristic. These 

particular geological characteristics combined with the 

in situ rock block distribution seem to be enough to characterize the rock masses of the primary copper ore. 

CONCLUDING REMARKS 

 A data collection campaign designed to characterize 

the rock structure of the primary copper ore was recently 

implemented at the El Teniente Mine in central Chile. The 

results indicated that mainly faults and veins are present 

within the rock mass. Subvertical faults were defined as 

widely spaced, while veins were present in high frequencies in at least three semiorthogonal orientations. Almost 

no joints were found within the rock mass. Moreover, 

negligible differences were observed in terms of in situ 

rock block distributions between the two studied sectors. 

 A definition of weak discontinuities has been adopted 

[Brzovic et al. 2006; Brzovic and Villaescusa 2007], which 

is based on the analysis of rock mass behavior during 

caving. Only when weak discontinuities are used, the rock 

mass characterization predicts differences between the two 

studied sectors. These differences, which are represented 

in terms of discontinuity frequency and in situ rock block 

distributions, are in accordance with actual observations at 

the mine site. 

 Truncation biases applied during data collection 

strongly affected the discontinuity set parameters, such as 

spacing and distribution of thicknesses, and in situ block 

size distribution. Some discontinuity parameters such as 

trace length and thicknesses have been found to have an 

isotropic characteristic within the rock mass. The discontinuity infill has also been found to have an isotropic 

characteristic, but only within each location, because it 

changes between sectors. It has also been found that longer 

discontinuities are statistically thicker than shorter ones, 

and the infill compositions do not change with their thicknesses. Most of these discontinuity characteristics were 

found for both weak and all discontinuities. 

 In conclusion, due to the geological features of the 

primary copper ore, rock mass classification schemes 

cannot be readily applied to characterize rock masses at the 

El Teniente Mine. 

ACKNOWLEDGMENTS 

 The authors wish to acknowledge the financial support 

of Codelco and the Western Australian School of Mines. 

Permission to publish this paper by Codelco-Chile, 

El Teniente Division, is also acknowledged. The authors 

also gratefully acknowledge the superintendence geology 

personnel for their collaboration, in particular, David 

Benado and José Seguel. 

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 Brzovic A [2001]. Fundamentos geológicos para un 

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 Figure 13.—In situ block size distributions from rock 

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oriented drill core logging (Oc). Light gray data are from 

Figure 8.

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0.01 0.1 1 10

Mean unfractured distance of rock block (m)

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Oc Fw (weak)

Oc Hw

Oc Fw

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37

STOPE DESIGN CONSIDERATIONS USING ROCK MASS CLASSIFICATION TOOLS 

AT THE XSTRATA ZINC GEORGE FISHER MINE 

By Geoff W. Capes,1

 Jeremy P. Doolan,2 and Leigh B. Neindorf3

ABSTRACT 

 The development and use of rock mass classification 

tools have been a key component for improving stope 

design over the past 4 years at the Xstrata Zinc George 

Fisher Mine in northern Queensland, Australia. In 2003, 

stope extraction data from 3 years of open-stope mining 

provided an excellent situation to review the assumptions 

in the feasibility study. Extracted stope profile information, 

drillhole geotechnical data, underground observations, and 

oral and written communication were used to develop a 

thorough stope reconciliation performance database. Without collecting the back analysis data and presenting the 

data in a usable format, engineers are left to debate opinion 

instead of engineered judgment. This can lead to biased 

and uninformed design parameter choices with the potential to repeat poor design. This paper demonstrates some 

effective, practical examples of empirical data collection 

where rock mass classifications tools were developed and 

used to create improved confidence in predicting stope 

stability and failure profiles. The work contributed to 

design changes that resulted in a reduction in stope 

hangingwall dilution and an increase in head grade while 

continuing to ramp up production from 2003–2005. 

INTRODUCTION 

 This paper describes the development and use of rock 

mass classification tools in stope design over the past 

4 years at the George Fisher (GF) North Mine. The deposit 

is located 22 km north along strike from the Mount Isa 

Lead Mine. A joint stope dilution study between the 

Xstrata Zinc George Fisher Mine (Figure 1) and the 

University of Saskatchewan, Canada, began in October 

2003. The objective was to create a thorough understanding of hangingwall (HW) overbreak using a sound 

geological engineering approach that focused on data 

collection of historic stope performance, rock mass classification, and underground observations of D ore body. The 

evolution of the work has created a better understanding of 

stope HW stability and created a change in mining 

methodology. The work contributed to a reduction in 

annual stope dilution from 14.4% (2003) to 6.3% (2005), 

an increase in zinc grade from 7.4% to 8.7%, and an 

increase in production from 2.1 to 2.6 Mt [Capes et al. 

2006]. 

BACKGROUND 

Geology 

 The George Fisher Mine contains two similar deposits 

located approximately 2 km apart with an estimated total 

resource of 127 Mt at 100 g/t silver, 5.3% lead, and 9.2% 

zinc (June 2005). The GF North deposit makes up approximately two-thirds of the current production, with the 

remaining ore coming from GF South (previously known 

as Hilton). The GF North deposit, the focus of this study, 

is composed of a series of stratiform ore bodies striking 

near north-south and dipping west between 30° to 70°. The 

sphalerite-pyrite-galena-pyrrhotite type mineralized areas 

are separated by various thicknesses of bedded shales and 

siltstones [Forrestal 1990]. 

 D ore body is currently the key area of extraction, 

grading at approximately 5% lead, 9% zinc, and 100 g/t 

silver with the mineralization being unequally distributed 

through the ore body, including a high-grade massive 

sulfide layer sometimes defining the ore/HW contact. 

Typically, the D ore body HW rock is composed of a 

siltstone marker rib followed by a package of pyritic shales 

 

 1

Ph.D. candidate, Department of Civil and Geological Engineering, University of Saskatchewan, Saskatoon, Canada, and 

senior planning engineer, Xstrata Zinc, George Fisher Mine, 

Mount Isa, Queensland, Australia. 

 2

Rock mechanics engineer, Xstrata Zinc, George Fisher Mine, 

Mount Isa, Queensland, Australia. 

 3

Project manager, Xstrata Zinc, Mount Isa, Queensland, 

Australia.

MOUNT ISA

QLD

WA

AUSTRALIA

SA

NT

NSW

VIC

TAS

Perth

Townsville

Brisbane

Cairns

Sydney

Canberra

Melbourne

Adelaide

Hobart

Darwin

HILTON

MINE

Airport

Barkly Highway

GEORGE

FISHER

ISA

MINE

Mount 

 Isa

Power Boulia Station

Cloncurry

CBD

Camooweal

Figure 1.—Location of George Fisher Mine.
38 

(5%–20% fine-grained pyrite) and a series of very fissile 

black shales (Figure 2). This is topped off with a variably 

thick mineralized lead-zinc lens. A section of siltstone 

separates this lens from the massive pyrite marker and 

massive sulfides, which define the start of C ore body. The 

thickness and rock mass quality of each package between 

D ore body and C ore body vary quite significantly along 

strike and dip as a result of past brittle and ductile 

deformation from faulting and folding events. A large 

number of geological structures affect the George Fisher 

deposit with varying degrees of offset, ore body rotation, 

drag folding, and metal redistribution [Grenfell and 

Haydon 2006]. The two main types of faults are crosscutting north-east trending faults and bedding parallel 

faults having their own individual characteristics and zone 

of influence. 

Stope Layout and Design 

 The mining method at GF consists mainly of 

transverse open-stope mining where the ore bodies are 

greater than 10 m in thickness. Transverse stopes are 

mined from a footwall drive access either on a 30- or 60-m 

sublevel spacing giving consideration to the local rock 

mass quality and stope shape relationship. Primary stopes 

are mined until uneconomic conditions exist and then are 

filled with either a cement aggregate fill or a paste fill 

material. A secondary stope is typically mined once the 

adjacent primaries have been mined to one 30-m sublevel 

higher then the secondary stope and the fill has been 

allowed to sit for 28 days and gain sufficient strength to act 

as a sidewall during extraction (Figure 3). The original 

“15–20” design (17.5-m crosscut centers), primary and 

secondary stope strike lengths, respectively, was changed 

in 2003 to a “10–20” design (15-m crosscut centers). This 

eventually became a floating design based on local rock 

quality where primary stopes could be up to 15 m on strike 

considering the cost of cemented backfill, local ore body 

width, HW stability, and effects on the mining cycle. In 

addition, the drill design of stopes within the George 

Fisher Mine underwent a change in 2003 when D ore body 

HW drives were eliminated and transverse stope crosscuts 

were used as the only access for D ore body extraction. 

One of the main motives behind this design change was 

13 Level

12 Level

11 Level

10 Level

60 m

Faults

West

 Figure 2.—Typical cross-section of George Fisher ore 

bodies.

FW Drive

HW Drive

D-orebody

C-orebody

N

Stope Crosscuts

HW and FW

Faults

Transvers Cross cut

Transverse Cross cut

Stope B

Stope A

West

Figure 3.—George Fisher mining layout (plan and cross-section views).
39 

the existence of problems with the stope hangingwalls and 

adjacent hangingwall drive failures as a response to stoping. The elimination of HW drives created the need for 

additional diamond drilling of the ore body to delineate the 

stope extraction wireframes. This addition of drilling and 

subsequent geotechnical logging has provided substantial 

relevant information for stope design and reconciliation 

purposes. 

 Numerical modeling provided an adequate set of rules 

for mine-wide sequencing [Beck 2003], which, when 

adhered to, lead to a sustainable mining method. The suggested guidelines included minimizing overbreak in 

primaries to avoid connection of voids when mining 

secondaries, tight-filling stopes, eliminating triple-lift 

pendant pillars, decreasing stope cycle time, and creating a 

better sequence to minimize stress effects on extraction. 

However, even with the mine-wide changes and minewide sequencing rules, a set sublevel spacing and crosscut 

width in poor-quality HW rock mass continued to result in 

unpredicted major individual stope HW failures and subsequent production inefficiencies. Thus, a thorough understanding of individual stope performance was required in 

order to achieve further positive design change. 

DEVELOPMENT AND USE OF 

ROCK MASS CLASSIFICATION TOOLS 

AND EMPIRICAL METHODS FOR DESIGN 

Overview 

 During late 2003, stope extraction data from 3 years of 

mining provided an excellent situation to review the 

assumptions in the feasibility study. Stope profile information based on data acquired using the cavity monitoring 

system [Miller et al. 1992], drillhole (BQ size) geotechnical data, underground observations, and oral and 

written communication was used to develop a thorough 

stope reconciliation performance database. On-site 

research, coupled with subsequent design trial and implementation, resulted in the development of a model that 

showed a relationship between rock mass quality, span, 

and resultant extraction profile. The goal of this model was 

to use it as a template and continually update the knowledge with underground observations and reconciliations of 

case histories. The model represented significant time, discussion, and research, but with more data, the model 

would evolve and lead to continued improvements in the 

understanding of HW behavior and design of stopes. 

Figure 4.—RQD long section contoured. 
40 

Phase 1: RQD Long Section 

 The first step of the study was to create a long section 

of the available geotechnical data. The comprehensiveness 

and density of geotechnical data depend on the year in 

which the drillhole was logged. Most holes after 1999 have 

RQD data available (BQ: 36.5–40.7 mm) as recommended 

by Hadjigeorgiou [1999]. Rock Quality Designation (RQD) 

[Deere 1964] was originally developed for drill core ≥54 

mm, although the “BQ RQD” is used to compare site data. 

The RQD data were averaged for the first 5 and 10 m of 

rock into the hangingwall and placed on a long section 

showing significant variation along strike and dip. The 5-m 

average RQD long section (Figure 4) has evolved from a 

scrappy, hand-contoured, coffee-stained desk map to a 

sharable plan able to be accessed by the technical services 

team on the mine design software. The original section 

was hand-contoured while the up-to-date section was contoured by triangulating the RQD values on the mine software as topographic surfaces between points and joining 

equal elevation points as contours. There is a substantial 

increase in RQD data density above the 2,800 elevation 

due to the removal of the HW drives and requirement for 

diamond drilling and subsequent geotechnical logging. 

Phase 2: Empirical Methods 

 The next step was to gather additional averaged data 

for use in empirical design methods, such as the modified 

stability graph method [Potvin 1988] and dilution graph 

method [Clark and Pakalnis 1997], which require a single 

estimate of rock mass properties for the surface being 

analyzed. This involved acquiring geotechnical parameters 

such as joint condition, joint alteration, and joint roughness 

used in the Q-system [Barton et al. 1974]. Based on many 

underground observations and stope reconciliations, the 

rock in the HW area was assigned three broad categories 

(Figure 5). Each stope was assigned values within a category and plotted with actual ELOS values using the 

dilution graph format. ELOS (“equivalent linear 

overbreak/slough”) is defined as the volume of the HW 

overbreak divided by the stope HW surface area [Clark 

and Pakalnis 1997]. ELOS is a useful tool for mine planning because it provides the ability to quantify a diluted 

stope shape to enhance mine scheduling purposes. The 

dilution graph was modified with new ELOS lines calibrated to data collected for stoping in poor-quality rock 

masses where minimal data have been presented except for 

Capes et al. [2005] and Brady et al. [2005] (Figure 6). 

The “calibrated” curves on the modified dilution graph 

(Figure 6, right) have been used to predict HW overbreak 

in both GF North and South operations for the last few 

years. The calibrated curves, which need to be statistically 

verified as a final part of the study, are an effective tool for 

local stope design as they contain a large amount of data in 

the required hydraulic radius (H.R.), modified stability 

number (N′) ranges. 

 However, further work was required to examine why 

some cases did not agree with the design approach based 

on the average rock mass conditions. When Category 3 

rocks (RQD < 10%–20%, N′ < 1) were analyzed, the failure prediction did not seem to correlate with the ELOS 

predictions based on a 5-m average of HW conditions. 

Two such examples included having a 6-m ELOS in a 

primary stope where <0.5 m was expected (N′ = 0.5, H.R. 

= 3) and an 13-m ELOS where approximately 4 m was 

expected (N′ = 0.8, H.R. = 7). For category 1 (RQD 

> 40%, N′ > 6) and category 2 (RQD 20%–40%, N′ = 1–4) 

rocks, the majority of cases of ELOS prediction were 

within acceptable error for stope prediction requirements, 

but an improved model was desired to examine the cases 

that did not fit. 

 Figure 5.—Examples of HW rock categories at George 

Fisher Mine.

• Category 2

RQD= 20-40%

(5m average)

Jn= 12

Jr = 1

Ja = 2

Q’ = 0.8-1.6

A =1, B=0.3, C= 4-7

N’ = 1 - 4

• Category 3

RQD<10-20%

(5m average)

Jn= 12-15

Jr = 1-2

Ja = 2-4

Q’ = 0.3-0.8

A =1, B=0.3, C= 4-7

N’ < 1

• Category1

RQD > 40%

(for at least 1-2m)

Jn < 6-9

Jr = 1

Ja = 1

Q’ = 4.4

A =1, B=0.3, C= 4-7

N’ > 6
41 

Phase 3: Rock Category and Failure Profile 

Relationships 

 

 The next step was to investigate the additional factors 

that have been documented to relate to HW overbreak 

from a comprehensive literature review and studies conducted at the George Fisher Mine. These additional factors, including faulting, stress, blasting, undercutting, and 

time, were identified to examine how or if they played a 

role in erroneous ELOS prediction. During this next phase 

of the study, interesting relationships were observed 

between failure shape, rock quality, and span when the 

RQD drillhole data were overlain as logged intervals on 

HW failure profiles in the mine design software. Thus, the 

methodology was changed to first investigate these novel 

relationships before examining the additional factors. 

 RQD cross-section plots were created for individual 

stopes and provided valuable data for understanding the 

variability of rock quality near the HW/ore boundary 

(Figure 7). The RQD plots coupled with underground 

observations created the idea of different failure profiles 

(Figure 7) for the different rock mass categories shown in 

Figure 5. Areas where the RQD < 10%–20% typically 

failed into the next rock mass category without arching. 

Areas with RQD 20%–40% would arch to become stable 

within span constraints or would change profile when a 

different rock category was intersected. Stability existed in 

other stopes where the stope failed to a composite beam or 

plate of rock in which the RQD > 40% for at least 1 m. 

These relatively thin zones of higher RQD rock define a 

more stable domain, and the position of this domain was 

found to define the extent of failure, within span 

constraints. These stable domains were not always a 

consistent lithology, but could often be defined as pyritic 

shales, siltstone beds, or a narrow mineralized area. Data 

were collected to create a design tool demonstrating the 

stability relationship between the composite beam 

thickness (meters of BQ core >40% RQD) and span 

(Figure 8). Figures 9–12 demonstrate four case histories of 

the model incorporating the three rock categories. 

Recognizing the changing rock mass condition with 

Dilution Graph Modified Dilution Graph

George Fisher Rock Category

1

3

2

 Figure 6.—Dilution graph (from Brady et al. [2005]) and modified dilution graph with additional data from poor-quality 

rock masses (after Capes et al. [2005]). 

15 m HW

Plan View

N

Representative RQD Plot

0

10

20

30

40

50

60

70

-10 -5 0 5 10

Distance from HW(m)

RQD%

Orebody Hangingwall

3

1

2

Arching Stable 

Beam/Plate

Failure 

Profile

 Figure 7.—RQD plot and representative failure 

shape profiles based on rock category and span.
42 

distance from the hangwall contact enabled an improved 

distance from the hangingwall contact enabled an 

improved and accurate method of estimating HW dilution. 

RQD was noted as the most significant contributor as a 

geotechnical input into stope design and is the most 

available and most easily obtained data. Stope performance prediction now includes a cumulative overbreak prediction where the extraction profile is estimated through 

the different domains to create an expected stope shape for 

mine planning. Further work is being conducted to look at 

correlations between arching failure angle, span, and RQD. 

This may be difficult to quantify due to the lack of available data to estimate bedding perpendicular joint spacing 

for individual stopes. 

Beam thickness (m) where beam RQD > 40%

Span (m) 

sublevel height = 30 m, dip =40-70

0

5

10

15

20

25

012345

FAILED

STABLE

Beam stable under span

Beam failed under span

 Figure 8.—Composite beam thickness versus span stability graph.

RQD Plot 716D 12C-11L (7163N Central)

0

10

20

30

40

50

60

70

-10 -5 0 5 10

Distance from HW Contact(m)

RQD%

ORE HW

D-FW D-HW

Drillhole

Plan View 20% >40%

West

>40% 20% Section View

West

D-FW

D-HW

 Figure 9.—716D 12C–11L. Secondary stope where a 

major failure was expected using predicted stress conditions. Local rock mass conditions limited depth of failure 

to 2–3 m as stope failed through category 3 rock to the 

category 1 composite beam (February 2004). 

20-40% < 20%

West Section View

HW FW

D-FW D-HW

<20% 20-40% Drillhole

West Plan View

RQD Plot - 742D 10L-10C (7440N Upper)

0

10

20

30

40

50

60

70

-10 -5 0 5 10

Distance from HW (m)

RQD %

ORE HW

 Figure 10.—742D 10L–10C. Primary stope where 6- to 

8-m depth of HW failure was predicted. Failure depth was 

8–10 m. Stope failed without arching through first 5 m of 

category 3 rock, then arched off to a stable profile through 

category 2 rock (September 2006).
43 

RQD Plot 742D 12C-11L(7440N upper)

0

10

20

30

40

50

60

70

-10 -5 0 5 10

Distance from HW(m)

RQD%

Orebody Hangingwall 

First Beam Second 

Beam

D-FW

D-HW

Drillhole

>40% <20% 20-40%

First Beam

Second Beam

Plan View West

 11-m HW span 17-m HW span 

NOTE: Dot represents similar position under each span. Span was increased in the upper left of photo.

 Figure 11.—742D 12C–11L. First composite beam (category 1) was stable under 11-m span, but failed under a 17-m span, 

arching through the category 2 rock to the second composite beam (May 2004).
44 

Phase 4: Additional Factors 

 With the improved model for predicting HW 

performance developed, the effect of faulting, blasting, 

stress, undercutting, and time were then investigated to 

examine how much of a role they played in stope HW 

stability. 

 The effect of faults at the mine was debated, as some 

stopes with faults passing through had major overbreak, 

while others remained stable under similar stoping conditions. Some trends between faulting and lower RQD values 

were evident, but were not consistent. The RQD long section showed that RQD and stope stability could not consistently be related to the location of faulting, although 

poor rock quality can be associated with some of the faults 

(Figure 5). For example, there has been failure next to the 

S73 fault on the majority of levels where the stopes have 

been mined to the north of the fault (Figure 13). This correlation cannot be seen with all the north-east trending 

faults and agrees with the geological descriptions that the 

major faults show variable dextral offset, displacement and 

features [Grenfell and Haydon 2006]. 

 Blasting has not been analyzed to the same degree as 

the other factors. This is due to the consideration of a 25-mwide ore body, the failures are occurring at a depth much 

greater than the influence of ANFO higher-density explosive (approximately 0-2 m back break based on local opinion) or ISANOL lower-density explosive (approximately 

0- to 0.5-m back break) in the HW ring of an 89-mm-diam 

production hole, and that the majority of HW rings have 

been charged with low-density explosive. The study has 

only touched on a few areas where data have been 

available to show the effect of using low- or high-density 

explosive near a category 1 rock mass. This is shown as 

the explosive-sensitive zone on the composite beam versus 

span stability plot (Figure 8). 

 The performance of primary and secondary stopes was 

compared to examine the effect of the different stress conditions under which the stopes were mined. There were no 

consistently observed trends to indicate that stress levels 

influenced stope HW behavior on a mine-wide scale based 

on a comparison of depth of HW failure in primary stopes 

and secondary stopes in the same area of the mine. The 

comparison of depth of failures averaged 4.2 m for secondary stopes (58 samples) and 4.3 m for primary stopes 

(75 samples). However, secondary stopes have most often 

either performed in a stable manner or had >5-m depth 

failure, with significantly fewer cases in the 0- to 5-m 

depth of failure category (Figure 14). The sizes of primary 

and secondary stopes have varied through the life of the 

mine, providing a good spread of data for comparison. 

Stope HW performance can be seen as a function of individual stope design choices, where the stope depth of 

failure is plotted on a long section with rock quality 

(Figure 13). Secondary stopes extracted with adequate 

sequencing have performed well under larger HW spans 

than primary stopes where the HW RQD in the secondary 

stope has been better than that of the primary. On the other 

hand, there have been additional HW and back failures in 

secondary stopes where mining has been conducted out of 

sequence (triple-lift pendants), where cemented fill has 

failed in primaries, and some areas where primary stopes 

in the lift above were not filled for up to 8 months, 

resulting in additional fall-off and a subsequently worse 

Category 1 Rock 

Category 3 Rock 

Figure 12.—Looking west through the open stope. Photo shows failure through category 3 to category 1 rocks. 
45 

stress condition for mining the secondaries. During 2006, 

significant delays in cement filling and/or lack of tight filling of previous lifts of the primaries were experienced. 

Three of the four secondary stopes (719D, 723D, 730D 

10L–10C) experienced large back failure/caving above the 

designed stope level. One particular stope had 10 m of HW 

failure and caved 20 m above the designed stope level 

when it was predicted to have 2–3 m of HW failure based 

on the cumulative overbreak model. The primary stopes 

had all been mined at a 15-m strike span to access the 

tonnes sooner and were left open for up to 254 days, during which time sidewall failures occurred off of the secondary stope pillars, resulting in a stope <15 m wide. It is 

important to capture such information to verify the limits 

to which the design model can be applied and to reiterate 

the potential downstream mining-influenced effects where 

sequencing and backfill conflict with meeting production 

targets. 

 Current work is being completed to determine the 

effects of producing stopes adjacent to voids that have 

remained open for extended periods of time. The mining 

cycle, from firing to filling, is being analyzed with respect 

to stope behavior prediction and performance, with emphasis on time-related failure of the stopes. Analysis to date 

has shown that relaxation/failure of the secondary pillar 

seems to be reduced by the timely filling of the primary 

stopes on the same level as well as the next lift above. 

Figure 13.—Long section with faults, stopes, and depth of failure (plotted at stope centerlines).

0

5

10

15

20

25

30

< 0 m overbreak 0-5 m Depth of failure > 5 m depth of failure

Depth of Failure Category

Number of Cases

Primary

Secondary

 Figure 14.—Comparison of failures in primary and 

secondary stopes.
46 

Being able to recognize potential poor stope performance 

at an early stage in the design process will allow for more 

efficient mine planning and extraction, with manageable 

dilution. So far, telltale signs of poor-performing stopes 

have been mining difficulties when establishing secondary 

crosscuts, such as arching failure of the crosscut profiles, 

and difficulty installing ground support and reinforcement. 

HW and/or back overbreak from the previous lift must also 

be considered to determine stope HW design choices for 

the next lift. In conjunction with the model, the timerelated failure study, together with observational data, 

should give the planning team more insight into predicting 

future stope performance and managing stope stability. 

Summary: Results of Trials and Methodology 

 Management and operational support to implement 

changes to individual stope designs was very positive, and 

many ideas were discussed to improve mine performance 

based on the understanding of HW behavior. Based on the 

cumulative overbreak model where the rock mass conditions meet the specific criteria, these design choices 

include the verticalization of HWs, use of cabled and noncabled ore chocks for HW stability, use of ore skins where 

mining consequences from HW failure are high, and an 

effective rock mass management strategy [Capes et al. 

2005]. The development and use of the model resulted in 

significant research and development benefits to the company. Figure 15 shows the contribution of the stoping 

methodology change as a reduction in annual stope dilution from 14.4% (2003) to 6.3% (2005), an increase in zinc 

grade from 7.4% to 8.7%, and an increase in production 

from 2.1 to 2.6 Mt [Capes et al. 2006]. 

CONCLUSIONS 

 Dedicated on-site research using a sound geological 

engineering approach of data collection, communication, 

underground observations, analysis, and implementation of 

ideas resulted in a thorough understanding of HW 

overbreak. This was achieved by creating a model for 

design based on historical performance, understanding 

why it worked and why it did not work, appreciating it for 

the insights it provided, and continually reanalyzing as 

more information became available. The study has 

reemphasized the belief in understanding relevant factors 

for individual stope design. The averaging approach for 

estimating rock mass properties for design methods does 

not always provide the best answer based on available 

information. Many considerations must be given to design 

individual stopes. Generalizing all stope designs into one 

category can lead to poor design. Individual fault characteristics play a role in stope stability. There is no discernible difference in average HW depth of failure between 

primary and secondary stopes, although mining out of 

sequence, triple-lift pendants, and poor cement fill quality 

or lack of tight fill and/or delayed fill in primary stopes 

may lead to greater HW failure and create subsequent 

stope back failure. The model created the ability to 

develop innovative empirical stope design tools to control 

and reduce dilution in conjunction with following a set of 

mine-wide extraction rules developed from numerical 

modeling. The development and use of the model resulted 

in significant research and development benefits to the 

company. The confidence in prediction created individual 

stope design changes that contributed to a reduction in 

dilution and an increase in head grade while continuing to 

ramp up production from 2003–2005. 

ACKNOWLEDGMENTS 

 The authors would like to thank Xstrata Zinc and the 

University of Saskatchewan for their support, as well as 

the various technical services and operations personnel and 

consultants who have shared their thoughts during their 

time at George Fisher Mine Operations. 

REFERENCES 

 Beck DA [2003]. Stress influences on rockmass damage and stope stability at GF mine-calibrated model criteria. Consultant report for Xstrata Zinc, George Fisher 

mine. 

 Barton N, Lien R, Lunde J [1974]. Engineering classification of rock masses for the design of tunnel support. 

Rock Mech 6(4):189–236. 

 Brady TM, Pakalnis R, Clark L [2005]. Design in 

weak rock masses: Nevada underground mining operations. SME preprint 05–43. Littleton, CO: Society for Mining, Metallurgy, and Exploration, Inc. 

 Capes GW, Grant DR, Neindorf LB [2006]. George 

Fisher: sustainable mine design. Paper presented at the 

Second International Seminar on Strategic Versus Tactical 

Approaches in Mining (Perth, Australia). 

 Capes GW, Milne DM, Grant DR [2005]. Stope 

hangingwall design approaches at the Xstrata Zinc, George 

0

2

4

6

8

10

12

14

16

2002 2003 2004 2005 2006

Year

Dilution%

7.2

7.4

7.6

7.8

8

8.2

8.4

8.6

8.8

Dilution%

Zinc Grade

Zinc Grade%

2.6 Mt

2.4 Mt

2.1 Mt

 Figure 15.—George Fisher North stope dilution percent, total mine tonnes, and zinc grade versus time.
47 

Fisher mine, North Queensland, Australia. In: Chen G, 

Huang S, Zhou W, Tinucci J, eds. Proceedings of the 40th 

U.S. Rock Mechanics Symposium (Anchorage, AK, June 

25–29, 2005). Alexandria, VA: American Rock Mechanics 

Association. 

 Clark L, Pakalnis R [1997]. An empirical design 

approach for estimating unplanned dilution from open 

stope hangingwalls and footwalls. Presented at the 99th 

Canadian Institute of Mining Annual Conference (Vancouver, British Columbia, Canada). 

 Deere DU [1964]. Technical description of rock cores 

for engineer-purposes. Rock Mech Eng Geol 1(1). 

 Forrestal PJ [1990]. Mount Isa and Hilton silver-leadzinc deposits. In: Hughes FE, ed. Geology of the mineral 

deposits of Australia and Papua New Guinea. AusIMM 

Monogr 14:927–934. 

 Grenfell K, Haydon M [2006]. Challenges in modelling large complex orebodies at the George Fisher north 

mine. In: Proceedings of the Sixth International Mining 

Geology Conference (Darwin, Northern Territory, Australia, August 21–23, 2006). 

 Hadjigeorgiou J [1999]. Memorandum of March 11, 

1999, from John Hadjigeorgiou, Laval University, Quebec, 

Canada: a discussion document on geotechnical rock mass 

characterisation for Mount Isa. 

 Miller F, Jacobs D, Potvin Y [1992]. Laser measurement of open stope dilution. CIM Bull 85:Jul-Aug. 

 Potvin Y [1988]. Empirical open-stope design in 

Canada [Dissertation]. Vancouver, British Columbia, 

Canada: University of British Columbia. 

49

AN INTEGRATED APPROACH TO SUPPORT DESIGN 

IN UNDERGROUND COAL MINES 

By Murali M. Gadde,1

 John A. Rusnak,2 and Christopher Mark, Ph.D., P.E.3

ABSTRACT 

 Rock mass classification systems are extremely useful 

for site characterization and have been employed by the 

rock mechanics community for several decades. While 

empirical in nature, the classification systems provide a 

viable means to quantify the nature of rock mass, which is 

necessary for stability analyses. In U.S. coal mines, the 

Coal Mine Roof Rating (CMRR) is the most widely used 

classification system for several purposes, including support selection, chain pillar design, assessing the stability of 

extended face cuts, etc. The Analysis of Roof Bolt Systems 

(ARBS) is an empirical method developed from the 

CMRR to guide selection of roof bolts as the primary 

support system in U.S. coal mines. In this paper, the 

experience of Peabody Energy in applying ARBS to support design is discussed. In general, data from the Peabody 

mines show that ARBS predictions match well with field 

conditions. Peabody, however, does not use ARBS as the 

stand-alone methodology for support selection. Peabody 

uses a two-pronged approach in which the support requirement is initially estimated from the classification method, 

and then numerical modeling is used to select the proper 

reinforcement system. Such an integrated approach is 

necessary, as ARBS suggests only the “amount of steel” 

that may be used to support the roof and does not specify 

which type of roof bolt to use. A case study is used to 

demonstrate the usefulness of ARBS and Peabody’s integrated approach to support design. Also, the application of 

ARBS at several Peabody mines showed a very good 

correlation with support cost. The correlation indicated a 

direct relation between bolting cost and the ARBS value. 

INTRODUCTION 

 A rock mass is an extremely complex material to deal 

with quantitatively. This is further so when one attempts to 

describe its “quality” in relation to its engineering 

behavior. Several factors influence rock mass behavior, 

including the number, nature, and spatial distribution of 

discontinuities traversing through it; compressive strength 

of the rock matrix; presence of water; etc. Despite the 

complexity, efforts have been made to provide quantitative 

descriptions of rock mass quality, which are indispensable 

for engineering analyses. 

 During the past 4 decades, significant progress has 

been made in quantitative rock mass site characterization, 

especially through the development of rock mass classification systems. The most notable of these systems are 

Bieniawski’s [1973] Rock Mass Rating (RMR) and the 

Rock Mass Quality Index (Q) proposed by Barton et al. 

[1974]. Common to these and other classification systems 

is the selection of a few significant variables that have the 

most bearing on rock mass engineering behavior. Each of 

these variables are assigned numerical values that reflect 

their importance in controlling such behavior. After 

individual ratings are assigned to the significant parameters, they are mathematically manipulated to obtain one 

final number, which provides a quantitative description of 

the nature of the rock mass. 

 Both the RMR and Q were developed mainly based on 

case histories from tunnels driven in “hard rock.” As a 

result, they cannot be directly extended for use with coal 

measure rocks, as the parameters that influence the 

response of the rock mass are different. Several classification schemes applicable for coal measure rocks have been 

developed by various researchers, the most popular of 

which is the Coal Mine Roof Rating (CMRR) developed 

by the U.S. Bureau of Mines [Molinda and Mark 1994]. 

This system follows Bieniawski’s RMR format with values 

ranging from 0 to 100 to indicate the quality of the strata. 

The CMRR is most widely used in the United States and 

Australia; it has also been employed in South Africa, 

Canada, and the United Kingdom. The most recent of the 

coal mine classification systems is the Coal Measure 

Classification (CMC) proposed by Whittles et al. [2007]. 

 Over the years since its inception, the CMRR has been 

used for several purposes in coal mine strata control. 

Correlations have been developed to select roof bolts as 

the primary support system, in sizing longwall chain pillars, to forecast if extended face cuts will work or not, and 

several others [Mark and Molinda 2005]. The main interest 

of this paper is the usefulness of the CMRR and its 

offshoot, the Analysis of Roof Bolt Systems (ARBS) 

[Mark et al. 2001], for roof bolt design as they are applied 

to Peabody Energy mines. Also, the limitations of ARBS 

are pointed out, and the integrated approach that Peabody 

has developed to overcome some of the problems is 

discussed. 

 

 1Senior engineer, Peabody Energy, St Louis, MO. 

 2

Vice president of engineering, Peabody Energy, St. Louis, 

MO. 

 3Principal research mining engineer, Pittsburgh Research 

Laboratory, National Institute for Occupational Safety and 

Health, Pittsburgh, PA.
50

THE COAL MINE ROOF RATING (CMRR) 

 The CMRR has two different versions: field [Molinda 

and Mark 1994] and drill core [Mark et al. 2002]. The field 

CMRR is estimated from underground observations where 

the roof is exposed mainly by roof falls and overcasts. 

Since visual observations play a key role in the field 

CMRR, it is somewhat subjective. Two different persons 

are likely to come up with different field CMRR values for 

the same site, although experience has shown that they will 

usually differ by no more than about five points. In 

contrast, drill core CMRR is derived from laboratorydetermined parameters and measurements on cores, which 

are less subjective, but are subject to their own variability. 

 In both the field and drill core CMRR, the following 

parameters are weighed to estimate the roof competence: 

• Compressive strength 

• Discontinuities 

In the field CMRR, the discontinuities are characterized by 

their cohesion, roughness, spacing, and persistence. In the 

core version, discontinuity ratings are determined by the 

fracture spacing and diametral point load strength. 

 The process of computing CMRR starts by dividing 

the roof into structural “units.” Strength and discontinuity 

ratings are then determined and added together to calculate 

the “unit ratings.” The unit ratings are then corrected for 

the number of discontinuity sets and the moisture sensitivity. Next, the overall CMRR of the roof is obtained by 

thickness-weighted averaging of the unit ratings within the 

“bolted interval.” Adjustments are then applied to the average CMRR for the following factors to determine the final 

rating: 

• Strong bed in the bolted interval 

• Number of units 

• Groundwater 

• Overlying beds 

 The structure of the CMRR as given above seems to 

work well in quantifying the quality of roof for most situations. One important assumption in the development of 

the CMRR is that the bedding plane is the major discontinuity in a coal mine. Since bedding planes are almost 

always horizontal to subhorizontal, their orientation is not 

a key factor in determining the roof stability. This is the 

reason why the orientation of a discontinuity has not been 

accounted for in the CMRR. The presence of other discontinuities, such as slickensides, is considered in the 

CMRR, but the orientation of those features is not. 

 There are situations, however, where practical experience indicates that the orientation of these features may be 

a critical factor in determining the support requirements, 

as indicated by the rock fall shown in Figure 1. This photo 

was taken at a mine that has frequently occurring slickensided slip planes, which intersect at unfavorable angles to 

create wedge failure conditions. The immediate roof at the 

mine is made of black shale overlain by a very competent 

limestone. The CMRR for this roof was estimated to be 

over 60. Despite this high CMRR, several rock falls have 

occurred at the mine mainly because of the unfavorably 

oriented slickensided slip planes. 

 In cases like these, a discontinuity orientation adjustment to the CMRR can help to make it more general. This 

additional correction, however, is not essential for every 

application. It may be applied only if the instability is governed by the orientation of the discontinuity and occurs 

very frequently in a panel to make it a “general” feature 

rather than an isolated abnormality. Further, the correction 

is needed only if it is intended to deal with any instability 

originating from the discontinuity orientation by the primary support system. Even though several possibilities 

exist theoretically, practical experience in coal mines indicates that orientation-related rock falls are unlikely unless 

multiple features intersect at adverse angles. Based on the 

experience at the one case history mentioned above, the 

tentative suggestion in Table 1 is made to account for the 

discontinuity orientation in the CMRR. The adjustment 

shall be applied only to the unit ratings of the units that are 

intersected by the discontinuities. 

Table 1.—Adjustment in the CMRR for the 

unfavorable discontinuity orientation 

Condition 

CMRR 

adjustment 

Multiple discontinuities (joints, slip planes, etc.) 

 intersecting at adverse angles to create sliding 

 or wedge failure conditions ................................. –5 

 Figure 1.—Roof fall initiated by unfavorably oriented 

slip planes. 
51

ROOF BOLT DESIGN USING ARBS 

AND THE CMRR 

 One of the most important applications of the CMRR 

is to design roof bolts as the primary support system. This 

is accomplished through an empirical approach called the 

ARBS [Mark et al. 2001]. Roof fall rates from 37 U.S. coal 

mines formed the necessary database for the logistic 

regression analyses conducted in ARBS. The final guidelines help in the selection of roof bolt pattern, bolt length, 

and intersection span based on the CMRR and other 

geomining inputs. ARBS is valid, however, only if the 

bolts work in beam building or supplementary support 

mode. A discriminant equation in terms of depth and 

CMRR was developed to determine which support mechanism was applicable for a given mining condition [Mark et 

al. 2001]. In ARBS, the required bolt density is given by a 

parameter, PRSUPG , as given below: 

 PSUPG = (SF) [0.3 (IsG – Is)] [(5.7 log10 H) 

– (0.35 CMRR) + 6.5] (1) 

where SF = stability factor, 

 IsG = suggested intersection span, ft, 

 Is = actual intersection span, ft, 

 H = depth of cover, ft, 

and CMRR = coal mine roof rating. 

 The key advantages of ARBS are that it is simple to 

use and it is based on actual case histories. Therefore, 

a large number of uncertainties associated with coal mine 

ground control designs are inherently included in the statistical analyses conducted for ARBS. However, just like 

any other empirical tool, ARBS has its own limitations. 

First, the design equations cannot be extrapolated with 

confidence beyond the range of the original data. Second, 

some critical equations were developed from rather limited 

amount of data and thus should be used with caution. 

Third, and most importantly, ARBS does not specify 

which type of bolt to use in providing the bolt density 

given by Equation 1. 

 In U.S. coal mines, several different types of roof bolts 

are used, and each one works on a different mechanism 

[Dolinar and Bhatt 2000]. For example, the reinforcing 

action in fully grouted bolts is different from resin-assisted 

mechanical bolts. Unfortunately, the selection of bolt type 

is an extremely complex problem that cannot be addressed 

by a simple approach like ARBS. For this reason, Peabody 

uses an integrated approach wherein the support requirement is first estimated by ARBS, then the bolt type is 

chosen with the help of numerical modeling. 

 In the following sections, Peabody’s experience with 

the application of ARBS and details on the Integrated Support Design Methodology (ISDM) are presented. 

PEABODY EXPERIENCE 

 Roof bolt design at most Peabody mines has evolved 

over the years by trial and error and limited engineering 

studies. Bolt pattern, bolt type, entry width, etc., were 

changed until each operation found the best system that 

worked for its conditions. Therefore, this database would 

form a very reliable check on the validity of the CMRR 

and ARBS. Data have now been collected from several 

operating Peabody mines located throughout the major 

U.S. coalfields. The number and location of the mines covered in this study are listed in Table 2. Some details 

relevant to the estimation of the CMRR and ARBS for the 

studied mines are provided in Table 3. The data were 

collected from both the mains and the panels at each 

operation. In the areas of adverse roof conditions, some 

secondary supports were also installed. The number of 

MSHA-reportable roof falls per 10,000 ft of development 

was collected from the mines that had noticed some 

instability. The data are given in Table 4 and plotted in 

Figure 2 against the CMRR. Five mines, with CMRR 

values ranging from less than 30 to nearly 50, have experienced roof fall rates less than 0.2 per 10,000 ft of development. The roof fall rates at three other mines were 

significantly higher. 

Table 2.—Number of mines by coalfield 

used in the study 

Coalfield/State No. of 

mines 

Interior Province: Eastern Region 

 (Illinois Basin).................................. 8 

Appalachian........................................ 4 

Colorado............................................. 1 

Table 3.—Different variables relevant to the CMRR and 

ARBS at the case study mines 

Depth, ft........................................ 150–1,400 

Entry width, ft................................ 16–22 

Actual intersection span, ft ........... 27–35 

Steel grade, ksi............................. 40, 50, 60, 75 

No. of bolts per row ...................... 3–6 

Bolt row spacing, ft ....................... 3.5–5 

Bolt length, ft................................. 3.5–10 

Bolt diameter, in............................ 0.625–0.875 

Bolt type........................................ RAM, FGR, TT 

Accessories .................................. Wood boards, square steel 

plates, straps, and wire mesh

RAM = resin-assisted mechanical. FGR = fully grouted rebar. 

TT = torque tension. 
52

 At each of the studied operations, the CMRR was estimated from either underground observations or drill core 

data. Then, the suggested ARBS from Equation 1 was 

computed and compared with the actual value based on the 

successful roof bolting system. In estimating the suggested 

ARBS value, the stability factor was set to 1.0 in Equation 

1. The derived numbers are given in Table 4 and plotted in 

Figure 3. 

 The data in Table 4 show that the CMRR values at the 

studied mines varied from 24 to 48. Further analysis indicated that all eight Illinois Basin mines had CMRR values 

equal to or below 40, while all of the Appalachian mines, 

except one from the northern Appalachian coalfields, had 

CMRR values that exceeded 40. 

 Table 4 and Figure 3 further show that, in general, 

there is excellent agreement between the suggested and 

actual ARBS values at the studied mines. The three mines 

(E, F, and G) with the lowest ARBS stability factors also 

have the highest roof fall rates. These data support the 

validity and usefulness of ARBS in predicting the primary 

support requirements. 

 Since ARBS is an indirect measure of the amount of 

steel installed for roof support, it is logical to expect a 

good correlation with the support cost. To verify this, data 

Table 4.—Suggested and actual ARBS values 

at the case study mines 

Mine CMRR Suggested 

ARBS 

Actual 

ARBS 

Actual-tosuggested 

ARBS ratio 

No. of roof falls 

per 10,000 ft of 

development 

A..... 39 5.6 4.9 0.88 0.074 

B..... 37 7.1 6.5 0.92 0.082 

C .... 40 6.5 6.7 1.03 — 

D .... 40 6.8 6.1 0.89 — 

E..... 38 7.4 5.6 0.75 0.490 

F..... 28 11.8 9.1 0.77 0.540 

G .... 24 13.4 9.5 0.71 1.200 

H .... 36 7.2 6.9 0.96 0.069 

I ...... 29 13.4 12.0 0.90 0.130 

J ..... 44 8.0 7.0 0.88 — 

K..... 48 6.7 6.8 1.01 0.089 

L ..... 46 7.0 7.3 1.05 — 

M .... 43 9.9 11.5 1.16 — 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 10 20 30 40 50 60

CMRR

Falls/10000ft drivage

 Figure 2.—Roof fall rate against CMRR for some case 

study mines. 

0.0

3.0

6.0

9.0

12.0

15.0

0.0 3.0 6.0 9.0 12.0 15.0

Suggested ARBS

Actual ARBS

 Figure 3.—Suggested versus actual ARBS values at 

the case study mines. 

0

2

4

6

8

10

12

14

0.0 3.0 6.0 9.0 12.0 15.0

ARBS value

 $ Cost per linear foot 

 Figure 4.—Relation between the ARBS value and the 

average cost per linear foot of advance over a 6-month 

period. 
53

were collected from the same mines in Table 4, which are 

plotted in Figure 4. A direct linear relation between ARBS 

value and the support cost can be seen from this figure. 

Note that the cost shown is the average value for a 6-

month period for each mine and includes only the steel 

(bolt + plate) used for the primary support. This analysis 

shows that ARBS will also serve as an indicator of the 

support cost incurred in installing the bolt density suggested by it. 

INTEGRATED SUPPORT DESIGN 

METHODOLOGY (ISDM) 

 Within the confines of the original database, discussions in the preceding section have shown the effectiveness of ARBS in estimating the required support density. 

For complete roof bolt design, however, it is also necessary to know which type of bolt to use for any given 

mining conditions. While providing the same support 

resistance, different types of bolts will provide different 

levels of reinforcement depending on how harmonious the 

bolt type is with the roof conditions. Any incompatibility 

may lead to instability or uneconomic designs. 

 Unfortunately, no scientific guidelines exist that help 

determine the matching bolt type for a given roof. The best 

available alternative is to use numerical modeling wherein 

the specific geomining conditions are simulated with 

different types of bolts to determine the best one. Therefore, a very effective roof bolt design can be achieved by 

combining the positive aspects of ARBS with those of 

numerical modeling. In fact, achieving such a fusion is the 

essence of Peabody’s Integrated Support Design Methodology (ISDM). The individual steps in this process are 

shown in Figure 5. With some site-specific alterations, this 

methodology is being implemented in all of the new support design exercises at Peabody mines. 

 The ISDM process clearly recognizes the intractability 

of strata control designs by any single approach. The 

ISDM aims to maximize the benefits of empirical and 

analytical methods, neither of which alone can provide 

answers to all of the questions in support design. As Figure 5 indicates, one of the most critical elements of the 

End

Estimate the CMRR from field 

observations or drill core. 

Build and solve the numerical model explicitly 

incorporating the identified discontinuities and 

roof bolts on the pattern given by ARBS in as 

many steps as the mining sequence would require. 

Gather roof lithology, in situ stresses, and the 

mining geometry needed to build the 

numerical model. 

Estimate the required bolt 

density using ARBS. 

From underground observations or drill 

core, identify the location of the weakest 

bedding planes or other discontinuities in 

the roof. 

Get the properties required to define the 

constitutive behavior of different rocks. 

If available, get the pull test data to define 

the behavior of bolt-grout-rock interfaces. 

Repeat the previous step with a change in the 

bolt type in each model until all candidate 

bolt types are evaluated. 

Select the design criteria (change in 

displacements, weakness plane slip and 

separation, yield zones, etc.). 

Choose the bolt type that provides the best 

performance in relation to the chosen 

design criteria. 

Figure 5.—Individual steps in Peabody’s ISDM. 
54

ISDM is to identify the weakest bedding planes in the roof 

and explicitly include those features in numerical models. 

Such detailed analysis is not possible any other way but 

through numerical modeling. Also, different types of roof 

bolts could easily be simulated with numerical methods. 

More details on the individual steps in Figure 5 are provided below with the help of a recent support design exercise carried out for a Peabody mine. 

ISDM EXAMPLE 

 The mine in this case study will extract a coal seam of 

variable thickness with a final mining height of about 7 ft. 

The immediate roof at the mine is predominantly shale. At 

a few places, where the coal seam is thicker than 7 ft, 

a rider coal forms the immediate roof. Although over the 

bulk of the reserve the bolted horizon consists mainly of 

shale, at a few locations sandstone comes close enough to 

the seam to be a part of the bolting horizon. The average 

depth of the seam is about 680 ft. Exploratory drill core 

was available from 11 boreholes with all of the necessary 

rock strength information to estimate the CMRR. 

 The following discussion illustrates each step involved 

in arriving at the recommended support design using the 

ISDM: 

Step 1: Estimate the CMRR. 

 From the available 11 core holes, the CMRR was estimated and the numbers are shown in Figure 6. 

Step 2: Use ARBS to determine the required bolt density. 

 Since the CMRR is reasonably consistent (as seen 

from Figure 6), the average value of 41 was used as the 

input for ARBS. Based on this and other mining inputs, it 

was found that for the proposed 18-ft-wide entry at the 

mine, four 6-ft-long #6, grade 60 bolts on 3.5-ft row spacing will provide a stability factor of 1.25. Since this bolt 

pattern will provide a stability factor in excess of the one 

recommended in ARBS, the design has been accepted. It 

may be mentioned that this is just one of the several support patterns suggested for different geomining conditions 

at the study mine. 

Step 3: Gather necessary inputs for modeling. 

 The next step in ISDM is to conduct numerical modeling to determine the proper bolt type. Based on physical 

observations on the recovered cores, no major discontinuities other than bedding planes were discovered at the case 

study mine. Although core breakage was noticed at several 

locations, the only discontinuities considered for the 

modeling were those between distinct lithologic units or 

those that were not related to core handling. For instance, 

one hole that has been used for modeling has rider coal in 

the immediate roof and shale above it. From core 

examination, it was found that there were seven different 

weakness planes within the first 7 ft of the roof that need 

to be considered in the modeling. All these seven contacts 

were explicitly included in the models for this type of roof 

lithology. 

 In all of the models, the rock was treated as an elastic 

material. The necessary data required to define this constitutive behavior were available from laboratory testing on 

cores. The contacts, however, were simulated using 

inelastic Mohr-Coulomb behavior. Even though the bedding planes were not tested for their properties, the 

assumed numbers will not significantly alter the modeling 

outcome, as the objective here is to compare the relative 

performance of different types of bolts under otherwise 

identical conditions. Any errors in the input data will most 

likely affect all of the models to the same extent. 

 Since the case study mine is a new venture, field pull 

tests on roof bolts were not conducted to determine the 

required inputs for the bolt simulation. However, actual 

pull test data from a different mine on #6, grade 60 fully 

grouted rebar were used to estimate the shear stiffness of 

the bolt system. 

Step 4: Build and solve the models with different bolt 

types. 

 Two different bolt types were considered for the case 

study mine: fully grouted and torque-tension type. In the 

case of the torque-tension bolt, only the top 4 ft was 

grouted. The models for each bolt type were run in two 

stages. In the first stage, the model was solved to create the 

premining stresses; in the second, the mine entries were 

created with bolts installed on the pattern suggested by 

ARBS. In each model, the bedding planes identified in 

step 3 were explicitly included. 

0

5

10

15

20

25

30

35

40

45

50

55

60

1 2 3 4 5 6 7 8 9 10 11

Hole Number

CMRR

Figure 6.—CMRR variation at the case study mine. 
55

 In previous modeling studies reported in the literature, 

roof bolts were typically simulated in one of two ways: 

either the bolts were built “physically,” or mathematically 

equivalent bolts were used. In the first approach, the roof 

bolt, resin, and drill hole are explicitly made in a numerical 

model. Inputs are then provided to define the constitutive 

behavior of each element separately along with the 

interface properties for bolt-resin and resin-rock contacts. 

Ideally, this seems to be the most accurate way to model 

roof bolts. Difficulties, however, arise for several reasons. 

First, the roof bolts are dimensionally two or three orders 

smaller than the mine entry and, therefore, achieving a 

proper mesh density becomes extremely difficult even with 

the best of the available computing resources. Second, the 

constitutive behavior of the bolt-resin and resin-rock 

interfaces has never been tested in situ to provide all of the 

inputs needed for modeling. Third, problems in numerical 

solutions will easily occur because of the several 

awkwardly intersecting contact planes in this approach. 

Finally, it is extremely time-consuming to build and solve 

a model that has all of the complications of “physically” 

including roof bolts. To make the problem solvable in a 

reasonable amount of time and within the limits of 

available computing resources, several assumptions and 

simplifications must be made. As a result, even though the 

explicit inclusion of bolts may provide a sense of precision, the benefits of such a tedious approach may be more 

illusory than real. In any case, explicit modeling of roof 

bolts may perhaps be justified for research work, but is 

certainly not a feasible option for routine support 

design. 

 In the second approach, mathematically equivalent 

roof bolt elements are created whose constitutive behavior 

will provide an accurate representation of the roof bolt 

action. Since many of the complications involved with the 

first approach are eliminated, it is much easier to model a 

large number of bolts in a single model with little effort. 

Also, the assumptions involved in formulating the bolt 

elements are probably no worse than those required to 

make physically built roof bolts “work” numerically. For 

the obvious advantages, in this study the second approach 

has been chosen for bolt modeling using a finite 

difference-based code, FLAC3D4

 [Itasca Consulting 

Group 2005]. This software is by far the most commonly 

used modeling tool in rock engineering. FLAC solves the 

dynamic equation of motion in time-domain to provide 

pseudostatic solutions. The explicit solution scheme 

adopted in FLAC3D makes it an ideal tool for simulating 

nonlinear behaviors [Itasca Consulting Group 2005]. 

Step 5: Examine the model results and choose the final 

bolt type. 

 Roof bolts are point-acting-type structures whose 

radius of influence is rather limited. As a result, there may 

not be a significant difference in the stress state of the 

 

4

Fast Lagrangian Analysis of Continua in 3 Dimensions. 

a) b)

c)

 Figure 7.—Deformed geometry with (a) no bolts, 

(b) fully grouted bolts, and (c) torque-tension bolts 

(for comparison, deformations are exaggerated by 

20 times in all three graphics). 
56

immediate roof to make a substantial difference to the 

extent of yield zones or to the magnitude of local safety 

factors. For this reason, in this study, the criterion used for 

bolt performance comparison was vertical displacement. 

If a bolt keeps individual layers in the immediate roof 

tightly bundled together, then the bolt will most likely perform well. Based on this criterion and the layered nature of 

the immediate roof, torque-tension rebar was found to be 

the best bolt type, as shown by the modeling results in 

Figures 7–8. 

 Without roof bolts, Figure 7 shows that the first three 

layers will separate from the layers above, and the resulting deformations are so large that these layers will most 

likely fail. Even with fully grouted roof bolts, the first 

layer’s movement is considerable, and thus some skin 

failures cannot be ruled out. The torque-tension bolts, 

however, substantially reduce the separations and slips 

across the first three bedding planes. This can be seen 

more clearly from a plot of vertical displacement at the 

middle of the entry shown in Figure 8. 

 In a different part of the reserve at the case study mine, 

the immediate roof has shale and sandstone within the 

bolting horizon. By a similar numerical modeling exercise, 

the models found that fully grouted roof bolts could 

stabilize the roof as effectively as torque-tension bolts. For 

the obvious cost benefits, fully grouted bolts were recommended for this area. 

CONCLUSIONS 

 Empiricism and coal mine strata control are inseparable. The contributions of empirical rock mass classification systems and their derivatives for successful roof 

support designs are considerable. Notwithstanding the 

ground-breaking advancements in numerical modeling, 

empirical tools will continue to play a critical role in strata 

control designs. This fact has been demonstrated by the 

success of the CMRR and ARBS, as shown in this paper. 

 Much can be achieved by synthesizing the benefits of 

empirical and analytical tools, which indeed is the crux of 

Peabody’s ISDM. As demonstrated by the case study in 

this paper, this integrated approach can provide very 

detailed information on the performance of different types 

of roof bolts under the same roof conditions. Also, the 

modeling helps to explain the failure mechanics, and this 

knowledge will substantially aid in devising proper support 

measures. 

REFERENCES 

 Barton N, Lien R, Lunde J [1974]. Engineering classification of rock masses for the design of tunnel support. 

Rock Mech 6(4):189–236. 

 Bieniawski ZT [1973]. Engineering classification of 

jointed rock masses. Trans S Afr Inst Civ Eng 15:335–344. 

 Dolinar DR, Bhatt SK [2000]. Trends in roof bolt 

application. In: Mark C, Dolinar DR, Tuchman RJ, 

Barczak TM, Signer SP, Wopat PF, eds. Proceedings: New 

Technology for Coal Mine Roof Support. Pittsburgh, PA: 

U.S. Department of Health and Human Services, Public 

Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, 

DHHS (NIOSH) Publication No. 2000–151, IC 9453, pp. 

43–51. 

 Itasca Consulting Group [2005]. Fast Lagrangian 

analysis of continua in 3 dimensions, version 3.0. Minneapolis, MN: Itasca Consulting Group, Inc. 

 Mark C, Molinda GM [2005]. The coal mine roof 

rating (CMRR): a decade of experience. Int J Coal Geol 

64(1–2):85–103. 

 Mark C, Molinda GM, Barton TM [2002]. New 

developments with the coal mine roof rating. In: Peng SS, 

Mark C, Khair AW, Heasley KA, eds. Proceedings of the 

21st International Conference on Ground Control in Mining. Morgantown, WV: West Virginia University, pp. 294–

301. 

 Mark C, Molinda GM, Dolinar DR [2001]. Analysis of 

roof bolt systems. In: Peng SS, Mark C, Khair AW, eds. 

Proceedings of the 20th International Conference on 

Ground Control in Mining. Morgantown, WV: West Virginia University, pp. 218–225. 

 Molinda GM, Mark C [1994]. Coal mine roof rating 

(CMRR): a practical rock mass classification for coal 

mines. Pittsburgh, PA: U.S. Department of the Interior, 

Bureau of Mines, IC 9387. 

 Whittles DN, Reddish DJ, Lowndes IS [2007]. The 

development of a coal measure classification (CMC) and 

its use for prediction of geomechanical parameters. Int J 

Rock Mech Min Sci 44(4):496–513. 

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

0 0.5 1 1.5 2 2.5 3 3.5 4

Displacement, inch

Distance into roof, ft

No Bolt

Fully Grouted Bolt

Torque Tension Bolt

 Figure 8.—Vertical displacement at the middle of the 

entry with distance into roof. 
57

A ROCK MASS RATING SCHEME FOR CLASTIC SEDIMENTS 

BASED ON GEOPHYSICAL LOGS 

By Peter J. Hatherly, Ph.D.,1

 Terry P. Medhurst, Ph.D.,2 and Stuart A. MacGregor3

ABSTRACT 

 In addition to fractures and joints, compositional factors and bedding influence the strength of clastic sedimentary rocks. Rock mass rating schemes must therefore 

consider all of these factors. 

 In this paper, a rating scheme for clastic sediments 

based on geophysical measurements is described. Geophysical logging allows an approximation of rock composition to be obtained and an assessment of bedding 

frequency and laminations. Velocity measurements incorporate the effects of fracturing. 

 The rating requires scores for the intact rock, bedding/

cohesion, and defects. These are combined to yield a 

Geophysical Strata Rating (GSR). Determination of the 

GSR is objective and repeatable. GSR values are typically 

between 20 and 80 and can be related to the Coal Mine 

Roof Rating. 

INTRODUCTION 

 Analysis of geophysical borehole logs provides one of 

the best approaches to characterizing rocks within boreholes. The techniques have been developed mainly for use 

in petroleum exploration, but there are also established 

applications in coal mining, metalliferous mining, groundwater investigations, and civil engineering. For mining, the 

main application is to provide information on ore quality, 

geological correlations, and geotechnical properties. 

 This paper concerns the geotechnical applications in 

underground coal mining where the characterization of 

roof and floor strata is important for understanding caving 

behavior and roof support requirements. The results relate 

to Australian coal mining conditions where the coals are 

mainly of Permian age and depths of mining are usually 

less than 500 m. Roof and floor strata are mainly sandstones, siltstones, and claystones with occasional tuff beds 

and bands of siderite. Limestones are absent. Given the 

mainly clastic nature of these strata, the techniques of geophysical log analysis developed for the characterization of 

petroleum reservoirs are particularly useful. 

 On the basis of these techniques, Medhurst and 

Hatherly [2005] proposed the Geophysical Strata Rating 

(GSR). In this paper, we further develop the GSR and 

provide examples of its application. The GSR mainly relies 

upon sonic logging. In this regard, it can be viewed as a 

refinement of UCS/sonic relationships frequently employed in Australian coal mining. It also has similarities 

with the approach developed by Barton [2002, 2006] for 

determining Q-values from sonic, porosity, and depth 

information. The GSR delivers results on a linear scale 

similar to the Coal Mine Roof Rating (CMRR) of Molinda 

and Mark [1994]. It combines separate ratings for the 

intact rock mass and defects. Beyond consideration of 

velocity, porosity, and depth, it also considers clay content 

(shaliness). 

 Owing to the widespread interest in using sonic 

velocity in geotechnical investigations, this paper begins 

with a review of the geotechnical significance of sonic 

velocity. 

SONIC LOGGING 

 The sonic velocity obtained by sonic logging is a 

compressional seismic wave (P-wave) with velocity, Vp

given by 

 (1) 

where k is the bulk modulus (incompressibility), μ is the 

shear modulus, and ρ is the density. 

 In an isotropic and homogeneous rock body, seismic 

velocity responds to the elastic properties and density of 

the medium as might be measured in a rock mechanics 

laboratory. If the strength of the rock were related to its 

elastic properties, then the velocity would also be related 

to the strength. However, when inhomogeneities due to 

factors such as compositional variations and defects are 

present in the rock mass, as well as anisotropy in the form 

of bedding and other directional features, k, μ, and ρ are 

variable and the interpretation of Vp becomes more difficult. To understand the significance of a velocity measurement, it is necessary to understand the influence of the 

various causes of inhomogeneity and anisotropy. 

 In fresh igneous rocks where the porosity is low and 

the crystals have similar elastic properties, the velocity is 

largely controlled by fractures and joints. Barton [2006] 

makes frequent reference to the work of Sjøgren et al. 

[1979], who correlated RQD with measurements of Vp

 

 1

CRC professor of mining geophysics, University of Sydney, 

Sydney, New South Wales, Australia. 

 2

Principal geotechnical consultant, AMC Consultants Pty. Ltd., 

Brisbane, Queensland, Australia. 

 3

Senior geological engineer, SCT Operations Pty. Ltd., 

Wollongong, New South Wales, Australia.

ρ

− 4 / 3μ = k Vp
58 

from shallow seismic refraction surveys in Norwegian 

igneous and metamorphic rocks. As is also reported by 

Barton [2006], Deere et al. [1967] found a relationship 

between RQD and the square of the ratio of Vp measured in 

the field and in the laboratory on intact samples. However, 

when igneous rocks weather, compositional changes occur 

and pore spaces develop. Other factors will then influence 

the velocity. 

 In sedimentary rocks, particularly those that form 

petroleum reservoirs, there has been considerable attention 

given to understanding the relationship between Vp and 

composition and porosity. Pressure is also an important 

factor because of its influence on the porosity. While it is 

not possible to determine exact expressions for Vp, laboratory studies allow development of empirical relationships. 

For example, Han et al. [1986] report on a study of sandstone samples with fractional porosities, φ, ranging from 

0.02 to 0.3 and clay contents, VShale, ranging from 0.03 to 

0.5. Results are given for a number of confining pressures. 

For example, at 5-MPa confinement, Vp is given by 

 (2) 

with a correlation coefficient of 0.969. 

 While this equation shows that porosity has 3.5 times 

the effect on the velocity compared to clay, the clay content does have a measurable effect. This is illustrated in 

Figure 1, which shows how velocity varies over the 

compositional range investigated by Han et al. [1986]. As 

Han et al. point out, the velocity in a rock with very nearzero porosity and low clay content is significantly lower 

than the velocity of 6.05 km/s true for quartz aggregates. 

This implies that just small amounts of clay are able to 

soften the sandstone matrix and produce a reduction in 

velocity. 

 Han et al. [1986] also suggest that for clay to have an 

effect on the velocity, it has to be either structural (i.e., 

bonding grains) or laminar (forming discrete layers 

between grains). If the clay were simply suspended 

between pores, then negligible affects would be expected. 

Dvorkin and Brevik [1999] use the separate influence on 

Vp of clay in the form of cements and interstitial clays to 

infer the strength and permeability of reservoir-forming 

sandstones. Similar observations were made in another 

study by Eberhart-Phillips et al. [1989], who obtained 

empirical relationships between Vp and φ, VShale and effective pressure, pe (confining pressure minus the pore 

pressure). They observed systematic departures for some 

sandstones from normal trends, which they attributed to 

factors such as the shape and size of grains and pores, 

as well as the degree of compaction. 

 As an empirical relationship between Vp, composition 

and pe, Eberhart-Phillips et al. [1989] derived the equation: 

( ) pe p shale e V V p e

16.7 5.77 6.94 1.73 0.446 − = − φ − + − (3) 

 By calculating velocity, this equation can be used to 

provide confirmation of clay content and porosity determinations from natural gamma, neutron porosity, and 

density logs. It also allows velocity measurements to be 

checked against the results from these other logging data. 

ROCK CHARACTERIZATION FROM 

SONIC VELOCITY 

 From Equation 1 it follows that there are relationships 

between Vp and modulus. If density is known and 

measurements are also made of shear wave velocity, it is 

possible to solve for k and μ. However, the strains involved 

in the measurement of Vp are of the order of microstrains, 

whereas in rock testing the strains are of the order of 

millistrains. With these very different orders of strain, it is 

found that different values of the modulus are obtained. 

 Both Barton [2006] and Wang [2000] review and 

discuss these issues and present numerous results. The 

reason for the difference is attributed to the behavior of 

pore and crack boundaries. At low strains, these are stiff, 

but they deform elastically at higher strains and the rock 

appears softer. In materials such as steel and solid quartz, 

there is little difference between values. This is also the 

case at depth (pressures greater than 100 MPa), when 

pores and cracks are closed. Closer to the Earth’s surface, 

the so-called dynamic modulus obtained by seismic 

measurements in sedimentary rocks may be twice the 

laboratory values (static modulus). Wang [2000] also 

reports that Winkler [1979] found, for the same reasons, 

there is a strain dependence for Vp. 

 In the case of the UCS, there is no theoretical basis for 

relating it to Vp. However, because it is generally observed 

that stiffer rocks are stronger, empirical estimates of UCS 

Vp = − 08φ − 2.02VShale 5.26 7.

 Figure 1.—Ternary diagram showing changes in Vp in 

sandstone according to porosity, clay, and quartz content 

at 5-MPa confinement. Velocities are in km/s. Quartz content = 1 – VShale – φ. 
59 

can be made from Vp, provided fracturing is not strongly 

influencing Vp. Barton [2006] presents comparisons 

between Vp and UCS and reference is made to a Vp

3

relationship, one that also provides a reasonable first estimate of UCS in Australian coalfields. 

 As discussed by Medhurst and Hatherly [2005], many 

Australian coal mines use empirical relationships to estimate UCS from Vp. If a relationship is established for a 

specific situation where strength does vary with modulus 

and there is proper consideration of the effects of pressure 

and fracturing on velocity, this approach can be followed. 

Situations where Vp is not particularly sensitive to UCS 

include those involving poorly cemented cohesionless 

rocks and also shales where moisture conditions influence 

strength. 

 For purposes of rock mass characterization, Barton 

[2002, 2006] developed a graphical approach for determining a Q-value and modulus from Vp, porosity, and 

depth. As a basis, it uses the hard-rock relationship: 

 (4) 

which Barton derived from results of numerous investigations involving unweathered rocks such as granites, 

gneisses, volcanic ignimbrite, and competent sandstones, 

all at depths to about 25 m. To allow for the influence of 

depth and porosity and to extend the application to other 

rock types, a normalized value Qc is introduced whereby 

 (5) 

 The physical basis for this approach is evident from 

the preceding discussion on the relationship between Vp, 

RQD, pressure, modulus, and UCS. Variations in Vp due to 

compositional factors such as the clay content in clastic 

rocks are not explicitly included. However, Equation 5 

does make some allowance for compositional variation 

because of the relationship between Vp and UCS and 

because of the strength reduction factors involved in the 

determination of Q. 

 The GSR for clastic rocks that is described below 

similarly uses information on Vp, porosity, and depth to 

determine a rock mass rating. However, it is based on 

direct geomechanical considerations of rock strength and 

includes explicit consideration of the clay content. To 

determine GSR, geophysical logging data are analyzed. As 

a minimum, sonic, density, and natural gamma logs are 

required. Neutron porosity logs can also provide alternative estimates of shaliness and porosity, which will help 

improve the analysis. A basis for the geophysical log interpretation procedure is given by Medhurst and Hatherly 

[2005] and Hatherly et al. [2006]. 

GEOPHYSICAL STRATA RATING (GSR) 

 Like soil classification systems, sedimentary rocks are 

amenable to characterization via a description of the grain 

size and type, amount of pore space, and moisture content. 

Fortunately, geophysical logs provide a reliable and 

repeatable measure of such parameters. As discussed, 

sonic velocity is a key measure that reflects rock stiffness 

and to some extent rock strength and fracturing, provided 

changes in mineral composition and porosity can be 

detected via other log data. At its core, the GSR is based 

on providing ratings for the quality of the individual beds, 

their contacts, and frequency. 

Rock Score 

 The rock score attempts to provide a measure of the 

quality of the individual beds and has three components: 

strength score, porosity score, and moisture score. The 

strength score is calculated using sonic velocity and is used 

as the basic measure of rock competency. Adjustments are 

then applied to take into account the influence of highporosity, poorly consolidated materials and the influence 

of high moisture content. Using an empirical approach, the 

following relationships have been developed: 

 Strength score = 20 * Vp – 45 (6) 

where Vp is in km/s and is corrected for effective pressure 

via Equation 3. 

 Porosity score = –5 * X * Y (7) 

where X relates to the clay content, VShale, and Y relates to 

the porosity, φ. If VShale > 0.35, X = 0. If VShale is between 

0.25 and 0.35, X is linear between 1 and 0. When VShale < 

0.25, X = 1. If φ < 0.05, Y = 0. When φ is between 0.05 

and 0.2, Y is linear between 0 and 3. When φ > 0.2, Y = 3. 

 Essentially, the maximum adjustment of –15 occurs 

when VShale < 0.25 and φ > 0.2 and reduces to zero when 

VShale > 0.35 or φ < 0.05. 

 Moisture score = –5 * X * Y (8) 

where X relates to VShale and Y relates to φ. If VShale < 0.65, 

X = 0. If VShale is between 0.65 and 0.75, X is linear 

between 0 and 1. When VShale > 0.75, X = 1. If φ < 0.025, 

Y = 0. When φ is between 0.025 and 0.075, Y is linear 

between 0 and 2. When φ > 0.075, Y = 2. 

Vp ≈ 3.5 + log10 Q

UCS Q Qc 100 =
60 

 Essentially, the maximum adjustment of –10 occurs 

when VShale > 0.75 and φ > 0.075 and reduces to zero when 

VShale < 0.65 or φ < 0.025. 

 The final estimate of rock score is therefore given by 

 Rock score = Strength score + Porosity score 

 + Moisture score (9)

Bedding Contact/Cohesion Score 

 In the Australian coalfields, stronger rocks tend to 

have stronger bedding. This allows a bedding/cohesion 

score to be given on the basis of sonic velocity. Highquartz sandstones are assumed to have strongly bound 

and/or cemented bedding surfaces, whereas mudstones are 

assumed to have smooth, planar, and weaker bedding 

surfaces. 

 Cohesion score = 10 + 5 * X (10) 

where X relates to Vp, again corrected for effective 

pressure. If Vp < 2.75, X = 0. When Vp is between 2.75 and 

3.25, X is linear between 0 and 2. When Vp > 3.25, X = 2. 

 In the case of hard sandstones, i.e., quartz contents 

greater than 0.57 and Vp > 3.25, an additional component 

applies: 

 Cohesion score = Cohesion score + 5 * X * Y (11) 

where X again relates to Vp and Y relates to the quartz 

content. If Vp is between 3.25 and 3.5, X is linear between 

0 and 1. If Vp > 3.5, X = 1. If the quartz content is between 

0.57 and 0.67, Y is linear between 0 and 1. When quartz 

> 0.67, Y = 1. 

Initial GSR (GSRi) 

 Initial GSR (GSRi) = Rock score + Cohesion score (12) 

 The initial GSR (GSRi) provides a measure of 

variation in the rock quality of individual beds. In doing 

so, it not only provides a bed rating, but by the contrast 

between beds, it also reflects the variation between beds. It 

is thus possible to obtain measures of bed frequency in 

laminated strata or to determine the thickness of so-called 

geotechnical strata units in thicker strata sequences where 

lithological boundaries are less significant. 

Defect Score 

 In keeping with other rock mass rating schemes, the 

GSR also needs to reflect the state of the defects introduced by fracturing and bedding. When cores and direct 

rock exposures are available, ratings are provided by 

manual logging—an intensive and potentially subjective 

process. With geophysical logs, acoustic scanner data also 

allow direct mapping of defects provided they are evident 

in the borehole wall. However, this is also an intensive and 

potentially subjective process. 

 For the GSR, defect information is extracted from the 

results of the analysis of the geophysical logs. The variability in these is taken to be the indicator of defects. 

Specifically, rapid changes in VShale are likely to indicate 

changes in lithology, while changes in GSRi are likely to 

indicate that defects in the form of fractures and changes in 

lithology are present. The variability is thus determined on 

the basis of the rate of change of GSRi and VShale. 

 The bedding score is based on the variability in VShale

and is designed to capture the transitions between sandstones and siltstones/mudstones and also the variability 

within fine-grained units. The mean value and standard 

deviation of the variability is established over the interval 

of interest, and a bedding score between 0 and 10 is 

assigned. A score of 10 indicates that there is no change 

occurring in VShale. A score of 0 indicates that the 

maximum changes in VShale are occurring at the point in 

question. 

 The fracture score is based on the variability in the 

GSRi. The GSRi provides an overall estimate of the state 

of the rock mass from all available geophysical logging 

data and therefore captures any influences on the logs of 

the bedding as well as fractures. Following the work of 

Priest [1993] on joint frequency, an exponential relationship is used to describe the variability and from this, the 

likelihood that any particular value of variability is due to 

a defect is predicted. At each point, a linear score between 

0 and 10 is applied on the basis of this probability. 

Final GSR 

 GSR = GSRi + Bedding score + Fracture score (13) 

EXAMPLES 

Implementation 

 Most geophysical logging data are recorded in the 

standard LAS (Log ASCII Standard) format.4

 Files begin 

with a header containing log and borehole information, and 

point-by-point log data are then supplied in column format. 

Being an ASCII format, LAS files can be read using 

standard text editors and imported into databases and 

spreadsheets such as Microsoft Excel. Visual Basic macros 

within Excel have been written to interpret the geophysical 

 

 4An exception arises with image and other data intensive logs, 

such as acoustic scanners and full waveform sonic logs. For 

these, binary data formats are used, but unfortunately standard 

file formats have not been accepted by the logging industry. 

Proprietary formats prevail.
61 

logs and to calculate the GSR. All data contributing to the 

GSR are tabulated and can be examined in their own right. 

Southern Coalfield, New South Wales, Australia 

 Figure 2 shows an example of a result from the Southern Coalfield, 60 km south of Sydney. The geophysical 

logs were initially obtained at 0.01-m spacings up the 

borehole. To reduce statistical uncertainty, especially in 

the natural gamma log, the logs were smoothed and 

resampled at 0.05-m spacings. Data are shown over a 4-m 

interval near the top of the working seam. 

 At the base of Figure 2, the interpreted clay content is 

shown together with the sonic data. Core photos are shown 

immediately above. There are no fractures evident, and the 

variations in clay content and velocity can be seen to be 

due to the changing lithology. In the gray silt bands, 

velocities are lower and clay content increases. Some of 

the bands have distinct margins (e.g., the siltstone band 

between 560.3 and 560.65 m).5

 In other sections, there are 

gradational changes in properties. For example, there is an 

increase in clay content and decrease in velocity between 

558.8 m and 559.8 m. Here the strata are coarsening 

upward. At 558.35 m there is a band of high velocity, 

which is due to siderite. Siderite bands show up in the geophysical logs as thin zones of abnormally high density and 

velocity. There is also a minor siderite band interpreted to 

be present at 561.3 m. 

 

 5Geophysical logs necessarily sample the rock mass over a 

finite interval of about 10–20 cm. For this reason, none of the 

rock boundaries appear abrupt. 

 At the top of Figure 2 are shown the GSRi and GSR. 

As expected, the GSRi shows the trends evident in the log 

data and core. Where there are distinct bands, they are 

evident as discrete layers. The sandstones have a GSRi of 

about 50. For the siderite it is 68; in the siltstones, it is 

about 42. Similarly, the gradational changes in lithology 

are represented by gradational changes in GSRi. There is 

also a region of low GSRi at 561.8 m, which is due to low 

velocity affecting the strength and cohesion score, and 

high shaliness and porosity, which influence the moisture 

score. 

 When the defect score is added to the GSRi to obtain 

the GSR, the gradational units remain gradational, the 

GSR at the boundaries of the discrete beds is enhanced 

(GSR goes relatively lower), and the GSR at the center of 

the discrete beds is enhanced. Lithological and bedding 

effects can thus be seen to be incorporated into the rating. 

Newcastle Coalfield, New South Wales, Australia 

 The second example (Figure 3) comes from the Newcastle Coalfield 100 km north of Sydney. Here, results for 

the 2-m-thick immediate roof of the working seam are 

shown. From the core, five lithological units were identified and assigned separate CMRR ratings based on their 

discontinuities and intact strength. The lithologies, 

discontinuity spacing rating, UCS, and overall CMRR 

ratings for each unit are shown. The mudstones have lower 

UCS values than the sandstones. There is a low discontinuity spacing rating in the muddy sandstone in the 

immediate roof. 

 Figure 3 also shows the GSRi, GSR, and various 

defect scores determined from the geophysical logs. From 

the GSRi, it can be seen that the mudstones tend to have 

lower values than the sandstones. The fracture score established from the variability of the GSRi tends to be low at 

the bed boundaries and also in the immediate muddy sandstone with the low discontinuity spacing rating. For the 

bedding score, lows occur in the vicinity of the bed 

boundaries. When the bedding and fracture scores are 

added to the GSRi to produce the GSR, it can be seen that 

the relativity between the various beds is maintained and 

there are decreases in GSR at the bed boundaries and in the 

fractured muddy sandstone. While the absolute values of 

the GSR differ from the CMRR unit ratings in the 

sandstones, there is quite reasonable overall correlation 

between the CMRR unit rating and the GSR. 

 Figure 2.—Top: GSR (black) and GSRi (gray). Center: 

Core photograph showing a sequence of sandstones 

(white) and siltstones (gray). Bottom: Sonic velocity 

(gray) and interpreted clay content (black). 

30

40

50

60

70

80

90

558 558.5 559 559.5 560 560.5 561 561.5 562

Geophysical Strata Rating 0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

558 558.5 559 559.5 560 560.5 561 561.5 562

Depth (m)

Clay content (fraction)

2.5

3.0

3.5

4.0

4.5

5.0

Velocity (km/s)
62 

DISCUSSION AND CONCLUSIONS 

 The GSR is designed to be a rock mass rating system 

based on geophysical logging data for clastic strata typical 

of coal mining regions. It has some similarities to the 

method proposed by Barton [2002, 2006] for determining 

Q-values from geophysical data, but it allows for the variations in seismic velocity that can occur as a result of 

changes in lithology from clay-rich rocks to sandstones. 

Such changes in lithology also change the geotechnical 

properties of the strata, and the GSR is designed to 

accommodate these. 

 The elements of the geophysical log interpretation 

behind the application of the GSR have been confirmed 

through the analysis of many geophysical logs from the 

coalfields of Australia, and the main structure of the GSRi 

is in place. Fine-tuning of the various scores, particularly 

the two defect scores, is now underway through comparisons with independent geotechnical ratings. The 

CMRR is the obvious scheme against which these comparisons can be made. Once these are completed, a more 

definitive GSR is likely. 

 Another area where work is under way concerns the 

effect of clay on rock properties. Earlier reference 

[Dvorkin and Brevik 1999; Eberhart-Phillips et al. 1989] 

was made to the effects of interstitial clay and clay in the 

form of a cement on seismic velocity. Following the work 

of Katahara [1995], it is possible to identify from natural 

gamma and porosity logs in shaly sandstones the presence 

of interstitial clay, clay cements, and laminar clay. On the 

basis of these considerations, refinements to the moisture 

and porosity scores are likely. 

 As a rating scheme, the GSR assigns a single value to 

every depth point. However, the component scores and 

interpreted geophysical data leading to the GSR have 

direct geotechnical and geological significance. Examination of these provides insights into the geotechnical 

properties of the strata and could also be used as input into 

numerical modeling investigations. 

 Geophysical log analysis and the GSR are not 

expected to totally replace manual geotechnical logging. 

As with any form of remote sensing, there will always be 

the need to provide ground truth. Anomalous regions 

identified by the GSR analysis should also be independently investigated to verify geotechnical conditions. 

 The benefits of the GSR should also be obvious. It is 

objective, repeatable, inexpensive to conduct, and representative of the state of the rocks as they are in the ground. 

Data from holes drilled for exploration purposes are also 

potentially available for analysis, thus supplementing the 

geotechnical database. By virtue of the fact that the GSR 

delivers a continuous assessment, it also provides insights 

into characteristics of rock units that may not be evident 

from manual logging, where properties are assigned across 

discrete geotechnical units. Bed and defect boundaries are 

also highlighted through the process. 

ACKNOWLEDGMENTS 

 Development of the GSR has been funded by the 

Australian Coal Association Research Program, CRC Mining, and CSIRO Exploration and Mining. We thank 

Centennial Coal, BHP Billiton Illawarra Coal, and David 

Hill of Strata Engineering for the data used in the 

examples. Discussions with Christopher Mark, Ph.D., and 

Gregory M. Molinda of the NIOSH Pittsburgh Research 

Laboratory and Winton J. Gale, Ph.D., of SCT Operations 

helped to provide clarity. 

REFERENCES 

 Barton N [2002]. Some new Q-value correlations to 

assist in site characterization and tunnel design. Int J Rock 

Mech Min Sci 39(2):185–216. 

 Barton N [2006]. Rock quality, seismic velocity, 

attenuation and anisotropy. London and Netherlands: 

Taylor & Francis. 

 Deere DU, Hendron AJ, Patton FD, Cording EJ 

[1967]. Design of surface and near-surface construction. 

In: Fairhurst C, ed. Rock failure and breakage of rock. 

New York: Society of Mining Engineers of AIME. 

 Dvorkin J, Brevik I [1999]. Diagnosing high-porosity 

sandstones: strength and permeability from porosity and 

velocity. Geophysics 64(3):795–799. 

 Eberhart-Phillips D, Han D–H, Zoback MD [1989]. 

Empirical relationships among seismic velocity, effective 

pressure, porosity and clay content in sandstone. Geophysics 54:82–89. 

 Han D–H, Nur A, Morgan D [1986]. Effects of porosity and clay content on wave velocities in sandstones. Geophysics 51:2093–2107. 

 Hatherly P, Thomson S, Armstrong M [2006]. Geophysical log analysis for the southern Sydney basin. In: 

 Figure 3.—Comparison of GSR and CMRR. The sample 

interval in the geophysical logs is 0.05 m. 

0

10

20

30

40

50

60

70

80

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

Height into Roof (m)

Rating

0

20

40

60

80

100

120

UCS (MPa)

CMRR unit rating Discontinuity spacing rating Defect score

Fracture score Bedding score GSR 

 GSRI UCS

muddy 

sandstone

mudstone

fine grained 

sandstone/siltston

mudstone

sandstone
63 

Hutton A, Griffin J, eds. Proceedings of the 36th Sydney 

Basin Symposium (Wollongong, New South Wales, 

November 27–29, 2006), pp. 59–70. 

 Katahara KW [1995]. Gamma ray log response in 

shaly sands. Log Analyst 36(4). 

 Medhurst T, Hatherly P [2005]. Geotechnical strata 

characterization using geophysical borehole logs. In: 

Peng SS, Mark C, Tadolini SC, Finfinger GL, Khair AW, 

Heasley KA, eds. Proceedings of the 24th International 

Conference on Ground Control in Mining. Morgantown, 

WV: West Virginia University, pp. 179–186. 

 Molinda GM, Mark C [1994]. Coal mine roof rating 

(CMRR): a practical rock mass classification for coal 

mines. Pittsburgh, PA: U.S. Department of the Interior, 

Bureau of Mines, IC 9387. 

 Priest SD [1993]. Discontinuity analysis for rock engineering. London: Chapman & Hall. 

 Sjøgren B, Øfsthus A, Sandberg J [1979]. Seismic 

classification of rock mass qualities. Geophys Prospecting 

48:815–834. 

 Wang Z [2000]. Dynamic versus static elastic properties of reservoir rocks. In: Wang Z, Nur A, eds. Seismic 

and acoustic velocities in reservoir rocks. Vol. 3: Recent 

developments. Tulsa, OK: Society of Exploration Geophysicists, pp. 531–539. 

 Winkler K [1979]. Effects of pore fluid and frictional 

sliding on seismic attenuation [Dissertation]. Stanford, 

CA: Stanford University. 

65

PRACTICAL EXPERIENCES WITH APPLICATION OF THE 

COAL MINE ROOF RATING (CMRR) IN AUSTRALIAN COAL MINES 

By David Hill1

ABSTRACT 

 The Australian underground coal mining industry has 

made extensive use of the Coal Mine Roof Rating 

(CMRR) classification system for a diverse range of purposes in recent years. These include mining method selection, and coal pillar and roof support design. This paper 

outlines a series of case histories, from large-scale feasibility studies to local support design investigations, that 

collectively illustrate the broad applicability, advantages, 

and usefulness of the methodology, as well as some of the 

current limitations. 

 The key role of the CMRR in an overall hazard definition methodology is demonstrated for a major Australian 

project, and some ideas with regard to the future application of the CMRR, in the context of geotechnical risk 

management within a progressive, highly productive 

extractive industry, are put forward. 

BACKGROUND 

 The Coal Mine Roof Rating (CMRR) is a measure of 

roof quality or structural competency for bedded roof types 

typical of underground coal mines. The CMRR was 

developed by the former U.S. Bureau of Mines (of which 

the health and safety research component was transferred 

to NIOSH) and has been widely applied in Australia since 

the mid-1990s. It was derived from the South African 

Council for Scientific and Industrial Research’s Rock 

Mass Rating (RMR) system, which has been used in the 

mining and tunneling industries for over 30 years 

[Bieniawski 1974]. 

 The CMRR was initially based on field observations 

at surface highwalls and portals, as well as underground air 

crossings and roof falls [Molinda and Mark 1994]. Later, 

a methodology was developed for assessing the CMRR 

from drill core, to assist where underground exposures 

were limited or unavailable [Mark and Molinda 1996]. The 

system was revised in 2003 to incorporate experiences 

gained since 1994 [Mark and Molinda 2003]. 

 The CMRR considers the following factors: 

• Thickness of the individual roof beds 

• Shear strength properties of the bedding/planes of 

weakness 

• Compressive strength of the rock material 

• Moisture sensitivity of the rock material 

• Number of different units (i.e., the degree of 

homogeneity of the roof) 

• Presence of groundwater 

• Presence of a particularly strong bed or weaker 

overlying beds 

 Essentially, the CMRR is calculated by deriving unit 

ratings for individual geotechnical units and then determining a weighted average for the bolted horizon. The 

CMRR is therefore specific to roof bolt length and can 

change, for example, if the bolt length is increased to 

anchor into an overlying relatively competent horizon or if 

a particularly incompetent unit in the immediate roof is cut 

down during drivage. Unit ratings can range from 0 to 100; 

the typical range encountered in Australia is 15–70. 

 Molinda and Mark [1994] suggest the following categorization of roof competency: 

 CMRR < 45 Weak roof 

 CMRR = 45–65 Moderate roof 

 CMRR > 65 Strong roof 

At the time of the original U.S. research, 75% of the data 

fell into the “weak” or “moderate” categories, with an 

average CMRR of around 53. By contrast, Australian coal 

industry research in the late 1990s indicated a lower average CMRR for longwall mines of 50, with 86% of the data 

falling into the “weak” or “moderate” categories 

[Colwell 1998]. The issue of typically lower roof competencies in Australia will be explored later in this paper. 

POSITIVE ASPECTS OF THE CMRR 

 With particular regard to the underground coal mining 

geotechnical environment, the CMRR system is considered 

to incorporate a number of positive technical features and 

to offer the rock mechanics engineer several practical 

advantages over alternative approaches. 

 The major positive technical aspect is that the CMRR 

system focuses on characterizing the structural competency of a bedded, sedimentary rock mass and effectively 

its propensity for deformation due to buckling under the 

action of horizontal stress, noting that in Australian 

collieries, this is the main cause of roof deterioration (see 

Figure 1). The propensity for roof buckling is a function of 

the excavation span, bed thickness, and the material stiffness properties. A roof that remains intact, without any 

 

 1Principal, Strata Engineering (Australia) Pty. Ltd., New South 

Wales, Australia. 
66 

appreciable delamination under the action of horizontal 

stress, can be referred to as “static.” 

 The CMRR drill core methodology typically ascribes 

approximately two-thirds of the overall rating to the 

discontinuity rating, which is related directly to bed thickness and the potential for delamination (i.e., reducing bed 

thickness). This discontinuity rating is defined as the lower 

of two parameters: the discontinuity spacing rating 

(defined from RQD and/or the fracture spacing) and the 

diametral point load test (PLT) rating. The diametral point 

load testing aids in identifying a material that is prone to 

delamination (e.g., fissile), which may be otherwise 

unbroken in the core tray. The relatively humble diametral 

PLT on vertically orientated core is highly relevant to 

assessing the potential for roof buckling due to horizontal 

stress. 

 The practical advantages of the CMRR relate very 

largely to its widespread application and the extensive 

databases that link the parameter to a range of mining 

situations (i.e., the CMRR is used as a primary input in a 

number of coal pillar and roof support design scenarios). 

Over the last decade, the CMRR has effectively become a 

common universal language for engineering geologists and 

geotechnical engineers operating in the U.S. and Australian coal industries. This has come about by a process of 

technology transfer, which has been particularly aided by 

the emphasis on the part of NIOSH on publishing associated research outcomes, including the underpinning databases. This availability and transparency of data has 

enabled other practitioners to interrogate the empirical 

findings and rapidly develop experience and confidence in 

the associated applications. As with all empirical methodologies, understanding the limitations and nature of the 

underpinning database is vital. Extrapolating technical 

findings, such as regression relationships, beyond the 

limits of a database can be highly problematic, requiring 

both caution and wisdom. 

 In practice, engineers have been able to take published 

research outcomes, derive their own local data, and 

interrogate that data in the context of the published work. 

This aids in understanding the local situation, including the 

extent to which local circumstances may vary from those 

previously encountered elsewhere, with associated caveats 

on the confidence that can be placed in the analysis. 

 The position of the CMRR within the coal industry 

has become akin to that of Microsoft Windows within the 

software industry—there may be a better commonly 

available and applicable operating system, but the CMRR 

has become entrenched. Furthermore, as the use of the 

CMRR spreads, the barriers to entry of alternative methodologies increase at a disproportionate rate. In the medium 

term (the next 10 years), it considered highly unlikely that 

the CMRR will be displaced by any new innovation. 

A more likely outcome is that current technical initiatives 

will develop “calibrations” with the CMRR, such that the 

latter remains the lingua franca. 

CURRENT ISSUES WITH USING THE CMRR 

 A number of issues associated with using the CMRR 

warrant mention, as they can influence the technical result 

and associated design outcomes. 

Methodology Aspects 

 Firstly and probably most significantly, it should be 

noted that the three published and accepted methodologies 

(i.e., the original exposure observation method, the initial 

drill core method, and the revised drill core procedures) 

can yield very different outcomes in specific circumstances. An extreme example is the massive conglomerate 

roof that is typical of the Great Northern Seam in the Lake 

Macquarie (Newcastle Coalfield) area of New South 

Wales. The original observation method would be guided 

by the general lack of discontinuities within the unit and 

would produce a rating of around 90. By contrast, both the 

• Tensile/shear failure occurs along bedding planes 

• Roof measures subdivide into thinner discrete units 

• Increased displacements 

0

10

20

30

40

50

0 500 1000 1500 2000

Time Since Extensometer Installation at Face (hours)

Roof Displacement (mm)

Controlled Buckling

Uncontrolled Buckling

Horizontal Stress 

 Figure 1.—Schematic of roof buckling under the action of 

horizontal stress and typical roof displacement data. 
67 

original and revised drill core procedures would recognize 

the moderate strength of the material (commonly ≤60 MPa 

and controlled by the nature of the cementation of the 

matrix between the pebbles), resulting in a typical unit rating of 60–65. In practice, the Teralba Conglomerate will 

typically span ≥50 m practically indefinitely, unsupported 

and with localized skin failures only. 

 In this case, the variance in the CMRR has no appreciable operational impact, as in the prevailing geotechnical 

environment (and in an Australian regulatory framework), 

even a CMRR of 60 will result in effectively minimum 

design outcomes (i.e., in this case, it does not particularly 

matter if the roof is better than suggested by a CMRR of 

60). However, it does have negative implications in that it 

can reduce confidence in the reliability of the technique 

and hinder effective communication (i.e., it is unhelpful to 

categorize a unit as “moderate” if, for all practical purposes, it behaves as “strong”). Furthermore, the variance 

would be operationally significant if the CMRR were to be 

used as a guide to cavability, in which case vastly different 

expectations would tend to be associated with a CMRR of 

60 as opposed to 90. 

 Also, the old and current drill core methods tend to 

produce different outcomes. The current method is considered an overall improvement in that it more systematically accounts for the influence of bedding and jointing, 

such that structurally affected areas are less likely to be 

overrated (essentially, the original method defaulted to the 

diametral PLT strength rating and ignored the discontinuity rating). However, in the current system, the maximum 

discontinuity spacing rating has been downgraded significantly from 70 to 48 (22 points). This tends to reduce the 

significance in the rating system of thickly bedded/massive 

sandstones and conglomerates, such that more conservative results are obtained. 

 Obviously, such a major change in a rating system 

requires careful consideration, as these systems are only 

useful in the context of their derived databases. Adjusting 

the input to these databases necessarily alters the outcomes 

in terms of the relationships between parameters and the 

derived equations. The impact of the changes is reduced in 

this case by the fact that the underpinning NIOSH CMRR 

databases are derived very largely from underground 

observations as opposed to drill core. 

 In fact, the use of the CMRR in the United States is 

understood to be based largely on underground observations, whereas in Australia the drill core method is most 

commonly applied. This change in emphasis also needs to 

be understood, as it materially impacts the way in which 

the CMRR is applied. As an example, a number of major 

Australian coal projects have used the CMRR to investigate spatial variations in roof competency across resource 

areas in recent years, which is only viable given the availability of adequate exploration borehole data. 

 Since the revision of the drill core method in 2003, 

Strata Engineering has on several occasions cross-checked 

the results obtained using the various published procedures. An example is illustrated in Figure 2, which summarizes the outcomes of a CMRR survey based on 30 drill 

cores across the resource area for a major longwall project 

in New South Wales. The following comments are made 

with regard to the results: 

1. On average, the CMRR values obtained using the 

revised procedure were reduced by 10% compared to 

those obtained using the old drill core method (i.e., 

a slightly more conservative result was generally 

obtained). 

2. 50% of the data points varied within only ±10%. 

3. The percentage variation trend line crossed zero at a 

CMRR value of 45, which, as noted, marks the category transition from “weak” to “moderate” roof. 

Practically, the revised procedure tended to have 

limited overall effect for CMRR values of <55. 

4. A variation of >20% was only noted in two circumstances. Firstly, in fault-affected areas, the revised 

procedure was more sensitive to jointing, with 

reduced CMRR values (note the three outlying data 

points in Figure 2). This was considered an 

improved, more realistic outcome. Secondly, the 

impact of reducing the maximum discontinuity rating 

from 70 to 48 was most pronounced in cases where 

the original methodology would generate particularly 

high CMRR values (>65). 

 Other surveys and comparisons have produced similar 

results, although it is common to find that there is still 

some reduction in the unit ratings between the old and 

current methodologies, even in the CMRR<40 range. In 

practice, experience suggests that it is virtually impossible 

to obtain a CMRR of >70 with the revised drill core 

procedure. 

 Figure 2.—Example of the effect of the revision to CMRR 

drill core procedures. 

y = -1.0154x + 45.425

R2

 = 0.5658 -40

-30

-20

-10

0

10

20

30 40 50 60 70 80

CMRR Value (Derived by Original Drill Core Method)

Percentage variation between original and revised

methods (%)

Structure-Affected Areas
68 

 It is important to note that that the great bulk of the 

Australian coal mining industry is currently operating at 

CMRRs of 35–55. This is the area of greatest significance 

for mine design and operational practice. It is also the zone 

of closest agreement between the various CMRR procedures, such that the impact of any discrepancies is reduced. 

Also, Australian experience suggests that roof behavior 

tends to become generally benign and insensitive to 

CMRR fluctuations at values of ≥55 (i.e., these values tend 

to be associated with static roof behavior, which is 

essentially self-supporting and the most stable roof condition attainable). Therefore, the disparities between the 

CMRR methodologies in stronger roof types tend to have 

minimal practical consequence. 

 Overall, the variances are currently tolerated, given 

that the modified procedures tend to be more conservative 

(in the case of areas of geological structure, appropriately 

so). However, in a different geotechnical environment or 

industry, the discrepancies could potentially be of more 

concern. 

Coal Roof 

 Approximately 50% of the Australian longwall mining 

industry operates under a roof wholly or at least partly 

composed of coal. Australian coals tend to be weak, 

bedded, and cleated (jointed), resulting in low CMRR 

values (typically 30–40). Nevertheless, in the absence of 

persistent, weak partings (commonly associated with thin 

mudstone or tuff bands), these coal roof units tend to 

perform relatively well, for example, under tailgate loading 

conditions. Historically, this has tended to be attributed 

largely to the low modulus of the material attracting 

reduced levels of horizontal stress (i.e., a specific gravity 

of 1.3–1.5, versus typically 2.5 for adjacent strata). 

 As a consequence, it has become common to apply 

adjustment factors to coal ratings. Although there is no 

generally agreed adjustment process, these adjustment factors typically range up to an additional 20% of the raw 

rating, depending on the extent to which the unit is clean 

(i.e., the prevalence of thin “dirt” bands), as well as the 

practical experiences of mining under the given roof type. 

Human Error 

 Although the CMRR is considered a relatively 

straightforward and uncomplicated system of rock mass 

classification, the potential for human error remains. 

A common error is the failure to distinguish between geological and geotechnical units. This is particularly true for 

gradational roof types (e.g., dark gray mudstone grading 

upward into gray siltstone, or bands of fine alternating 

with medium-grained sandstone). It is common for a roof 

material that visually is reasonably uniform to be logged 

by a geologist as a single lithological unit, whereas in 

practice the structural competency of the unit can vary 

markedly. This is especially true over short distances 

directly relevant to ground behavior (i.e., the first 2 m of 

roof). Unless the individual conducting the geotechnical 

logging is aware of the need to gather sufficient detail to 

define the homogeneity of a particular unit, valuable information can be lost. 

 When using the drill core method in the absence of 

visibly distinct roof units, PLTs (diametral plus axial) at a 

maximum of a 0.5-m spacing in the bolted interval will 

normally generate sufficient data to enable a reasonable 

analysis. Evaluation of a combined RQD, fracture spacing, 

and PLT data set then often facilitates subdivision of 

preliminary roof units, producing a more meaningful overall CMRR outcome. 

Horizontal Stress 

 A common feature of the Australian coal mining geotechnical environment is a level of horizontal stress that is 

much higher than the vertical, often with appreciable stress 

anisotropy. Major principal horizontal stresses two to four 

times the vertical stress are typical, along with minor 

horizontal stresses one to three times the vertical. Elevated 

horizontal stress magnitudes and stress field rotation can 

be associated with major geological structures, such as 

reverse faults. The stress regime often manifests itself in 

roof behavior that is strongly directionally dependent (i.e., 

an unfavorable roadway orientation with respect to the 

major horizontal stress is frequently associated with 

increased roof displacement). 

 The relatively high levels of horizontal stress are a 

cause for prudence in applying any empirical relationships 

involving the CMRR and parameters related to stress (e.g., 

depth) derived from the U.S. coal mining industry, which 

does not seem to experience the phenomena described 

previously (at least not to the same degree). 

 An example is the use of the CMRR as a guide to the 

probability of stability of extended cuts (i.e., temporarily 

unsupported heading lengths of greater than 6 m) in “cutand-flit” (place-changing) development operations, based 

on U.S. research [Mark 1999]. There are at least two 

known Australian cases of the failure of extended-cut 

drivage systems in strongly anisotropic horizontal stress 

fields due to instability of the unsupported cut in the 

unfavorable roadway direction. 

USE OF THE CMRR IN AUSTRALIA: 

PRACTICAL EXAMPLES 

Drivage Method Selection 

 Following directly from the comments made in the 

“Horizontal Stress” section above, it is useful to consider 

the Australian experience of cut-and-flit mining in the 

context of the overall knowledge base. The relationship 

between the CMRR, depth, and stability of extended cuts 

taken during cut-and-flit operations is shown in Figure 3 

[Mark 1999], together with the Australian data. 
69 

 The U.S. database derives from a survey of placechanging operations requesting mine operators to rank 

their experiences with regard to extended-cut stability. 

Also shown in Figure 3 is the discriminant equation trend 

line derived by Mark [1999], which is the line that best 

splits the “always stable” from the “sometimes stable/

never stable” cases. 

 The line of the discriminant equation is given by 

CMRR = 40.9 + H/30.5 (1) 

where H is the depth of cover (m). 

 Effectively, the higher the CMRR, the more likely 

place-changing is to be a success and the more likely the 

roof is to retain static behavior, depending in part on depth 

of cover (and the associated levels of in situ stress). 

 The following comments are made with regard to 

Figure 3: 

1. It is evident that Australian cut-and-flit experience is 

generally consistent with that of the United States 

in terms of the distribution of the data with respect to 

the discriminant equation. 

2. The Australian “always stable” cases are characterized by CMRRs of >50 and depths of <300 m. 

3. The transition from “always” to “sometimes stable” 

is commonly marked by a progressive increase in the 

severity of skin failure (i.e., detachment of the first 

0.5 m of roof in the unsupported cut, often associated 

with a bed with a low unit rating in the immediate 

roof), as opposed to massive roof failure. 

4. The “sometimes stable” Australian case at a 380-m 

depth was characterized by directionally dependent 

roof behavior. 

5. The two “sometimes stable” Australian cases 

involving CMRRs of 39 both involved a coal roof. 

 The CMRR extended-cut relationship has been used, 

in conjunction with cover depth data, to delineate areas of 

potential cut-and-flit development as part of the planning 

process for new mines. However, cut-and-flit has never 

been the preferred method of gate road drivage in Australia, and the use of this process has declined since the 

1990s. 

 More recently, the CMRR extended-cut relationship 

has been used as a guide as to the likely transition point 

from static to buckling roof behavior when using conventional cut-and-bolt (or “in-place”) drivage techniques. This 

transition point is associated with a marked increase in 

roof support requirements and a need to restrict the 

unsupported span at the face, which even with conventional development can vary between 2 and 15 m, depending on the configuration of the miner bolter. 

 Therefore, although a mine may not be contemplating 

using cut-and-flit, use can be made of the fact that the 

successful application of this technique depends on the 

roof behaving in a largely self-supporting fashion (such 

that cuts >6 m will tend to stand unsupported, often for 

extended periods prior to bolting). This has ramifications 

for continuous miner selection, particularly regarding the 

distance from the face at which bolts are installed. 

Roof Characterization 

 The support system designer is required to have an 

appreciation of expected ground conditions in an area to be 

mined, as well as the likely range of ground conditions 

(e.g., the propensity for zones of poor roof). In this regard, 

characterization of likely roof competency at the planning 

CMR

R

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600

Depth of Cover (m)

Discriminant Equation: CMRR = 40.9 + (Depth in metres / 30.5)

Always Stable

Always Stable (Australia)

Sometimes Stable (Australia)

Never Stable

Sometimes Stable

Figure 3.—CMRR extended-cut database. 
70 

stage, backed up with hazard mapping during subsequent 

mine development, are key components of the strata 

management process. 

 New projects place increased emphasis on mapping 

roof competency using drill core data and, in particular, the 

CMRR. The information generated is usually combined 

with the available knowledge of the in situ stresses and 

geological structure in the area of interest to arrive at 

initial estimates of ground conditions and likely associated 

ground support needs. At the operational stage, this information is combined with mapping of geological structure 

and roof behavior to produce composite hazard plans, 

which are progressively extrapolated into adjacent mining 

areas. 

 Increasingly, the focus of these activities is not on 

drawing copious “lines on plans,” but on producing colorcoded hazard information (e.g., green – yellow – red) that 

can be readily assimilated by mining personnel. 

 An example of CMRR contouring for planning purposes is shown in Figure 4. This particular plan is based on 

50 CMRR results from an area of approximately 16 km2

(an exploration borehole spacing of around 500 m). Subsequent mining has borne out the general strength trends 

depicted in the example. 

 From Australian experience, the following refinement 

of the CMRR classification is considered appropriate. This 

particularly focuses on the CMRR 35–55 rating zone, 

which is of most practical interest: 

 CMRR < 35 Very weak roof 

 CMRR ≥ 35, but < 45 Weak roof 

 CMRR ≥ 45, but < 55 Moderate roof 

 CMRR ≥ 55, but < 65 Strong roof 

 CMRR ≥ 65 Very strong roof 

It is understood that less success has been had in the 

United States regarding the development of spatial trends 

of roof strength, although the exercises known to date 

[Mark et al. 2004] have involved significantly greater 

borehole spacings (i.e., typically >2 km). 

 The successful application of this technique in Australia in recent years has generally been based on the 

following: 

1. Exploration borehole spacings of ≤ 500 m. 

2. Drawing interpretations also from complementary 

geological data sets (e.g., structure and sedimentology information). 

3. Adoption of a pragmatic approach as to the quality of 

the information generated versus practical project 

needs. 

4. An example of the interpretation of a CMRR data set 

is given in Figure 5. Although there is no overall 

trend linking the CMRR to depth, if the northwestern 

area (bounded by a seam convergence zone and 

characterized by a distinct thickening of the seam) is 

isolated, then it is apparent that over the major part 

of the resource area, roof quality improves gradually 

with depth. Within this area, it is not necessary or 

appropriate to attempt to define the CMRR to two 

decimal places for a given depth; it is enough to be 

aware that very weak roof can be expected at depths 

of <150 m, with weak/moderate roof at greater 

depths. 

 Spatial trends for the CMRR can be used in 

conjunction with other relevant information and 

parameters (e.g., structural and sedimentology data, depth, 

and drivage orientation with respect to the major horizontal stress) to produce preliminary hazard plans. The 

plans are then progressively refined as actual mining 

information becomes available. An example of a preliminary hazard plan for a major mining project is shown in 

Figure 6. 

Figure 4.—Example of CMRR contouring. 
71 

Design Optimization 

 Information regarding spatial trends of the CMRR has 

been used in a number of recent Australian projects to 

optimize the design of the layout and/or ground support 

system. An example is tapered longwall chain pillars 

(Figure 7), which were first used at the South Bulga Mine 

in New South Wales in 2001. A tapered pillar design is 

feasible wherever the mining layout is unconstrained by 

existing development and there is a reasonably consistent 

change in one or more variables, such as the CMRR or 

depth, from one end of a panel to the other. The dimensions of the longwall block itself do not change, such that 

the panel will be rotated by a fractional amount (the 

“splay” angle is typically <1°, which is practically imperceptible underground). Gate road drivage savings of several kilometers have been achieved by optimizing pillars 

widths using this approach. 

 Figure 6.—Preliminary hazard plan for a major mining 

project. 

Xm

(X+Y)m

Travel RoadBelt Road

Portal / Main 

Development

Headings

Inbye End of

Longwall Panel

Figure 7.—Splayed chain pillar concept. 

 Figure 5.—Example of spatial trends from a CMRR 

data set. 

30

35

40

45

50

55

100 150 200 250 300 350 400

Depth (m)

CMRR

Depth <300m

Depth >300m

North-western thick 

seam area
72 

CONCLUDING REMARKS 

 A number of applications of the CMRR in Australia 

have been outlined. Although the CMRR is not considered 

a perfect rock mass classification system (several current 

issues have been highlighted), it is generally well suited to 

the Australian coal mine geotechnical environment and 

practical ground control issues facing the industry. 

Accordingly, the CMRR is increasingly accepted and its 

applications continue to extend, such that the scope and 

potential for the use of alternative systems is restricted. 

 It should not be implied, however, that the CMRR is 

used exclusively. There are several technical areas, mainly 

in the design of ground support, in which the CMRR and 

its associated empirical relationships are very commonly 

used in conjunction with other methodologies, including 

alternative rock mass classification schemes (specifically, 

Q and RMR), as well as numerical, analytical, and experiential approaches. This is most evident at the feasibility 

stage of a mining project. In the absence of meaningful 

local experience, design outcomes pertaining to alternative 

methodologies are often compared and cross-checked; 

inconsistencies can then be scrutinized. 

 It is expected that, in Australia at least, there will be 

an increasing focus on the use of the CMRR for defining 

spatial roof strength trends across resource areas, as this is 

an area in which the geotechnical engineer can add considerable value to a mining project, provided that the data are 

used rationally. 

REFERENCES 

 Bieniawski ZT [1974]. Geomechanics classification of 

rock masses and its application in tunnelling. In: Proceedings of the Third International Congress on Rock 

Mechanics (Denver, CO), ISRM, 11A:27–32. 

 Colwell MG [1998]. Chain pillar design: calibration of 

ALPS. Australian Coal Association Research Program, 

final report: ACARP project C6036. 

 Mark C [1999]. Application of coal mine roof rating 

(CMRR) to extended cuts. Min Eng 51(4):52-56. 

 Mark C, Molinda GM [1996]. Rating coal mine roof 

strength from exploratory drill core. In: Ozdemir L, Hanna K, 

Haramy KY, Peng S, eds. Proceedings of the 15th International Conference on Ground Control in Mining. 

Golden, CO: Colorado School of Mines, pp. 415–428. 

 Mark C, Molinda GM [2003]. The coal mine roof 

rating in mining engineering practice. In: Aziz N, 

Kininmonth B, eds. Proceedings of the Fourth Underground Coal Operators’ Conference. Carlton, Victoria, 

Australia: Australian Institute of Mining and Metallurgy. 

 Mark C, McWilliams LJ, Pappas DM, Rusnak JA 

[2004]. Spatial trends in rock strength: can they be determined from coreholes? In: Peng SS, Mark C, Finfinger 

GL, Tadolini SC, Heasley KA, Khair AW, eds. Proceedings of the 23rd International Conference on Ground 

Control in Mining. Morgantown, WV: West Virginia University, pp. 177–182. 

 Molinda GM, Mark C [1994]. Coal mine roof rating 

(CMRR): a practical rock mass classification for coal 

mines. Pittsburgh, PA: U.S. Department of the Interior, 

Bureau of Mines, IC 9387. 
73

USE OF THE MINING ROCK MASS RATING (MRMR) CLASSIFICATION: 

INDUSTRY EXPERIENCE 

By Jarek Jakubec, C.Eng.,1

 and Gabriel S. Esterhuizen, Ph.D.2

ABSTRACT 

 In 2000, Laubscher’s Mining Rock Mass Rating 

(MRMR) classification system was updated and published. 

The new system brought a few fundamental changes that 

were in direct response to the challenges and problems 

encountered when applying the classification system in the 

mining environment, specifically caving operations. The 

fundamental changes introduced into the MRMR system in 

2000 were the abandonment of the Rock Quality Designation (RQD) as an input parameter, accounting for healed 

and cemented joints, and the concept of rock block 

strength. 

 The objective of this paper is not to discuss the role 

and usefulness of classification systems; the fact that 

classification systems are widely used in every stage of 

mining projects speaks for itself. This paper discusses 

some of the experiences gained with the MRMR 2000 

system in various mining projects and shows how the 

changes to the system have resulted in improved assessment of rock mass conditions. Issues related to core logging for rock mass assessment are also presented. 

INTRODUCTION 

 Laubscher’s Mining Rock Mass Rating (MRMR) system was introduced in 1975 [Laubscher 1975] and has 

been modified and expanded several times since then 

[Laubscher 1990, 1993; Laubscher and Taylor 1976]. The 

last update was released in 2000 [Laubscher and Jakubec 

2001]. The principal changes in the new In Situ Rock Mass 

Rating (IRMR) included the concept of rock block 

strength, which accounts for the effect of cemented joints 

and veins. All of the changes were in direct response to the 

challenges encountered when applying the classification 

system in the mining environment, specifically caving 

operations in Chile and Australia. 

 If rock mass classification is to reflect reality, it is 

important that all of the critical parameters influencing the 

rock mass behavior are accounted for. Ignoring strength 

reduction due to microfractures or ignoring the presence of 

cemented joints could result in the misclassification of the 

rock mass competency and can have serious safety and/or 

economic consequences. 

 As with any empirically based system, it is important 

that experiences from new projects are analyzed and the 

classification system is further refined and calibrated. 

Although some of the rules and relationships used in 

MRMR and its applications are “crude,” it is our view that 

it is better to use a simplistic method than to ignore the 

issues. To quote John Maynard Keynes: “It is better to be 

roughly right than precisely wrong.” 

 Unfortunately, in the real world, the rock masses are 

inherently variable and do not conform to an ideal pattern. 

The issue of appropriate site-specific geotechnical evaluation of rock masses was recently discussed by Murphy 

and Campbell [in press]. In order to ensure that rock mass 

classification reflects reality, a certain amount of engineering judgment/interpretation is required. A classification 

system can provide guidelines for design, but the mining 

practitioner must ensure that the system is applied correctly. The role of the classification system as a communication tool between operation, engineering, geology, and 

management cannot be stressed enough. Unfortunately, 

a failure in communication is often one of the root causes 

of the problem. 

 This paper discusses some of the experience with 

Laubscher’s IRMR/MRMR system as introduced in 2000. 

THE MRMR CLASSIFICATION SYSTEM: 

AN OVERVIEW 

 There are currently three main classification systems 

used in the metal mining industry: Bieniawski’s RMR 

[Bieniawski 1973], Barton’s Q [Barton et al. 1974], and 

Laubscher’s MRMR [Laubscher and Jakubec 2001]. 

A rough comparison of these systems in terms of required 

input parameters is shown in Table 1. The main differentiators of the MRMR 2000 system compared to previous 

versions of the MRMR, Q-system, and Bieniawski RMR 

systems are: 

• Scale concept in material strength (intact rock > 

rock block > rock mass) 

• Inclusion of cemented joints and veinlets 

• Abandonment of the Rock Quality Designation 

(RQD) as an input parameter 

• Mining adjustments (in comparison to Q) 

 

 1Principal rock mechanics engineer, SRK Vancouver, Vancouver, British Columbia, Canada. 

 2

Senior research fellow, Pittsburgh Research Laboratory, 

National Institute for Occupational Safety and Health, Pittsburgh, PA. 
74 

 Another system that is occasionally encountered in 

metal-mining projects is the Geological Strength Index 

(GSI) [Hoek et al. 1995]. Since this system cannot be 

easily “decoded” and individual parameters assessed 

separately, it was not used for comparison in Table 1. The 

objective of this paper is not to discuss which system is 

more suitable, nor is it to describe every detail of the 

MRMR system. It is recommended that the reader refer to 

Laubscher and Jakubec [2001], where the MRMR 2000 

system is fully discussed. Flowsheets illustrating the different parts of the MRMR 2000 system are shown in 

Figures 1–2. Figure 1 illustrates the parameters used to 

determine the IRMR, and mining adjustments that produce 

the final MRMR value are presented in Figure 2. 

 The application of the MRMR system in mine design 

is presented in the paper “Planning Mass Mining Operations” [Laubscher 1993]. The main design recommendations and guidelines include: 

• Support design 

• Cavability diagrams and stability of open stopes 

• Extent of cave and failure zones 

• Caving fragmentation 

• Caving rates and mining sequence 

• Pit slope guidelines 

The design charts and associated recommendations are 

based on experience gained in mining projects around the 

world and have found wide acceptance within the mining 

industry. 

THE CONCEPT OF A ROCK BLOCK 

 The MRMR 2000 system accounts for the effect of 

scale in its assessment of rock strength, recognizing that 

small-scale intact rock samples do not necessarily reflect 

the strength of the larger rock blocks bounded by throughgoing joints. The concept of a rock block is illustrated in 

Figure 3. A rock block is defined as the rock material 

bounded by throughgoing joints and can contain discontinuous fractures and veinlets. It is important to separate 

continuous “block-bounding” joints from discontinuous 

fractures and veinlets, especially for mass mining methods 

where cavability and fragmentation assessment are fundamental to the design. 

 The scale concept, which addresses the material 

strength from small intact rock samples that can be tested 

directly in the laboratory, through rock block strength that 

is influenced by discontinuous fractures and veinlets, to the 

full-scale rock mass strength, is illustrated in Figure 4. 

Category Parameters

 Beniawski RMR Barton Q Laubscher 90 Laubscher 2000

Intact rock strength UCS x x x x

Open joint frequency RQD x x x -

FF/m x - x x

Joint set (Jn) x x x x

Open Joint strength Roughness (Jr) x x x x

Alteration (Ja) x x x x

Infill (Ja) x x x x

Cemented joints CJ/m - - - x

quantity and strength CJ strength - - - x

Table 1.—Comparison of main classification systems 

used in the mining industry 

Figure 1.—IRMR 2000 flowsheet. 

Figure 2.—Mining adjustments. 
75 

 The challenge is to assign appropriate strength reduction factors to account for the cemented joints (Figure 5), 

fractures, and veinlets that may be present in rock blocks. 

It is clear that if a classification system ignores such 

features, the rock mass strength is overestimated, or if they 

are forced into the open joint category, the rock mass is 

underestimated. 

ACCOUNTING FOR CEMENTED JOINTS 

AND VEINLETS 

 

 The MRMR 2000 system introduced empirical charts 

where the impact of the quantity and quality of cemented 

joints and veinlets on rock block strength can be assessed. 

The method is based on the Mohs hardness number of the 

infill materials and the frequency of the filled joints and 

veinlets. 

 It should be noted that the suggested Mohs hardness 

number for estimating the strength of the infill is only a 

field guideline, and effort should be made to better define 

the strength of such defects. The use of laboratory tests, 

back analysis, and numerical models (such as Itasca’s Particle Flow Code (PFC)) could be very useful in better 

understanding the role of healed discontinuities with 

regard to rock block strength. 

 The effect of cemented joints and veinlets can have a 

significant impact on the caving process in block caving or 

sublevel caving operations. Figure 6 illustrates the difference in the predicted fragmentation for a rock mass that 

contains healed, calcite-filled veinlets based on two methods of assessing the IRMR value. The Block Cave Fragmentation (BCF) [Esterhuizen 2003] software package was 

used to conduct the analyses. The software makes use of 

joint set data, uniaxial compressive strength of the rock, 

stress field, and characteristics of small-scale fractures and 

veinlets to estimate rock fragmentation during block 

caving. The rock block strength is calculated as part of the 

process and affects stress-related fracturing. The lower 

curve in Figure 6 shows the predicted fragmentation if the 

presence of fractures and cemented veinlets is ignored in 

the assessment of rock strength. These results indicate very 

coarse fragmentation, with about 25% of the rock fragments being less than 2 m3

 in size. The upper curve shows 

the results if the fractures and veinlets are accounted for. In 

this case, the predicted fragmentation is good, with about 

90% of the rock fragments predicted to be less than 2 m3

. 

The difference in predicted fragmentation is largely due to 

the effect of the field stress on the rock blocks. If the 

fractures and veinlets are ignored, the rock block strength 

is overestimated, and coarse fragmentation is predicted. 

 Figure 3.—Example of a rock mass that contains 

throughgoing joints (thick lines) as well as discontinuous 

fractures (thin lines). Rock blocks are bounded by the 

throughgoing joints. 

Figure 4.—Scale concept used in MRMR classification. 

 Figure 5.—Cemented joints in the core (left) could 

significantly influence rock block strength and fragmentation in a caving environment (right). 
76 

When the effects of these features are included, the 

assigned rock block strength is reduced, which in turn 

dramatically reduces the predicted fragmentation. The 

expected fragmentation has a significant impact on the 

likely production rates, mine layout, and operational cost 

of a block-caving operation. 

RQD AND FRACTURE FREQUENCY 

 The other major difference of MRMR compared to 

other classifications is in the utilization of RQD. The RQD 

system was originally developed for tunneling conditions 

and was published in 1967. The fact that it is still used 

today is a good testimony to Deere, who introduced it 

40 years ago. 

 RQD is a very simple, effective, and quick method to 

assess the rock mass competency in certain types of rocks. 

However, besides the lack of accountability for the basic 

rock mass parameters such as intact rock strength and 

strength of defects, the tradeoff against its simplicity is its 

poor reliability in highly fractured, massive, or highly 

anisotropic conditions. The method simply does not have 

the resolution that may be required for a more accurate 

assessment of fragmentation, cavability, and other mine 

design aspects. Figure 7 illustrates some of the issues 

related to RQD as a rock mass descriptor, and the RQD is 

compared to the IRMR obtained from fracture frequency. 

 If the rock mass character is such that RQD does not 

reflect the conditions accurately, then, of course, any 

classification system that uses RQD is exposed to problems. Figure 8 illustrates an example from one of the major 

block-caving projects in Chile, where the difference in 

IRMR values obtained by the fracture frequency (FF/m) 

method versus the RQD method is quite obvious. The 

comparison was made from drill core logging for a blockcaving project in which an accurate assessment of rock 

mass conditions has a significant impact on the choice of 

mine layout, operating procedures, and financial investment. In this case, the IRMR calculated from the FF/m was 

considered to be more representative of the actual rock 

mass conditions than the values based on the RQD. Thirdparty review of the outcomes, inspection of exposures in 

the current open-pit mine, and comparison to values estimated from the GSI rating confirmed this conclusion. 

PRACTICAL PROBLEMS WITH ROCK MASS 

ASSESSMENT RELATED TO 

DATA COLLECTION METHOD 

 As discussed above, the difference between the reality 

and the rock mass competency models could be due to the 

lack of ability to include specific geological features in our 

classification systems, e.g., cemented joints and veinlets. 

 Figure 8.—Example of difference between RQD and fracture frequency-based IRMR. The IRMR based on fracture 

frequency (solid line) is considered more representative of 

actual rock mass conditions. 

 Figure 6.—Effect of calcite-filled veinlets on predicted 

fragmentation in block caving. 

0

10

20

30

40

50

60

70

80

90

100

0.01 0.1 1 10 100 1000

Block size (m3)

Percent passing (%)

Veinlets included

Veinlets ignored

 Figure 7.—Example of the problems with RQD assessment of highly fractured or massive rock masses. 
77 

However, if only drill core is used for rock mass assessment, we are exposed to a whole range of biases, and the 

resulting description of the rock mass could be significantly skewed. The potential problems and pitfalls were 

described by Laubscher and Jakubec [2001] and Murphy 

and Campbell [in press]. It is important to realize that rock 

mass assessment based on drill core only can easily be off 

by 50%. 

 The main challenges in rock mass assessment based on 

core logging, regardless of the classification system used, 

are: 

• Differentiation between artificially induced breaks 

and natural defects. In situ borehole scanners can 

help to assess in situ conditions. 

• Assessment of discontinuities in foliated or highly 

laminated rocks. In such rock masses, the borehole 

scanner may not be effective. 

• Differentiation between continuous joints and 

discontinuous fractures. This problem cannot be 

successfully resolved without rock mass exposures 

(see Figure 9). 

• Drilling orientation bias. Missing or underestimating discontinuity sets subparallel to the drillhole. Different orientation of the drillholes can 

mitigate the problem. 

• Accurate assessment of weak joint infill that is 

washed out in most drilling processes. Triple tube 

techniques can help to alleviate this problem. 

• Rock strength assessment in weathered/altered sensitive rock types such as kimberlites and mudstones. 

Using specialized drilling fluids, very careful sample collection/preservation programs, and speedy 

delivery to the laboratory can partly mitigate these 

problems. 

• Material anisotropy. Assessment of both intact rock 

strength and discontinuity strength anisotropy from 

the drill core could be a problem. The core crosssection is simply too small to capture joint geometry. (See the example shown in Figure 10.) 

 Any of the points mentioned above can have a significant impact on the rock mass assessment, and it is necessary that data be scrutinized in that respect. 

DISCUSSION AND CONCLUSIONS 

 Some of the challenges in assessing rock mass conditions have been addressed by the MRMR 2000 system. 

These include the abandonment of RQD as a parameter, 

accounting for healed and cemented joints, and the introduction of the concept of rock block strength. This paper 

shows how these modifications have resulted in improved 

assessment of critical aspects of rock mass behavior for 

mine design. 

 When assessing rock mass behavior (by any method), 

it is important to remember that we cannot rely only on 

exact science. The inherent variability of nature does not 

allow the development of a universal, rigorous rock mass 

classification system that would be practical at the same 

time. It is therefore necessary to keep the system flexible 

and open to adjustments. This raises the issue of whether 

 Figure 10.—Joint geometry may not be obvious from the 

drill core unless the joint is intersected at a very shallow 

angle.

 Figure 9.—Picture illustrating the bias that could be 

introduced by borehole orientation. Also, it is difficult 

from the core to judge which discontinuities represent 

continuous joints and which are small-scale fractures. 
78 

we should strictly follow the letter of the classification 

systems or whether we should treat classification systems 

as a guideline to be used together with engineering 

judgment. The authors believe that spirit is more important 

than the letter and that field observations must be 

accounted for in the final judgment. 

 Unfortunately, the trend in the mining industry is to 

shift focus from the field to the office and solve problems 

“remotely.” As our computational skills have increased 

dramatically, it seems that our observational skills have 

decreased at the same rate. Also, the discipline and somewhat rigorous process of data collection, visualization, and 

analysis have broken down. Despite the fact that most of 

today’s projects have rendered three-dimensional models 

of geology (or at least an artist’s image), it is very rare 

these days to find a proper set of working plans and 

sections where a “creative thinking” process was applied 

and geological and geotechnical concepts are tested prior 

to computerization. We would like to quote Dr. ScottSmith—“the answers are in the rocks”—to remind us that 

“reality” checks should be constantly performed on our 

models. 

ACKNOWLEDGMENT 

 The authors would like to thank Dr. Dennis Laubscher 

for reviewing this paper and for his valuable comments. 

REFERENCES 

 Barton N, Lien R, Lunde J [1974]. Engineering classification of rock masses for the design of tunnel support. 

Rock Mech 6(4):189–236. 

 Bieniawski ZT [1973]. Engineering classification of 

jointed rock masses. Trans S Afr Inst Civ Eng 15:335–344. 

 Esterhuizen GS [2003]. Block cave fragmentation 

(BCF) user’s manual. Unpublished. 

 Hoek E, Kaiser PK, Bawden WF [1995]. Support of 

underground excavations in hard rock. Rotterdam, Netherlands: Balkema. 

 Laubscher DH [1975]. Class distinction in rock 

masses. Coal gold base min S Afr 23(Aug). 

 Laubscher DH [1990]. A geomechanics classification 

system for the rating of rock mass in mine design. Trans 

S Afr Inst Min Metal 9(10). 

 Laubscher DH [1993]. Planning mass mining operations. In: Hudson J, ed. Comprehensive rock engineering. 

Vol. 2. Pergamon Press, pp. 547–583. 

 Laubscher DH, Jakubec J [2001]. The MRMR rock 

mass classification for jointed rock masses. In: Hustrulid 

WA, Bullock RL, eds. Underground mining methods: 

engineering fundamentals and international case studies. 

Littleton, CO: Society for Mining, Metallurgy, and 

Exploration, Inc., pp. 475–481. 

 Laubscher DH, Taylor HW [1976]. The importance of 

geomechanics of jointed rock masses in mining operations. 

In: Proceedings of the Symposium on Exploration for 

Rock Engineering (Johannesburg, South Africa), pp. 119–

128. 

 Murphy BA, Campbell JR [in press]. Establishing a 

site specific mining geotechnical atlas. In: Proceedings of 

the First Canada-U.S. Rock Mechanics Symposium, Vancouver, British Columbia, Canada. 
79

CLASSIFICATION-BASED PROBABILISTIC DESIGN OF GROUND SUPPORT 

By Radford B. Langston,1

 Dr. Hendrik A. D. Kirsten,2 Dr. Graham C. Howell,3 and John A. Marjerison4

ABSTRACT 

 Probabilistic design is gaining wider acceptance in the 

rock engineering community since it allows more rigorous 

determination of risk relating to ground fall or excavation 

instability. Risk analysis can be conducted by various 

means, but the basis is formed by either objectively or 

subjectively determining probability of occurrence of an 

event. In the case of rock engineering, this event is either 

instability or excavation failure. Rock mass classification 

systems provide objective analysis of data collected on a 

typically subjective basis that also relate closely to excavation stability. A probabilistic analysis technique is presented that uses statistical distributions of rock mass and 

material properties, ground support fixture specifications, 

stress conditions, opening geometry, and ground support 

installation quality to more rigorously determine probability of failure for an underground opening and the subsequent risk to personnel. 

INTRODUCTION

 The philosophy governing much ground support 

practice in underground excavation relies on the concept of 

constructing a support arch by harnessing the frictional and 

cohesive strength inherent in the rock mass through 

reinforcement. Rock masses, in some cases, are selfsupporting and need few, if any, additional elements to 

mobilize their strength. However, in instances where the 

rock mass requires added elements to be stable, three 

essential components must be present in order to effectively construct an arch. The first two components are rock 

reinforcement and surface support. Presence of competent 

abutments for the support arch to stand upon is the third 

vital component. 

 Field data collection systems are useful tools to gauge 

the effectiveness of these three components in creating a 

stable support arch. These systems typically are intimately 

tied to empirical design methods, design graphs, and 

deterministic approaches, as are the principles of the 

geomechanical concepts put forth in this paper. However, 

with the technique described herein, both objective and 

subjective probabilistic methods are suggested to derive a 

basis for design, as well as an overall picture of system 

integrity and risk analysis. 

 In the course of rock mass data collection, reduction, 

and design, varying degrees of uncertainty exist concerning all input parameters. Ignorance or simply the 

unknowability of specific values for often critical design 

factors makes a probabilistic approach to rock mass classification and ground support design a useful tool. Empirical 

and deterministic design approaches do not incorporate 

uncertainty into the process aside from ad hoc methods or 

simply by pure overdesign. Probabilistic methods also 

allow the production of more objective end products from 

input variables that are frequently quite subjective. Objective products resulting from the design process make their 

contribution in a risk, financial, or other decision-making 

analysis more rigorous. If little or no geotechnical data 

exist, this process can be also be used to conduct “what if” 

or sensitivity analysis for specified components of a feasibility study. 

BASIC PROBABILITY CONCEPTS 

 Some basic probability concepts as they apply to this 

particular problem are outlined below. 

Cause and Effect 

 Human interaction is a world of complex cause and 

effect. Causes to effects that we pursue are often effects of 

lesser-order causes. A cause of an effect is an effect of one 

or more identifiable underlying causes. Human shortcomings, in principle, constitute the lowest order of causes 

because humans ultimately hold the initiative to all action. 

 Cause: underlying factor that leads to a particular event 

 Effect: outcome resulting from a particular event 

Independent and Dependent Causes 

 Two separate types of causes are considered in this 

paper. Independent causes take place separately from other 

causes and are represented by a logical “OR” statement. 

Dependent causes require other factors in order to occur 

and are represented by a logical “AND” statement. Probabilities of occurrence of independent causes are added with 

each other to exclude joint occurrences, and those for 

dependent causes are multiplied together. 

 

1

Consulting engineer, Red Lodge, MT. 

2

Consulting engineer, Fontainebleau, South Africa. 3

Steffen, Robertson, and Kirsten, Cape Town, South Africa. 4

Stillwater Mining Co., Nye, MT.
80

Thresholds for Probability 

 International thresholds for probability of loss are 

given after Cole [1993] in Table 1 in terms of total losses 

of life, property, and money. Total loss of life denotes 

fatality, whereas property denotes fixed assets and money, 

and business ventures. Thresholds are selected in terms of 

voluntary and involuntary exposure of the affected aspects 

to the hazards. They apply to the overall probability of 

loss, which includes the probabilities of failure and 

exposure. 

 The thresholds are expressed in terms of lifetime 

frequencies, which are defined as the probable unit number 

of times that a hazard would occur during the life of the 

person affected. A natural lifetime is on average 70 years 

and a working lifetime is 50 years, which corresponds 

to 250 × 8 working hours per annum or 250 × 8 × 50 = 

100,000 total working hours. Expressed as a percentage, 

the lifetime probability of loss is equal to one-tenth of the 

fatality accident rate. The fatality accident rate is equal to 

the number of deaths from 1,000 people who are involved 

in a hazardous activity for their entire lives. The upper 

limit of lifetime probability of loss is equal to 7,000%, 

which by definition is the product of 70 as the average 

lifetime in years and 100% as the probability of occurrence 

of an event that will certainly occur in every year. The 

probabilities of failure that may be determined for engineering systems represent lifetime frequencies because 

they represent the unit number of times that the systems 

may fail in the conceivable future. 

 Cole [1993] determined the threshold probabilities of 

losses in Table 1. They are generally much more stringent 

than those recommended in the literature prior to 1987, but 

are substantially in agreement with recommendations of 

various authors since. The “Acceptable and Tolerable Risk 

Criteria” given in Appendix H of the Landslide Risk 

Management Concepts and Guidelines published by the 

Australian Geomechanics Society [2000] correspond 

accurately with these thresholds. 

Assignment of Probabilities 

 Probabilities of failure can be derived from randomly 

sampled distributions of input variables to generate a 

distribution of end product capacity versus demand values. 

They can also be subjectively assigned through experience, 

engineering judgment, and use of the eight-point scale 

shown in Table 2. 

Objective Assignment of Probability 

 The likely occurrence of an event may be objectively 

determined by the following process. First, random 

sampling of the distributions for the governing input 

parameters produces a population of rock mass values that 

are translated into ground support demand. Next, a second 

group of values of ground support capacity is generated in 

the same manner. Values from both of these distributions 

are then randomly sampled to generate a distribution of 

capacity versus demand. The probability of failure is then 

the area under the probability density function left of the 

value where capacity equals demand. Distributions can be 

derived from statistical analysis of the input data or from 

predetermined functions if the statistical parameters are not 

well established. Figures 1–3 are normal, triangular, and 

uniform probability density functions commonly used for 

these purposes. 

Table 1.—Acceptable lifetime probabilities of total losses [Cole 1993] 

Attitude to reliability Probability (%) of total loss of Degree of risk Voluntary Involuntary Life Property Money 

Very risky.......... Very concerned Totally 

unacceptable 

70 (–) 

(deep-sea diving or 

rock climbing) 

700 (–) (volcano or 

avalanche) 7,000 (–) (gambling) 

Risky................. Concerned Not acceptable 

7 (1.60) 

(deep-sea diving or 

rock climbing) 

70 (–) (volcano or 

avalanche) 700 (–) (gambling) 

Some risk.......... Circumspect Very concerned 

0.7 (2.50) 

(car, airplane, or 

home accident) 

7 (1.60) (undermining 

or earthquake) 

70 (–) (small 

business failure) 

Slight chance .... Of little concern Concerned 

0.07 (3.22) 

(car, airplane, or 

home accident) 

0.7 (2.50) 

(undermining or 

earthquake) 

7 (1.60) (small 

business failure) 

Unlikely ............. Of no concern Circumspect 0.007 (3.82) (public 

transport accident) 0.07 (3.22) (flooding) 0.7 (2.50) (company 

failure) 

Very unlikely ..... Of no concern Of little concern 

0.0007 (4.35) 

(fatality in public 

place) 

0.007 (3.82) (failure 

of foundation on soil) 

0.07 (3.22) (failure of 

banks or building 

societies) 

Practically 

impossible......... Of no concern Of no concern 

0.00007 (4.83) 

(failure of nuclear 

powerplant) 

0.0007 (4.35) (failure 

of foundation on 

rock) 

0.007 (3.82) 

(collapse of National 

Savings) 
81

SUBJECTIVE ASSIGNMENT OF PROBABILITY 

 The eight-point scale described below was developed 

for evaluating operational safety in South African coal 

mines. It can consistently evaluate operational safety, 

system integrity, economic viability, process reliability, 

and environmental protection and rehabilitation in a wide 

range of engineering systems [Kirsten 1999]. The six-point 

scale Risk Assessment Table published by the Institution 

of Civil Engineers and the Faculty and Institute of 

Actuaries [1998] in the United Kingdom is identical in 

concept. The six-point scale in Appendix G of the 

Landslide Risk Management Concepts and Guidelines

published by the Australian Geomechanics Society [2000] 

Figure 1.—Normal probability density function and cumulative distribution function [MathWorld 2007]. 

Figure 3.—Uniform probability density function and cumulative distribution function [MathWorld 2007]. 

Figure 2.—Triangular probability density function and cumulative distribution function [MathWorld 2007]. 
82

is almost identical to Table 2 in both qualitative and 

quantitative levels of probability. 

Table 2.—Classes for probability of occurrence 

Qualitative evaluation Quantitative evaluation 

Certain.................................. Every time 1.0 

Very high .............................. 1 in 10 10–1

High................................. 1 in 100 10–2

Moderate .............................. 1 in 1,000 10–3

Low.................................. 1 in 10,000 10–4

Very low................................ 1 in 100,000 10–5

Extremely low....................... 1 in 1 million 10–6

Practically zero..................... 1 in 10 million 10–7

GEOMECHANICAL DATA

 Rock mass parameters are closely tied and most often 

differ with variations in rock type, which usually also 

defines the spatial relationships of the parameters and the 

excavation. The system used in this paper for assessing 

rock mass quality is the Q-system developed by Barton 

et al. [1974]. The methods described in this paper would 

work equally well for virtually any rock mass classification system. 

 Rock mass quality, Q, varies on a logarithmic scale 

from 0.001 to 1,000 and is determined by Equation 1: 

 Q = (RQD/Jn ) × (Jr /Ja ) × (Jw /SRF) (1) 

RQD/Jn is an estimate of block size. Jr/Ja generally 

represents the strength of the discontinuities demarcating 

the blocks. Jw/SRF is a measure of the active stress present 

in the rock mass [Barton et al. 1974]. 

 All of the inputs for the equation can be defined as 

random variables and, as such, may belong to populations 

easily defined by statistical distributions. Five of the six 

parameters needed to define rock mass quality (Q) listed 

below can be sampled from their own distribution. The 

last, stress reduction factor (SRF), can be either chosen 

from a distribution of SRF or calculated from the UCS and 

depth, each of which can also be picked from a specific 

distribution [Kirsten 1988]. Any one of these random 

variables can also be held to a constant value. The rock 

mass input parameters are listed below. 

• RQD is a measure of the degree of fracturing in the 

rock mass; 

• Jn represents the total number of discrete joint sets 

in the rock mass; 

• Jr is a measure of friction and, to a somewhat lesser 

degree, cohesion of a discontinuity; 

• Ja represents the amount of both cohesion and friction of a discontinuity; 

• Jw is the amount of water inflow affecting the rock 

mass; and 

• SRF quantifies the effect of the excavation on the 

rock mass. 

Other geomechanical parameters that can be selected out 

of specified distributions are— 

• UCS; 

• Angle of internal friction; 

• Unit weight; 

• Maximum principal primitive stress; 

• Minimum principal primitive stress; 

• Maximum principal primitive stress direction; 

• Depth below surface; 

• Span variation; 

• Excavation support ratio; and 

• Geologic structure variations. 

 All of these rock mass and geomechanical properties 

combine with excavation size and geometry to place a 

demand of some magnitude on the ground support system. 

The designed ground support system must reinforce and 

confine the rock mass to the point that the effects of this 

demand are counteracted in order to provide excavation 

stability. 

SUPPORT COMPONENTS 

 Variation in ground support elements is typically less 

pronounced than rock mass properties because they are 

produced by a relatively well controlled manufacturing 

process. However, the installation process, design layout, 

and excavation profile often vary widely. For a specified 

rock-reinforcing fixture, any of the following support 

component parameters can be sampled randomly from an 

appropriate distribution: 

• Fixture length; 

• Angle from normal; 

• Material properties; 

• Fixture specifications; 

• Hole diameter; 

• In-plane spacing; 

• Out-of-plane spacing; 

• Plate properties; and 

• Properties of the fixture rock interface(s). 

Statistical distributions can represent the shotcrete properties listed below: 

• Thickness; 

• Span-thickness ratio; 

• Compressive strength; 

• Shear strength; and 

• Reinforcement properties. 
83

Surface support elements also conform to this process. 

These are: 

• Area per meter; 

• Caternary rise; 

• Strand tensile strength; 

• Mesh anchor shear area; 

• Weld strength; and 

• Strand spacing. 

 When the distribution of rock mass and geomechanical 

demand has been generated, it is compared to a distribution of ground support capacity. This comparison results in 

a distribution of capacity versus demand. The probability 

of failure determined from the capacity versus demand 

distribution is the area under the best-fit curve left of the 

point where capacity equals demand. This represents 

probability of structural failure (psfw), and the process of 

deriving the probability is applied to each wall of the 

excavation in turn. Probability of failure for each wall will 

then be combined with the probabilities for the other walls 

by a logical “OR” statement since failure can occur in any 

wall independent of the others. From this point, the other 

aspects determining total probability failure and overall 

threat may now be applied. 

 Failure of ground support subsystems such as installation quality increase the overall probability of failure, but 

due to the difficulty in objective measurement, the probabilities of failure are best subjectively applied. These 

probabilities of the factors listed below are subjectively 

assessed and applied to each wall separately. The subjective application of the probabilities should be derived from 

the eight-point scale in Table 2. In general terms, the subsystems are described by the following list: 

• Fixtures installed per manufacturer’s specifications 

and standard industry practice 

• Proper anchorage for the specified fixture 

• Rock bolt plates tight against the rock face 

• Angle of the installed fixture as close to normal to 

the bearing surface as possible 

• Significant structures or weak contacts crossed 

and locked together 

• Systematic and regular support installation 

• Adherence to specified design or ground support 

standard 

• Installed support adequate for the ground type 

• Rock not excessively damaged due to blasting 

• Blast holes drilled on line and not out into the walls 

or up into the back 

• The walls have relatively smooth profile 

• No excessive loading of ground support elements 

• Surface support elements secured tightly against the 

rock surface 

DISCUSSION 

 Each of these subjectively assigned probabilities of 

subsystem failure can now be combined with the probability of structural failure for a given wall with a logical 

“OR” statement, giving a probability of overall structural 

failure (posfw) calculated by: 

Posfw = 1 – [(1 – psfw) * (1 – pss1) * (1 – pss2) * (1 – pssn+1)] (2) 

where posfw = P(overall structural failure wall); 

 psfw = P(probability of structural failure wall); 

 pss1 = P(probability of subsystem failure 1); 

 pss2 = P(probability of subsystem failure 2); 

and pssn+1 = P(probability of subsystem failure n). 

 Three additional aspects needed in determining total 

probability of failure and overall threat to personal injury 

must now be considered. The first of these is probability of 

ejection freedom. This concept is based on the degree of 

confinement or restraint against spontaneous block ejection resulting from a gravitational or seismic acceleration 

provided to the rock mass as the level of ground support 

increases. As the number of ground support elements 

multiply, the probability that a block of rock can spontaneously be ejected, taking a worker by surprise, decreases. 

The probability of surprise or ejection freedom with no 

support installed is 100%, or certain. As the support 

quantity increases, the probability of ejection freedom 

decreases by an order of magnitude as shown in Table 3. 

Table 3.—Probability of ejection freedom 

Support level Probability of ejection freedom

No support........................... Every time 1.0 

Light support........................ 1 in 10 10–1

Moderate support.................. 1 in 100 10–2

Heavy support...................... 1 in 1,000 10–3

Very heavy support................ 1 in 10,000 10–4

Extremely heavy support......... 1 in 100,000 10–5

 The next facet of the overall threat to consider is the 

probability of personnel appearance. This probability can 

be calculated as the percentage of the entire work shift that 

personnel spend exposed to potential excavation instability. It can also be referenced from Table 4. 

Table 4.—Probability of personnel appearance 

Personnel appearance Probability 

Continuous........................... 1.0 

Very regular......................... 0.3 

Regular.............................. 0.03 

Occasional............................ 0.003 

Very occasional..................... 0.0003 

Rare .................................... 0.00003 
84

 The final variable needed to complete this calculation 

is the probability of personnel coincidence. Coincidence is 

essentially calculated by a 0.5-m width of a person divided 

by the total length of excavation that exposes that person 

to a rock fall hazard. Thus, for one person in 50 m of tunnel, the personnel coincidence is 0.5/50, or 0.1. 

 In order to now determine total probability of failure, 

the probability of overall structural failure for each wall is 

“OR” gated to the other walls to obtain the probability of 

failure for the entire excavation. 

Posfe = 1 – [(1 – posfw1 ) * (1 – posfw2 ) * (1 – posfw3)] (3) 

where posfe = P(overall structural failure excavation); 

 posfw1 = P(overall structural failure wall 1); 

 posfw2 = P(overall structural failure wall 2); 

and posfw3 = P(overall structural failure wall 3). 

 In order to calculate the overall threat of injury for the 

entire excavation, the overall probability of structural failure for the excavation is “AND” gated with the probability 

of ejection freedom, probability of personnel appearance, 

and probability of personnel coincidence, as shown in 

Equation 4: 

T = posfe * pejec * papp * pcoin (4) 

where T = overall threat of injury; 

 posfe = P(overall structural failure excavation); 

 pejec = P(ejection freedom); 

 papp = P(personnel appearance); 

and pcoin = P(personnel coincidence). 

 When the overall threat is calculated, it can be compared to the thresholds shown in Table 1. If the overall 

threat is below an acceptable level, the design can stand. If 

it is not, a number of methods can be employed to reduce 

the threat. Rock mass variable distributions should be 

checked for plausibility or appropriate application and 

assumptions regarding input variables recalibrated. The 

design can be reconsidered and altered to increase ground 

support capacity, thereby reducing the possibility of structural failure. Subsystem shortfalls found to contribute 

significantly to the probability of structural failure can be 

remediated. The probability of ejection freedom decreases 

with an increase in support quantity, and the probability of 

personnel appearance and coincidence can be reduced by 

limiting access to the area. 

 Another methodology that has been employed is to 

conduct a survey of numerous excavations at a project 

such as a large mining operation and apply this process to 

each excavation. When the overall threat for an appropriate 

number of workings have been calculated, the distribution 

of threats can be plotted, as shown in Figure 4. 

 An appropriate design level of threat for work in 

underground excavations is less than 10–4, or 1 chance in 

10,000. As a basis for comparison, this probability of 

occurrence is equivalent to acceptable risk of injury on a 

public transport system [Kirsten 1999]. Values above this 

threshold indicate a need to promptly address conditions, 

while values below this indicate acceptable level of threat 

or risk. Probabilities that are several orders of magnitude 

below the threshold indicate less than optimal ground support economy. Figure 4 shows an example distribution of 

threat from 42 hypothetical cases. 

 As previously stated, when overall threat exceeds the 

threshold, access by personnel to the area can be limited 

until ground support remediation has been completed. It is 

advisable to install remedial ground support only from 

under supported ground that has an overall threat below a 

threshold acceptable to operations management. Prohibiting access to an area effectively decreases exposure so that 

the threat is reduced to below the acceptable level of 

threat. A reasonable goal is to not let more than 5% of 

headings exceed an acceptable threshold of 10–4 at any 

instant of time. 

SUMMARY 

 Typical rock mass classification design systems 

involve data collection, data reduction, and then plotting 

the reduced data on empirical design curves. From this 

step, empirical or deterministic criteria are applied and a 

final design proposed. This type of process is quite 

adequate for many rock engineering problems. Some, 

however, lend themselves to probabilistic analysis due to 

the inhomogeneous nature of the rock mass and inherent 

uncertainty of its characterization. 

 Rock mass data collection, reduction, and design 

involve varying degrees of uncertainty due to the variability of all input parameters. Parameters collected during 

the course of rock mass classification and excavation 

design are random variables and, as such, belong to populations naturally expressed by statistical distributions. 

Histogram of Overall Threat 

0

2

4

6

8

10

12

14

16

Overall Threat

Frequency

Threshold

1.0E-8 1.0E-7 1.0E-6 1.0E-5 1.0E-4 1.0E-3 1.0E-2 1.0E-1

Figure 4.—Example histogram of threat.
85

Unknowns regarding specific values needed in ground 

support allow a probabilistic design approach to provide 

inputs that can be used with a known degree of confidence. 

That degree of confidence may be low or high, but it is 

known and was systematically derived. This process can 

also produce more objective end products from frequently 

subjective input variables. Objective products resulting 

from the design process make their contribution in a risk, 

financial, or other decision-making analysis more rigorous. 

If little or no geotechnical data exist, this process can be 

also be used to conduct “what if” or sensitivity analysis for 

specified components of a feasibility study. 

 Probability of loss thresholds are specified for total 

loss of life, property, and money. Thresholds are chosen 

with regard to voluntary and involuntary exposure to hazards and expressed in terms of lifetime frequencies. Probabilities of failure determined for engineering systems 

represent lifetime frequencies, since they correspond to the 

unit number of times the systems could fail in a potential 

lifetime. 

 After generation of a rock mass and geomechanical 

demand distribution, it is compared to a ground support 

capacity distribution that gives rise to a capacity versus 

demand distribution from which the probability of structural failure is calculated. Subjectively derived probabilities of failure for ground support subsystems are added to 

the probability of structural failure to give a probability of 

overall structural failure. The probability of ejection freedom, personnel appearance, and personnel coincidence are 

multiplied with the overall probability of structural failure 

to give the overall threat of injury. 

 When the overall threat of injury is above an acceptable threshold, several approaches can be taken to lessen 

the threat, from recalibrating input variables to increasing 

the quantity of ground support and remediating ground 

support subsystem shortfalls. Increasing support quantity 

decreases the probability of ejection freedom, and limiting 

access to the area lowers the probability of personnel 

appearance and coincidence. 

REFERENCES 

 Australian Geomechanics Society [2000]. Landslide 

risk management concepts and guidelines. Aust Geomech 

Mar:49–92. 

 Barton N, Lien R, Lunde J [1974]. Engineering classification of rock masses for the design of tunnel support. 

Rock Mech 6(4):189–236. 

 Cole K [1993]. Building over abandoned shallow 

mines. Paper 1: Considerations of risk and reliability. 

Ground Engineering, pp. 35–37. 

 Hoek E [1994]. Acceptable risks and practical decisions in rock engineering. Short course notes for 12th 

Annual Canadian Conference on Tunneling. 

 Holton GA [2004]. Defining risk. Financ Analysts J 

60(6):19–25. 

 Institution of Civil Engineers and the Faculty and 

Institute of Actuaries [1998]. Risk analysis and management for projects (RAMP). London: Thomas Telford, Ltd. 

 Kirsten HAD [1988]. Norwegian Geotechnical Institute or Q-system by N. Barton. Discussion contribution. 

Symposium on Rock Classification Systems for Engineering Purposes D–18, ASTM STP 984, ASTM, Cincinnati, 

OH, pp. 85–88. 

 Kirsten HAD [1999]. Course on evaluation of risk as a 

decision-making criterion. SRK Consulting, 15 sections. 

 MathWorld [2007]. Continuous distributions. [http://

mathworld.wolfram.com/topics/ContinuousDistributions.html]. 

Date accessed: March 2007. 

87

THE GEOLOGICAL STRENGTH INDEX (GSI): A CHARACTERIZATION TOOL 

FOR ASSESSING ENGINEERING PROPERTIES FOR ROCK MASSES 

By Paul G. Marinos, Ph.D.,1 Vassilis Marinos,2

 and Evert Hoek, Ph.D.3

ABSTRACT 

 The Geological Strength Index (GSI) is a system of 

rock mass characterization that has been developed in 

engineering rock mechanics to meet the need for reliable 

input data related to rock mass properties required as input 

for numerical analysis or closed-form solutions for designing tunnels, slopes, or foundations in rocks. The geological 

character of the rock material, together with the visual 

assessment of the mass it forms, is used as a direct input 

for the selection of parameters for predicting rock mass 

strength and deformability. This approach enables a rock 

mass to be considered as a mechanical continuum without 

losing the influence that geology has on its mechanical 

properties. It also provides a field method for characterizing difficult-to-describe rock masses. Recommendations on the use of GSI are given and, in addition, cases 

where the GSI is not applicable are discussed. 

INTRODUCTION 

 A few decades ago, the tools for designing tunnels 

started to change. Numerical methods were being developed that offered the promise for much more detailed 

analysis of difficult underground excavation problems. 

 Numerical tools available today allow the tunnel 

designer to analyze progressive failure processes and the 

sequentially installed reinforcement and support necessary 

to maintain the stability of the advancing tunnel until the 

final reinforcing or supporting structure can be installed. 

However, these numerical tools require reliable input 

information on the strength and deformation characteristics 

of the rock mass surrounding the tunnel. As it is practically 

impossible to determine this information by direct in situ 

testing (except for back analysis of already constructed 

tunnels), there was an increased need for estimating the 

rock mass properties from the intact rock properties and 

the characteristics of the discontinuities in the rock mass. 

This resulted in the development of the rock mass failure 

criterion by Hoek and Brown [1980]. A brief history of the 

development of the Hoek-Brown criterion is to be published in the first issue of a new international journal 

entitled Soils and Rocks [Hoek and Marinos, in press]. 

 The present paper is an update and extension of the 

paper by Marinos et al. [2005]. 

THE GEOLOGICAL STRENGTH INDEX (GSI) 

 Hoek and Brown recognized that a rock mass failure 

criterion would have no practical value unless it could be 

related to geological observations that could be made 

quickly and easily by an engineering geologist or geologist 

in the field. They considered developing a new classification system during the evolution of the criterion in the late 

1970s, but they soon gave up the idea and settled for the 

already published RMR system. It was appreciated that the 

RMR system (and the Q-system) [Bieniawski 1973; Barton 

et al. 1974] were developed for the estimation of underground excavation and support and that they included 

parameters that are not required for estimating rock mass 

properties. The groundwater and structural orientation 

parameters in RMR and the groundwater and stress parameters in Q are dealt with explicitly in effective stress 

numerical analyses, and the incorporation of these parameters into the rock mass property estimate results is 

inappropriate. Thus, it was recommended that only the first 

four parameters of the RMR system (intact rock strength, 

RQD rating, joint spacing, and joint conditions) should be 

used for the estimation of rock mass properties if this 

system had to be used. 

 After several years of use, it became obvious that the 

RMR system was difficult to apply to rock masses that are 

of very poor quality. The relationship between RMR and 

the constants “m” and “s” of the Hoek-Brown failure criterion begins to break down for severely fractured and 

weak rock masses. 

 Additionally, since RQD in most of the weak rock 

masses is essentially zero, it became necessary to consider 

an alternative classification system. The required system 

would place greater emphasis on basic geological observations of rock mass characteristics; reflect the material, its 

structure, and its geological history; and would be developed specifically for the estimation of rock mass properties 

rather than for tunnel reinforcement and support. This new 

classification, now called GSI, started life in Toronto, 

Canada, with engineering geology input from David Wood 

[Hoek et al. 1992]. The index and its use for the HoekBrown failure criterion was further developed by Hoek 

 

 1Professor, National Technical University of Athens, School of 

Civil Engineering, Athens, Greece. 

 2

Research assistant, National Technical University of Athens, 

School of Civil Engineering, Athens, Greece. 

 2Consultant, Vancouver, British Columbia, Canada.
88 

[1994] and presented in Hoek et al. [1995] and Hoek and 

Brown [1997], but it was still a hard-rock system roughly 

equivalent to RMR. Since 1998, Evert Hoek and Paul 

Marinos, dealing with incredibly difficult materials 

encountered in tunneling in Greece, developed the GSI 

system to its present form to include poor-quality rock 

masses (Figure 1) [Hoek et al. 1998; Marinos and Hoek 

2000, 2001]. Today, GSI continues to evolve as the principal vehicle for geological data input for the Hoek-Brown 

criterion. 

FUNCTIONS OF THE 

GEOLOGICAL STRENGTH INDEX 

 The heart of the GSI classification is a careful engineering geology description of the rock mass, which is 

essentially qualitative, because it was believed that numbers on joints were largely meaningless for weak and complex rock masses. Note that the GSI system was never 

intended as a replacement for RMR or Q, as it has no rock 

mass reinforcement or support design capability. GSI 

alone is not a tunnel design tool; its only function is the 

estimation of rock mass properties. It is intimately linked 

with the intact rock strength and should never be used 

independently of this parameter. 

 This index is based on an assessment of the lithology, 

structure, and condition of discontinuity surfaces in the 

rock mass, and it is estimated from visual examination of 

the rock mass exposed in outcrops, in surface excavations 

such as road cuts, and in tunnel faces and borehole cores. 

The GSI, by combining the two fundamental parameters of 

the geological process—the blockiness of the mass and 

the conditions of discontinuities—respects the main 

geological constraints that govern a formation. It is thus a 

geologically sound index that is simple to apply in the 

field. 

 Note that attempts to “quantify” the GSI classification 

to satisfy the perception that “engineers are happier with 

numbers” [Cai et al. 2004; Sonmez and Ulusay 1999] are 

interesting, but have to be applied with caution in order not 

to lose the geologic logic of the GSI system. The quantification processes used are related to the frequency and 

orientation of discontinuities and are limited to rock 

masses in which these numbers can easily be measured. 

These quantifications do not work well in tectonically disturbed rock masses in which the structural fabric has been 

destroyed. In such rock masses, the authors recommend 

the use of the original qualitative approach based on careful visual observations. Thus, the “quantification” system 

is only valid in the range of, say, 35 < GSI < 75, when the 

rock mass behavior depends on sliding and rotation of 

intact rock pieces, and where the spacing and condition of 

discontinuities that separate these pieces and not the intact 

rock strength control the behavior. When the intact rock 

pieces themselves can fail, then the quantification is no 

longer valid. 

 Once a GSI “number” has been decided upon, this 

number is entered into a set of empirically developed 

equations to estimate the rock mass properties that can 

then be used as input into some form of numerical analysis 

or closed-form solution. The index is used in conjunction 

with appropriate values for the unconfined compressive 

strength (UCS) of the intact rock, σci, and the petrographic 

constant, mi, to calculate the mechanical properties of a 

rock mass, in particular the compressive strength of the 

rock mass (σcm) and its deformation modulus (E). Updated 

values of mi can be found in Marinos and Hoek [2000] or 

in the RocLab program [Rocscience, Inc. 2007]. Basic 

procedures are explained by Hoek and Brown [1997], but a 

refinement of the empirical equations and the relationship 

between the Hoek-Brown and Mohr-Coulomb criteria have 

been addressed by Hoek et al. [2002] for appropriate 

ranges of stress encountered in tunnels and slopes. Hoek 

and Diederichs [2006] recently presented new equations 

for estimating rock mass deformation modulus incorporating measured or estimated intact modulus. 

 Figure 1.—General chart for GSI estimates from geological observations.
89 

SUGGESTIONS FOR USING GSI 

 After more than a dozen of years of application of the 

GSI and its variations for the characterization of the rock 

mass, this paper attempts to answer questions that have 

been raised by users about the appropriate selection of the 

index for various rock masses under various conditions. 

When Not to Use GSI 

 The GSI classification system is based on the 

assumption that the rock mass contains a sufficient number 

of “randomly” oriented discontinuities such that it behaves 

as a homogeneous isotropic mass. In other words, the 

behavior of the rock mass is independent of the direction 

of the applied loads. Therefore, it is clear that the GSI 

system should not be applied to those rock masses in 

which there is a clearly defined dominant structural orientation or structurally dependent gravitational instability. 

However, the Hoek-Brown criterion and the GSI chart can 

be applied with caution if the failure of such rock masses is 

not controlled by such anisotropy (e.g., in the case of a 

slope when the dominant structural discontinuity set dips 

into the slope and failure occurs through the rock mass). 

For rock masses with a structure such as that shown in the 

bottom row of the GSI chart (Figure 1), anisotropy is not a 

major issue, as the difference in the strength of the rock 

and that of the discontinuities within it is often small. 

Anisotropy in cases of stress-dependent instability is discussed later in this paper. 

 It is also inappropriate to assign GSI values to excavated faces in strong hard rock with a few discontinuities 

spaced at distances of similar magnitude to the dimensions 

of the tunnel or slope under consideration. In such cases, 

the stability of the tunnel or slope will be controlled by the 

three-dimensional geometry of the intersecting discontinuities and the free faces created by the excavation. 

Obviously, the GSI classification does not apply to such 

cases. 

Geological Description in the GSI Chart 

 In dealing with specific rock masses, it is suggested 

that the selection of the appropriate case in the GSI chart 

should not be limited to the visual similarity with the 

sketches of the structure of the rock mass as they appear in 

the charts. The associated descriptions must also be read 

carefully, so that the most suitable structure is chosen. The 

most appropriate case may well lie at some intermediate 

point between the limited number of sketches or descriptions included in the charts. 

Projection of GSI Values Into the Ground 

 Outcrops, excavated slopes, tunnel faces, and borehole 

cores are the most common sources of information for 

estimating the GSI value of a rock mass. How should the 

numbers estimated from these sources be projected or 

extrapolated into the rock mass behind a slope or ahead of 

a tunnel? 

 Outcrops are an extremely valuable source of data in 

the initial stages of a project, but they suffer from the 

disadvantage that surface relaxation, weathering, and/or 

alteration may have significantly influenced the appearance of the rock mass components. This disadvantage can 

be overcome by trial trenches but, unless these are 

machine-excavated to considerable depth, there is no guarantee that the effects of deep weathering will have been 

eliminated. Judgment is therefore required in order to 

allow for these weathering and alteration effects in assessing the most probable GSI value at the depth of the 

proposed excavation. 

 Excavated slope and tunnel faces are probably the 

most reliable source of information for GSI estimates, provided that these faces are reasonably close to and in the 

same rock mass as the excavation under investigation. 

 Borehole cores are the best source of data at depth, but 

it must be recognized that it is necessary to extrapolate the 

one-dimensional information provided by the core to the 

three-dimensional in situ rock mass. However, this is a 

problem common to all borehole investigations, and most 

experienced engineering geologists are comfortable with 

this extrapolation process. 

 For stability analysis of a slope, the evaluation is based 

on the rock mass through which it is anticipated that a 

potential failure plane could pass. The estimation of GSI 

values in these cases requires considerable judgment, 

particularly when the failure plane can pass through several zones of different quality. Mean values may not be 

appropriate in this case. 

 For tunnels, the index should be assessed for the 

volume of rock involved in carrying loads, e.g., for about 

one diameter around the tunnel in the case of tunnel 

behavior or more locally in the case of a structure such as 

the elephant foot of a steel arch. In more general terms, the 

numerical models may include the variability of GSI 

values over the tunnel in “layers.” Drs. Edmund Medley 

and Dimitrios Zekkos are currently considering developing 

a function defining the variation of GSI with depth for a 

specific case. 

Anisotropy 

 As discussed above, the Hoek-Brown criterion (and 

other similar criteria) assumes that the rock mass behaves 

isotropically and that failure does not follow a preferential 

direction imposed by the orientation of a specific discontinuity or a combination of two or three discontinuities. In 

these cases, the use of GSI to represent the whole rock 

mass is meaningless, as the failure is governed by the shear 

strength of these discontinuities and not of the rock mass. 
90 

However, cases where the criterion and the GSI chart can 

reasonably be used have been discussed above. 

 However, in a numerical analysis involving a single 

well-defined discontinuity such as a shear zone or fault, it 

is sometimes appropriate to apply the Hoek-Brown criterion to the overall rock mass and to superimpose the 

discontinuity as a significantly weaker element. In this 

case, the GSI value assigned to the rock mass should 

ignore the single major discontinuity. The properties of 

this discontinuity may fit the lower portion of the GSI 

chart or they may require a different approach, such as 

laboratory shear testing of soft clay fillings. 

 In general terms, when confinement is present, the 

stress-dependent regime is controlled by the anisotropy of 

the rock masses (e.g., slates, phyllites, etc.). A discussion 

of anisotropy rock mass behavior in tunneling beyond the 

commonly used classification systems is presented by 

Button et al. [2004]. In these cases, it would be necessary 

to develop an orientation-dependent GSI. This is a recent 

idea to try to simplify the treatment of anisotropic 

problems. However, in view of the potential for 

complicating the understanding of GSI, an alternative 

approach may be to use an orientation-dependent UCS. 

This is more logical from a physical point of view and, 

being almost completely interchangeable with GSI from a 

mathematical point of view, should work just as well. The 

GSI value in this case would be high, and the rock mass 

strength would be determined by the orientation-dependent 

σci value. 

 With the capacity of present-day microcomputers, it is 

also possible to model anisotropy by superimposing a large 

number of discontinuities on an isotropic rock mass which 

is assigned a higher GSI value. These discontinuities can 

be assigned shear strength and stiffness characteristics that 

simulate the properties of the schistosity, bedding planes, 

and joints in the rock mass. Such models have been found 

to work well and give results that compare well with more 

traditional anisotropic solutions. 

Aperture of Discontinuities 

 The strength and deformation characteristics of a rock 

mass are dependent on the interlocking of the individual 

pieces of intact rock that make up the mass. Obviously, the 

aperture of the discontinuities that separate these individual pieces has an important influence on the rock mass 

properties. 

 There is no specific reference to the aperture of the 

discontinuities in the GSI chart, but a “disturbance factor” 

D has been provided in the most recent version of the 

Hoek-Brown failure criterion [Hoek et al. 2002] and is also 

used in the Hoek and Diederichs [2006] approach for 

estimating deformation modulus. This factor ranges from 

D=0 for undisturbed rock masses, such as those excavated 

by a tunnel boring machine, to D=1 for extremely disturbed rock masses, such as open-pit mine slopes that have 

been subjected to very heavy production blasting. The factor allows for the disruption of the interlocking of the 

individual rock pieces as a result of opening of the discontinuities. The influence of this factor is of great significance to the calculated factors of safety. 

 At this stage, there is relatively little experience in the 

use of this factor, and it may be necessary to adjust its 

participation in the equations as more field evidence is 

accumulated. However, the experience so far suggests that 

this factor does provide a reasonable estimate of the 

influence of damage due to stress relaxation or blasting of 

excavated rock faces. Note that this damage decreases with 

depth into the rock mass and, in numerical modeling, it is 

generally appropriate to simulate this decrease by dividing 

the rock mass into a number of zones with decreasing 

values of D being applied to successive zones as the distance from the face increases. On the other hand, in very 

large open-pit mine slopes in which blasts can involve 

many tons of explosives, blast damage has been observed 

up to 100 m or more behind the excavated slope face. This 

would be a case for D=1 and there is a very large reduction 

in shear strength associated with damage. Hoek and 

Karzulovic [2000] have given some guidance on the extent 

of this damage and its impact on rock mass properties. For 

civil engineering slopes or foundation excavation, the blast 

damage is much more limited in both severity and extent, 

and the value of D is generally low. 

 This problem becomes less significant in weak and 

tectonically disturbed rock masses, as excavation is generally carried out by “gentle” mechanical means and the 

amount of surface damage is negligible compared to that 

which already exists in the rock mass. 

Geological Strength Index at Great Depth 

 In hard rock at great depth (e.g., 1,000 m or more) the 

rock mass structure is so tight that the mass behavior 

approaches that of the intact rock. In this case, the GSI 

value approaches 100 and the application of the GSI system is no longer meaningful. 

 The failure process that controls the stability of underground excavations under these conditions is dominated by 

brittle fracture initiation and propagation, which leads to 

spalling, slabbing, and, in extreme cases, rock bursts. 

Considerable research effort has been devoted to the study 

of these brittle fracture processes, and Diederichs et al. 

[2004] provide a useful summary of this work. 

 When tectonic disturbance is important and persists 

with depth, these comments do not apply and the GSI 

charts may be applicable, but should be used with 

caution. 

Discontinuities With Filling Materials 

 The GSI charts can be used to estimate the characteristics of rock masses with discontinuities with filling 
91 

materials using the descriptions in the columns for “poor” 

or “very poor” condition of discontinuities. If the filling 

material is systematic and thick (e.g., more than a few 

centimeters) or shear zones are present with clayey material, then the use of the GSI chart for heterogeneous rock 

masses (discussed below) is recommended. 

Influence of Water 

 The shear strength of the rock mass is reduced by the 

presence of water in the discontinuities or filling materials 

when these are prone to deterioration as a result of changes 

in moisture content. This is particularly valid in the “fair” 

to “very poor” categories of discontinuities, where a shift 

to the right may be made for wet conditions. The shift to 

the right is more substantial in the low-quality range of 

rock mass (last rows and columns of the chart). 

 Water pressure is dealt with by effective stress analysis 

in design, and it is independent of the GSI characterization 

of the rock mass. 

Weathered Rock Masses 

 The GSI values for weathered rock masses are shifted 

to the right of those of the same rock masses when these 

are unweathered. If the weathering has penetrated into the 

intact rock pieces that make up the mass (e.g., in 

weathered granites), then the constant mi and the 

unconfined strength of the σci of the Hoek-Brown criterion 

must also be reduced. If the weathering has penetrated the 

rock to the extent that the discontinuities and the structure 

have been lost, then the rock mass must be assessed as a 

soil and the GSI system no longer applies. 

Heterogeneous and Lithologically Varied or 

Complex Rock Masses 

 GSI has been extended to accommodate the most 

variable of rock masses, including extremely poor quality 

sheared rock masses of weak schistose materials (such as 

siltstones, clay shales, or phyllites) often interbedded with 

strong rock (such as sandstones, limestones, or quartzites). 

A GSI chart for flysch, a typical heterogeneous lithological 

Figure 2.—Geological Strength Index for heterogeneous rocks such as flysch.
92 

formation with tectonic disturbance, was published by 

Marinos and Hoek [2001]. This chart has recently been 

revised and is reproduced in Figure 2. This revision is 

based on recent experience from a number of tunnels constructed in Greece. It includes cases of siltstones with little 

disturbance and a variety of cases of siltstones alternating 

with good rock (e.g., sandstone). 

 For lithologically varied but tectonically undisturbed 

rock masses, such as the molasses, a new GSI chart was 

presented by Hoek et al. [2005] (Figure 3). For example, 

molasse consists of a series of tectonically undisturbed 

sediments of sandstones, conglomerates, siltstones, and 

marls produced by the erosion of mountain ranges after the 

final phase of an orogeny. The molasses behave quite 

differently from flysch, which has the same composition 

but was tectonically disturbed during the orogeny. They 

behave as continuous rock masses when they are confined 

at depth, and the bedding planes do not appear as clearly 

defined discontinuity surfaces. Close to the surface the 

layering of the formations is discernible, and only then 

similarities may exist with the structure of some types of 

flysch. 

 In design, the intact rock properties σci and the mi must 

also be considered. A “weighted average” of the properties 

of the strong and weak layers should be used. 

 Marinos et al. [2006] recently presented a quantitative 

description, using GSI, for rock masses within an ophiolitic complex. Included are types with large variability 

due to their range of petrographic types, their tectonic 

deformation, and their alternation (Figure 4). The structure 

of the various masses include types from massive strong to 

sheared weak, while the conditions of discontinuities are, 

in most cases, fair to very poor due to the fact that they are 

affected by serpentinization and shearing. This description 

allows the estimation of the range of properties and the 

understanding of the dramatic change in tunneling, from 

stable conditions to severe squeezing within the same formation at the same depth. 

 Figure 4.—Ranges of GSI for various qualities of 

peridotite-serpentinite rock masses in ophiolites. 

 Figure 3.—Chart for confined molasse (mainly applicable for tunnels). 
93 

Rocks of Low Strength of Recent Age 

 When rocks such as marls, claystones, siltstones, and 

weak sandstones are developed in geologically stable conditions in a posttectonic environment, they usually present 

a simple structure with no or few discontinuities. When 

these rocks form continuous masses with no discontinuities, the rock mass can be treated as intact with engineering parameters given directly by laboratory testing. In such 

cases, the GSI classification is not applicable. 

 In cases where discontinuities are present, the use of 

the GSI chart for “blocky” or “massive” rock masses 

(Figure 1) may be applicable. The discontinuities in such 

weak rocks, although they are limited in number, cannot be 

better than “fair” (usually “fair” or “poor”); thus, the GSI 

values tend to be in the range of 45–65. In these cases, the 

low strength of the rock mass results from low intact 

strength σci. 

PRECISION OF THE 

GSI CLASSIFICATION SYSTEM 

 The “qualitative” GSI system works well for engineering geologists since it is consistent with their experience in 

describing rocks and rock masses during logging and 

mapping. In some cases, engineers tend to be uncomfortable with the system because it does not contain parameters that can be measured in order to improve the 

precision of the estimated GSI value. 

 The authors do not share this concern, as they believe 

that it is not meaningful to attempt to assign a precise 

number to the GSI value for a typical rock mass. In all but 

the very simplest of cases, GSI is best described by 

assigning it a range of values. For analytical purposes, this 

range may be defined by a normal distribution with mean 

and standard deviation values assigned on the basis of 

common sense. GSI, with its qualitative principles of 

geological descriptions, is not restrained by the absence of 

good exposures or the limitations of quantitative core 

descriptions. 

 Although GSI is a totally independent system, in the 

earlier period of its application it was proposed that 

correlation of “adjusted” RMR and Q values with GSI be 

used for providing the necessary input for the HoekBrown criterion. Although this procedure may work 

with the better-quality rock masses, it is unreliable in the 

range of weak (e.g., GSI<35), very weak, and heterogeneous rock masses, where these correlations are not 

recommended. 

 Whenever GSI is used, a direct assessment, based on 

the principles and charts presented above, is recommended. Fortunately, most GSI users have no difficulty in 

thinking of it as a totally independent system. However, in 

cases of comparisons or back analysis where other classification systems have been used, some kind of correlation 

with these other systems is needed. In such cases, it may 

be useful to consult the paper by Tzamos and Sofianos 

[in press]. The four classification-characterization systems 

(RMR, Q, RMi [Palmström 1996], and GSI) were investigated, and all systems ratings are grouped in a common 

fabric index chart. The reader is reminded not to lose sight 

of the real geological world in considering such 

correlations. 

GSI AND CONTRACT DOCUMENTS 

 One of the most important contractual problems in 

rock construction and particularly in tunneling is the issue 

of “changed ground conditions.” There are invariably 

arguments between the owner and the contractor on the 

nature of the ground specified in the contract and that 

actually encountered during construction. In order to overcome this problem, there has been a tendency to specify 

the anticipated conditions in terms of tunneling classifications. More recently, some contracts have used the GSI 

classification for this purpose, and the authors are strongly 

opposed to this trend. 

 As discussed earlier in this paper, GSI was developed 

solely for the purpose of estimating rock mass properties. 

Therefore, GSI is only one element in a tunnel design 

process and cannot be used, on its own, to specify 

tunneling conditions. It must be associated with the intact 

rock strength, the petrographic constant mi, and all of the 

characteristics (such as anisotropy) of the rock mass that 

may impose a different mode of failure than that of a 

stressed homogeneous isotropic rock mass. 

 The use of any classification system to specify 

anticipated tunneling conditions is always a problem as 

these systems are open to a variety of interpretations, 

depending on the experience and level of conservatism of 

the observer. This can result in significant “changes” in 

excavation or support type and can have important financial consequences. 

 The geotechnical baseline report [Essex 1997] was 

introduced in an attempt to overcome some of the difficulties and has attracted an increasing amount of international attention in tunneling. 

CONCLUSIONS 

 Rock mass characterization has an important role, not 

only to define a conceptual model of the site geology, but 

also for the quantification needed for analyses “to ensure 

that the idealization (for modeling) does not misinterpret 

actuality” [Knill 2003]. If it is carried out in conjunction 

with numerical modeling, rock mass characterization presents the prospect of a far better understanding of the 

mechanics of rock mass behavior [Chandler et al. 2004]. 

The GSI system has considerable potential for use in rock 

engineering because it permits many characteristics of a 

rock mass to be quantified, thereby enhancing geological 

logic and reducing engineering uncertainty. Its use allows 

the influence of variables, which make up a rock mass, to 

be assessed and thus the behavior of rock masses to be 

explained more clearly. One of the advantages of the GSI 

is that the geological reasoning it embodies allows adjust-
94 

ments of its ratings to cover a wide range of rock masses 

and conditions, but it also allows us to understand the 

limits of its application. 

ACKNOWLEDGMENTS 

 The paper is the result of a project cofunded by the 

European Social Fund (75%) and Greek National 

Resources (25%) – Operational Program for Educational 

and Vocational Training II (EPEAEK II) and particularly 

the Program PYTHAGORAS. 

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95

DEVELOPMENT AND APPLICATION OF THE COAL MINE ROOF RATING (CMRR)

By Christopher Mark, Ph.D., P.E.,1

 and Gregory M. Molinda2

________________________________________________________________________________ _ 

ABSTRACT 

 The Coal Mine Roof Rating (CMRR) was developed 

10 years ago to fill the gap between geologic characterization and engineering design. It combines many years of 

geologic studies in underground coal mines with worldwide experience with rock mass classification systems. 

Like other classification systems, the CMRR begins with 

the premise that the structural competence of mine roof 

rock is determined mainly by the discontinuities that 

weaken the rock fabric. However, the CMRR is specifically designed for bedded coal measure rock. Since its 

introduction, the CMRR has been incorporated into many 

aspects of mine planning, including longwall pillar design, 

roof support selection, feasibility studies, extended-cut 

evaluation, and others. It has also become truly international, with involvement in mine designs and funded 

research projects in South Africa, Canada, and Australia. 

This paper discusses the sources used in developing the 

CMRR, describes the CMRR data collection and calculation procedures, and briefly presents a number of practical 

mining applications in which the CMRR has played a 

prominent role. 

INTRODUCTION 

 Roof falls continue to be one of the greatest hazards 

faced by underground coal miners. In 2006, there were 

7 fatalities from roof falls and nearly 500 rock fall injuries 

in the United States. In addition, more than 1,300 major 

roof collapses were reported to the Mine Safety and Health 

Administration. These roof falls can threaten miners, damage equipment, disrupt ventilation, and block critical emergency escape routes. 

 One reason roof falls have proven so difficult to 

eradicate is that mines are not built of manmade materials 

like steel or concrete, but rather of rock, just as nature 

made it. The structural integrity of a coal mine roof is 

greatly affected by natural weaknesses, including bedding 

planes, fractures, and small faults. The engineering properties of rock cannot be specified in advance with adequate 

precision and can vary widely from mine to mine and even 

within individual mines. 

 Engineers require quantitative data on the strength of 

rock masses for design. Traditional geologic reports 

contain valuable descriptive information, but few engineering properties. Laboratory tests, on the other hand, are 

inadequate because the strength of a small specimen is 

only indirectly related to the strength of the rock mass. 

ROCK MASS CLASSIFICATION 

 Rock mass classification schemes were developed 

to address these concerns. The most widely known systems, including Deere’s Rock Quality Designation (RQD), 

Bieniawski’s Rock Mass Rating (RMR), and Barton’s 

Q-system, have been used extensively throughout the 

world [Deere and Miller 1966; Bieniawski 1973; Barton 

et al. 1974]. Rock mass classifications have been successful [Bieniawski 1988] because they— 

• Provide a methodology for characterizing rock mass 

strength using simple measurements; 

• Allow geologic information to be converted into 

quantitative engineering data; 

• Enable better communication between geologists and 

engineers; and 

• Make it possible to compare ground control experiences between sites, even when the geologic conditions are very different. 

 This last point highlights an extremely powerful application of rock mass classification systems, which is their 

use in empirical design methods. Empirical designs are 

based on mine experience—on the real-world successes 

and failures of actual ground control designs. By collecting 

a large number of case histories into a single database and 

subjecting them to statistical analysis, reliable and robust 

guidelines for design can be developed. A key advantage 

of empirical techniques is that it is not necessary to obtain 

a complete understanding of the mechanics to arrive at a 

reasonable solution. Rock mass classifications play an 

essential role in empirical design because they allow the 

overwhelming variety of geologic variables to be reduced 

to a single, meaningful, and repeatable parameter. 

 Unfortunately, the standard rock mass classification 

systems are not readily applicable to coal mining because— 

• They tend to focus on the properties of joints, 

whereas bedding is generally the most significant 

discontinuity affecting coal mine roof. 

 

 1Principal research mining engineer. 

 2

Research geologist. 

 Pittsburgh Research Laboratory, National Institute for Occupational Safety and Health, Pittsburgh, PA. 
96

• They rate just one rock unit at a time, whereas coal 

mine roof often consists of several layers bound 

together by roof bolts. 

In addition, the dimensions and stability requirements of 

tunnels are often very different from those of mines. 

COAL MINE GROUND CONTROL 

 The Coal Mine Roof Rating (CMRR) was developed 

more than 10 years ago to meet the needs of mine planners 

for a simple, repeatable, and meaningful classification 

system [Molinda and Mark 1994]. It employs the familiar 

format of Bieniawski’s RMR, summing the individual 

ratings to obtain a final CMRR on a 0–100 scale. It is also 

designed so that the CMRR/unsupported span/standup 

time relationship is roughly comparable to the one determined for the RMR. 

 In determining the specific rock mass attributes and 

weightings to use, the CMRR built upon the rich vein of 

experience with coal mine ground control during the past 

30 years. These sources can be divided into two groups. 

The first are papers describing specific geologic features, 

such as faults, clay veins, sandstone channels, kettlebottoms, and others. A summary of this work was reported 

by Molinda [2003]. The second group, which includes 

efforts to generalize results for specific mines, regions, or 

countries, was more directly relevant to the development 

of the CMRR. In effect, these papers describe rock mass 

classification systems, although most are qualitative rather 

than quantitative. Table 1 provides a list of the coal mine 

roof classification systems consulted in the development of 

the CMRR, along with the significant geologic factors that 

they identified as being important to ground control. 

Following is a discussion of some of these factors and the 

issues involved with incorporating them into the CMRR.

 Bedding: Bedding was the factor that was most 

consistently cited as causing roof problems in coal mines. 

The two most common examples were weak laminations 

in shale and thinly interbedded sandstone and shale 

(stackrock). In both examples, it is not just that the bedding planes are closely spaced, but also that the bedding 

surfaces are very weak. Indeed, several authors included 

“massive shale” as one of the more stable rock types 

[Moebs and Ferm 1982]. 

 The issue of bedding (or grain alignment) is further 

complicated because some shales may appear massive, 

particularly to untrained eyes, but are actually highly 

laminated. The CMRR therefore emphasizes testing of the 

rock material to determine bedding plane strength even 

when the bedding is not visible. The approach is similar to 

that proposed by Buddery and Oldroyd [1992] and used 

successfully in South African coal mines. 

 Strong Bed: A problem unique to horizontally layered sedimentary rocks is that the roof structure often 

consists of several layers with different engineering 

characteristics. In developing the CMRR, two key questions had to be answered: 

1. How far up into the roof should the evaluation 

extend? 

2. How should the different layers be combined into a 

single rating? Should they be averaged together, or 

should the weakest or strongest layers be given 

precedence? 

 Few answers were available in the literature. Buddery 

and Oldroyd [1992] evaluated the first 2 m of roof, but 

weighted the layers nearest the roof line more heavily. 

Several authors seemed to imply that a weak layer can be 

very important by their emphasis on rider coal seams 

[Karmis and Kane 1984; Stingelin et al. 1979; Miller 1984]. 

 The CMRR started with the insight that the roof bolt 

length largely determines the thickness of the mine roof 

structure. All coal mine roof in the United States must be 

bolted, and the bolts bind the different layers together. 

Generally, the layers above the bolts have much less influence than the units that are penetrated by the roof bolts. 

 Moreover, experience in many U.S. coalfields has 

clearly established that roof stability is greatly enhanced 

when the roof bolts anchor in a strong layer. This effect is 

most evident in the Illinois Basin, where roof falls are 

almost unknown when the bolts anchor in a limestone that 

is at least 0.6 m thick [Kester and Chugh 1980; Schaffer 

1985; Damberger et al. 1980]. The strong bed effect has 

also been recognized in Alabama [Martin et al. 1988] and 

central Appalachia [Hylbert 1978]. Indeed, even the Code 

of Federal Regulations implies a strong bed effect when it 

states at 30 CFR 75.204(f)(1) that “roof bolts that provide 

support by suspending the roof from overlying stronger 

strata shall be long enough to anchor at least 12 inches into 

the stronger strata.” 

 Moisture Sensitivity: Moisture sensitivity is another 

factor that has been ignored by traditional rock mass 

classification systems, but is extremely important to coal 

mine ground control. Two roof shales may initially have 

very similar properties, but one may be essentially impervious to moisture while the other completely disintegrates 

when exposed to groundwater or even humid mine air 

(Figure 1). 

 The presence of moisture-sensitive mudrocks may be 

just a nuisance, or it can severely damage the roof by 

reducing rock strength, generating swelling pressures, or 

compromising support effectiveness by causing sloughing 

around roof bolt plates. While the Slake Durability Test 

(SDT) has been widely used to evaluate moisture sensitivity [Hoek 1977], the CMRR employs a modified version 

of the simpler immersion test described by Sickler [1986]. 
Table 1.—Rock mass classification systems for coal mines 

Author Location Rock strength Bedding Strong bed Moisture sensitivity Sandstone channels Slickensides Minor structures No. of beds Ground- water Lineaments/ faults Rider coals Joints 

Buddery and Oldroyd [1992];  Lattila et al. [2002] ........................ South Africa x x Damberger et al. [1980]................... Illinois Basin x x x Ealy et al. [1979].............................. S. West Virginia x x Hylbert [1978] .................................. E. Kentucky x x x x Karmis and Kane [1984].................. Virginia x x x x x x Kester and Chugh [1980] ................ Illinois Basin x x x x x Martin et al. [1988]........................... Alabama x x x x Milici et al. [1982]............................. Virginia x x x Miller [1984] ..................................... Central Appalachia x x x x x x Moebs and Ferm [1982];  Ferm et al. [1978] ......................... 

Virginia, 

S. West Virginia x x x x 

Moebs and Stateham [1985] .......... United States x x x x x x Newman and Bieniawski [1986] ...... United States x x x x x x Schaffer [1985] ................................ Illinois Basin x x Sinha and Venkateswarlu [1986];  Venkateswarlu et al. [1989] .......... India x x x x x x x x Stingelin et al. [1979] ...................... N. Appalachia x x x Zhou et al. [1988]............................. United States x x x x x Coal Mine Roof Rating (CMRR) ...... United States x x x x x x x x x 

97 
98 

 Slickensides and Other Discontinuities: While bedding is generally the most significant weakness in the 

fabric of coal measure rocks, often some other type of discontinuity is present. Slickensides, which are small-scale 

(<2-m) fault surfaces of highly aligned clay minerals 

distinguished by glassy, grooved surfaces, are frequently 

cited as greatly reducing the competence of coal measure 

mudrocks (for example, see Moebs and Stateham [1985]). 

Jointing is encountered in Virginia [Karmis and Kane 

1984] and occasionally elsewhere. In sandstones, coal 

spars and crossbeds can be significant. The original RMR 

rates only the most significant discontinuity set and largely 

ignores the others. The CMRR contains a “multiple discontinuity adjustment” so that the weakening effects of 

slickensides and other discontinuities can be explicitly 

included. 

 Large-scale Features: Large-scale features include 

sandstone channel margins, lineaments, faults, and some 

medium-scale features such as seam rolls and clay veins. 

These types of features are not included directly in the 

CMRR, although in some cases one CMRR value can be 

determined for “typical conditions” and another for 

“fracture zones” or “sandstone channel margin areas,” and 

these can then be plotted on hazard maps. However, the 

CMRR is not designed to rate conditions impacted by a 

major throughgoing discontinuity such as a fault. Such 

features normally require specially designed support 

systems. 

DATA COLLECTION AND CALCULATION 

OF THE CMRR 

 The data required for the CMRR can be determined 

either from underground exposures, such as roof falls and 

overcasts, or from exploratory drill core. In either case, the 

main parameters measured are the— 

• Uniaxial compressive strength (UCS) of the intact 

rock; 

• Intensity (spacing and persistence) of bedding and 

other discontinuities; 

• Shear strength (cohesion and roughness) of bedding 

and other discontinuities; 

• Moisture sensitivity of the rock; and 

• Presence of a strong bed in the bolted interval. 

Other secondary factors include the number of layers, the 

presence of groundwater, and surcharge from overlying 

weak beds. 

 The CMRR is calculated in a two-step process. First, 

the mine roof is divided into structural units, and Unit 

Ratings are determined for each. A structural unit generally contains one lithologic layer, but several rock layers 

may be lumped together if their engineering properties are 

similar. In the second step, the CMRR is determined by 

averaging all the Unit Ratings within the bolted interval 

(with the contribution of each unit weighted by its thickness) and applying appropriate adjustment factors. This 

second step is the same regardless of whether the Unit 

Ratings were from data collected underground or from 

core. Figure 2 illustrates the process. 

 The procedures for gathering data and calculating the 

CMRR from underground exposures have remained essentially unchanged since they were first proposed in 1993. 

The underground data sheet is shown in Figure 3. Procedures to determine Unit Ratings from drill core have now 

been streamlined and updated based on new research 

[Mark et al. 2002]. Calculating the CMRR has been greatly 

simplified by the development of a CMRR computer program that can be obtained free of charge. 

 The sections below discuss each of the input parameters used in the CMRR. 

Figure 1.—Effect of water on a moisture-sensitive shale. 

Figure 2.—Flowchart for the CMRR. 
CMRR 

DATE MINE LOCATION PAGE OF CMRR 

TYPE OF EXPOSURE NAME 

UNIT UNIT DISCONTINUITIES 

Unit 

No.

Unit 

Thickness

Strip 

Log Description Strength Moisture Sensitivity Disco. I.D. Description Cohesion Roughness Spacing Persistence Lateral/Vert

A.

B. 3

C.

CONTACT

A.

B. 2

C.

CONTACT

A.

B. 1C.

1 Rebounds Not Sensitive 1 Strong (>7)** Jagged >1.8 m 0 - 0.9 m2 Pits Slightly Sensitive 2 Moderate (4-7) Wavy 0.6 - 1.8 m 0.9 - 3 m3 Dents Moderately Sensitive 3 Weak (1-3) Planar 20 - 60 cm 3 - 9 m4 Craters Severely Sensitive 4 Slickensided (0) 6 - 20 cm >9 m5 Molds 5 <6 cm

Groundwater (inflow/10 m of entry length) L/min 

(Circle one) 

 Dry 0 1 Heavy Drip 2.7 - 12.2 4  Damp 0 - 1.3 2 Flowing >13.2 5  Light Drip 1.3 - 2.7 3 

Describe condition in vicinity of fall (circle one)  1. Good 3. Heavy  2. Scaly 4. Failed 

 **Chisel blows necessary to split bedding.  COMMENTS: 

Figure 3.—Underground data sheet for the CMRR. 

99 
100

Uniaxial Compressive Strength (UCS) 

 The UCS of the rock material influences roof strength 

in several ways. First, it determines the ease with which 

new fracturing (as opposed to movement along preexisting 

discontinuities) will take place. Second, the compressive 

strength of the rock is a factor in the shear strength of 

discontinuities. Approximately one-third of the CMRR is 

determined by the compressive strength rating, which is 

approximately twice the weight given to the UCS in the 

original RMR. 

 Laboratory testing is generally considered the standard 

method of determining the UCS. Unfortunately, laboratory 

tests are expensive because the samples must be carefully 

prepared. The variability in the results is also high, with 

the standard deviation typically about one-third of the 

mean for coal measure rocks [Rusnak and Mark 2000]. 

 As an alternative, the CMRR recommends the point 

load test (PLT) for drill core. The PLT has been accepted 

in geotechnical practice for nearly 30 years [Hoek 1977]. 

An advantage of the PLT is that numerous tests can be 

performed because the procedures are simple and inexpensive. The apparatus is also inexpensive and portable. The 

International Society for Rock Mechanics has developed 

standard procedures for testing and data reduction [ISRM 

1985]. 

 Another advantage of the PLT is that both diametral 

and axial tests can be performed on core. In a diametral 

test, the load is applied parallel to bedding (Figure 4). The 

diametral test is therefore an indirect measure of the lateral 

strength, or bedding plane shear strength, and is further 

discussed later. 

 When the axial PLT is used to estimate the UCS, the 

Point Load Index (Is50) is converted using the following 

equation: 

 UCS = K (Is50) (1) 

where K is the conversion factor. A comprehensive study 

involving more than 10,000 tests of coal measure rocks 

from six states [Rusnak and Mark 2000] found that K=21 

fit the data well for the entire range of rock types and 

geographic regions (Figure 5). The study also found that 

the variability of the PLT measurements, as measured by 

the standard deviation, was no greater than for UCS tests. 

The UCS rating scale used in the CMRR program is shown 

in Figure 6. 

 Underground, the CMRR employs an indention test 

proposed by Williamson [1984] to estimate UCS. The 

exposed rock face is struck with the round end of a ball 

peen hammer, and the resulting characteristic impact 

reaction is compared to the drawings shown on the left 

side of Figure 3. It is the nature of the reaction (indentation), not its magnitude, that is important. 

 A study was conducted to compare the UCS ratings 

derived from the Ball Peen Test with the PLT. In 17 of the 

23 sites studied (or 74% of the cases), the difference 

between the two measurements was 4 points or less (Figure 7). The analysis resulted in slight changes to the 

Williamson rock strength classes, as shown in Table 2. 

Figure 4.—Diametral and axial point load tests. 

 Figure 5.—Relationship between axial PLT and UCS 

test for shale (Rusnak and Mark [2000]). 

Figure 6.—CMRR rating scale for UCS tests. 
101

Table 2.—Approximate UCS ranges from 

Ball Peen Hammer Tests 

Ball peen 

hammer class 

Williamson UCS 

range (MPa) 

CMRR UCS 

range (MPa) CMRR rating 

Molds ............... <7 <14 5 

Craters ............. 7–21 14–35 10 

Dents ............... 21–56 35–70 15 

Pits................... 56–105 70–120 22 

Rebounds ........ >105 >120 30 

Discontinuity Intensity 

 Intensity is determined by the spacing between 

bedding planes or other discontinuities and the persistence, 

or extent, of each individual discontinuity. The more 

closely spaced a set of discontinuities, the greater the 

weakening effect on the rock mass. Persistence is more 

important for discontinuities that are widely spaced. Like 

UCS, intensity accounts for about one-third of the total 

CMRR. 

 Underground, both spacing and persistence can be 

measured directly using the standard methods for rock 

mass characterization [ISRM 1982]. Table 3 shows the 

Bedding/Discontinuity Rating Scale for underground data. 

The matrix shows what point value is added for each 

combination of spacing and persistence of discontinuities. 

Table 3.—Bedding/discontinuity intensity rating table

for underground data 

Spacing 

Persistence >1.8 m 0.6–1.8 

m 

0.2–0.6 

m 

60–200 

mm 

<60 

mm 

 0–1 m ........... 35 30 24 17 9 

 1–3 m .......... 32 27 21 15 9 

 >1 m ............ 30 25 20 13 9 

 Most standard geotechnical core logging procedures 

include some measure of the natural breaks in the core. 

The two most commonly employed are the fracture 

spacing and the RQD. Fracture spacing is easily determined by counting the core breaks in a particular unit and 

then dividing by the thickness of the unit. The RQD is 

obtained by dividing combined length of core pieces that 

are greater than 4 in long by the full length of the core run. 

 Both measures have their advocates in the geotechnical community. Priest and Hudson [1976] suggested 

that the two can be related by the following formula: 

 RQD = 100 e–0.1L (0.1L+1) (2) 

where L = number of discontinuities per meter. 

 As input, the CMRR uses either the RQD or the fracture spacing. When the fracture spacing is greater than 

about 1 ft, the RQD is not very sensitive, so the fracture 

spacing is used directly. At the other extreme, when the 

core is highly broken or lost, the RQD seems to be the 

better measure. Either measure may be used in the intermediate range. 

 The program uses the equations shown in Figure 8 to 

calculate the Discontinuity Spacing Rating (DSR) of core 

from RQD or the fracture spacing. The equations were 

derived from the original CMRR rating tables. The minimum value of the DSR is 20; the maximum is 48 (see 

Figure 8). 

Shear Strength of Discontinuities 

 Bedding plane shear strength is a critical parameter for 

coal mine ground control because the most severe loading 

applied to coal mine roof is normally lateral, caused by 

horizontal stress [Mark and Barczak 2000]. Molinda and 

Mark [1996] found that the lateral strength of some shales 

are just one-sixth of their axial strength. 

 Underground, the cohesion of bedding surfaces is 

evaluated by using a 9-cm mason chisel and a hammer to 

split hand samples of rock. Weaker, less cohesive surfaces 

require fewer chisel blows to split (see Figure 3). Cohesion 

can also be estimated by observing the nature of the 

fractured wall of a roof fall. If the wall “stairsteps,” with 

most of the roof failure occurring along bedding, then the 

cohesion is probably low. On the other hand, if most of the 

5

10

15

20

25

30

5 10 15 20 25 30

UCS Rating

Ball Peen Rating

Figure 7.—Comparison between UCS and Ball Peen Tests. 

Figure 8.—CMRR rating scale for fracture spacing or RQD. 
102

failure surfaces cut across bedding, then the strength of the 

bedding is most likely equal to or greater than that of the 

intact rock. Slickensided surfaces are already planes of 

failure and receive the minimum rating. 

 The roughness along a discontinuity surface is the 

other component of the surface’s shear strength. In the 

CMRR, roughness of a surface is estimated visually and 

classified as jagged, wavy, or planar, using the system 

proposed by Barton et al. [1974]. This measure is to be 

applied on a scale that ranges from hand sample size to 

several feet across a fall exposure. The CMRR assumes 

that roughness significantly affects shear strength only 

when cohesion is in the middle range (see Table 4). 

Table 4.—Bedding/discontinuity shear strength rating table 

for underground data1

COHESION Roughness Strong Moderate Weak Slickensided

Jagged ............... 35 29 24 10 

Wavy.................. 35 27 20 10 

Planar................. 35 25 16 10 

 1

If unit has no bedding/discontinuities, then apply test to the 

intact rock. 

 When drill core is available, strength testing can be 

conducted. The diametral PLT is a convenient index test 

that provides a substitute for bedding plane shear testing. 

Because the precise relationship between bedding plane 

shear strength and the PLT is not known, and since it 

seems unlikely that the same K-factor used to convert the 

axial test to the UCS would apply, the CMRR uses the 

Point Load Index (IS50) directly. The diametral PLT rating 

values were derived from the original CMRR tables and 

the data presented by Molinda and Mark [1996] and are 

shown in Figure 9. 

 If the diametral test results show that the rock fabric or 

laminations are low-strength, it would be illogical to give 

the rock high marks for discontinuity spacing. In fact, both 

the fracture spacing and the RQD also actually measure the 

strength of discontinuities as well as their spacing, because 

strong discontinuities might withstand the rigors of the 

drilling process while weak ones break apart. Therefore, 

the discontinuity rating is the lower of the Diametral PLT 

Rating or the Discontinuity Spacing Rating. 

Moisture Sensitivity Deduction 

 In the CMRR, the maximum deduction for moisture 

sensitivity is 15 points. The data sheet for the Immersion 

Test is shown in Figure 10. If Immersion Test results are 

not available, moisture sensitivity can sometimes be estimated visually in underground exposures. 

 Usually, some time is required for contact with humid 

mine air to affect rock strength. In short-term applications, 

therefore, it may not be appropriate to apply the moisture 

sensitivity deduction. The CMRR program reports both the 

Unit Rating and the CMRR with and without the moisture 

sensitivity deduction. 

 Research was conducted to explore the relationship 

between the Slake Durability Test (SDT) and the Immersion Test. In the SDT, 10 lumps of rock, each weighing 

about 0.1 lb, are oven-dried, weighed, and then rotated 

through a water bath for 10 min. The repeated wetting and 

drying, together with the mild abrasion that takes place 

during the test, causes moisture-sensitive rocks to break 

down. The Slake Durability Index is the final dry weight of 

the sample expressed as a percentage of the original dry 

weight [Hoek 1977]. 

Figure 9.—CMRR rating scale for diametral point load tests.

Figure 10.—Data sheet for the Immersion Test. 

IMMERSION TEST

Mine: Date: 

Unit No.: Tester: 

Sample Description (Lithology, bedding, etc.): 

Immersion Breakability

Observation Rating Observation Rating

 Appearance of Water 

Clear = 0 No Change 0 

Misty = -1 Small Change-2 

Cloudy = -3 Large Change -6 

 Talus Formation Breakability Index 

None = 0 

Minor = -1 

Major = -3 

 Cracking of Sample 

None = 0 

Minor/Random = -1 

Major/Preferred Orientation =-3 

Specimen Breakdown = -9 

 Total Immersion Index 

Procedure for Immersion Test

1. Select sample(s) - ~ hand sized 

2. Test for hand breakability. 

3. Rinse specimen (to remove surface dirt, dust, etc.) 

4. Immerse in water for 1 hour 

5. Observe and rate water appearance, talus formation, and cracking of sample 

6. Sum Rating for Immersion Index.

7. Retest for hand breakability. 

8. Determine Breakability Index

9. The final Immersion Test Index is the greater of the Breakability Index or the Immersion Index.
103

 To compare the two tests, rock samples were collected 

underground from a variety of mine settings, carefully 

wrapped to maintain in situ moisture content, and tested in 

the laboratory. A total of 96 tests were run on 16 distinct 

rock types from 9 mines. The results are shown in Figure 11. 

From the testing conducted to date, there is a good 

correlation between the two tests for the “not sensitive” 

and “slightly sensitive” classes. The correlation is less 

reliable for distinguishing “moderately sensitive” rocks 

from “severely sensitive” rocks. Table 5 indicates how the 

results from either test can be used for input to the CMRR. 

Table 5.—Moisture sensitivity classes and ratings from both 

Immersion and Slake Durability Tests1

Moisture sensitivity 

class 

Rating 

adjustment 

Immersion 

Index 

Slake Durability 

Index 

Not sensitive............ 0 0–1 100–98 

Slightly sensitive....... –3 2–4 98–92 

Moderately sensitive.. –7 5–9 92–80 

Severely sensitive..... –15 >9 <80 

 1

Apply rating adjustment to Unit Rating only when unit forms the 

immediate roof or if water is leaking through the bolted interval. 

Calculation of the Unit Rating 

 When using underground data, the equation for calculating the Unit Rating is— 

Unit Rating = UCS Rating + Discontinuity Intensity 

 Rating + Discontinuity Shear Strength Rating 

 + Multiple Discontinuity Adjustment 

 + Moisture Sensitivity Deduction 

For drill core data, the equation is even simpler: 

Unit Rating = UCS Rating + Discontinuity Rating 

 + Moisture Sensitivity Deduction 

Thickness-weighted Average Roof Rating 

 The next step in calculating the CMRR is to determine 

the thickness-weighted average of the Unit Ratings of all 

the units within the bolted interval. For example, assume 

that the roof consists of three units (from top down): 

• 2-m sandstone, Unit Rating = 60 

• 0.8-m siltstone, Unit Rating = 50 

• 0.4-m shale, Unit Rating = 40 

If the length of the roof bolts is 1.8 m, then the thicknessweighted average (RRW) is: 

51.1

1.8

[(0.4 40) (0.8 50) (0.6 60)] RRW = ∗ + ∗ + ∗ = (3) 

Note that even though the uppermost layer was 2 m thick, 

only the lowest 0.4 m (the distance to the top of the bolts) 

was used in the calculation. 

 The CMRR is now determined by applying several 

adjustment factors to the RRW. 

Strong Bed Adjustment (SBADJ) 

 One of the most important concepts in the CMRR is 

that the strongest bed within the bolted interval often 

determines the performance of mine roof. The strong bed’s 

effect on the CMRR depends first upon how much stronger 

it is than the other units. Second, the strong bed must be at 

least 0.3 m thick before it can provide any additional 

support, and the amount of the adjustment is maximum 

when the bed is at least 1.2 m thick. Third, the roof bolts 

must obtain at least 0.3 m of anchorage in the strong bed 

for the adjustment to be considered. Finally, the higher into 

the roof that the strong bed is located, the less its positive 

effect will be. 

 In the original CMRR, the SBADJ was determined 

using a table. For improved accuracy and to facilitate 

implementation of the table in the computer program, 

Equation 4 was derived using multiple regression: 

 (4)

where: 

• SBD is the strong bed difference—the difference 

between the strong bed’s Unit Rating and the 

thickness-weighted average of all the Unit Ratings 

within the bolted interval; 

• THSB is the thickness of the strong bed (m); and 

SBADJ = [(0.72 SBD*THSB) – 2.5] 

 * [1 – (0.33 (THWR – 0.5))]

 Figure 11.—Comparison of the Slake Durability and 

Immersion Tests. 
104

• THWR is the thickness of the weak rock suspended 

from the strong bed (m). 

Note that if the strong bed is at the top of the bolted 

interval, its full thickness is used in the calculation of the 

SBADJ (up to a maximum of 1.2 m). 

Other Adjustments 

 Number of Units: Many workers have indicated that 

mine roof that contains numerous lithologic contacts is less 

competent than roof that consists of a single rock type 

[Karmis and Kane 1984; Kester and Chugh 1980]. When 

depositional processes change and deposit distinctly different material, there is generally, but not always, a sharp 

contact between units. Since gradational contacts do not 

weaken the roof, the characteristics of major bedding contact surfaces (cohesion and roughness) should be noted. 

The maximum deduction from the CMRR is 5 points when 

more than four weak contacts are present. 

Groundwater Adjustment: Groundwater is most 

prevalent in shallow mines, particularly beneath stream 

valleys, but it can also be introduced by leakage from 

pooled water in abandoned mines or fracturing of overlying aquifers during high-extraction mining. The CMRR 

maintains the RMR system’s rating scale, with a maximum 

deduction for flowing groundwater of 10 points. 

Surcharge: The strength of rocks overlying the bolted 

interval is considered only when they are significantly 

weaker than the rocks within it. An example is a western 

mine where 1.2 m of relatively strong top coal was 

overlain by 6 m of weak, rooted claystone. Because the 

roof beam needed to carry some of the surcharge (extra 

weight) of the incompetent claystone, stability was 

reduced. The CMRR accounts for the surcharge with a 

3-point deduction. 

THE CMRR COMPUTER PROGRAM 

 The CMRR program is designed to facilitate the entry, 

storage, and processing of field data. Either core or 

underground data can be entered, and calculations are 

updated instantly when a change is made. This allows the 

user to vary parameters, such as the bolt length, to see their 

effect on the final CMRR. 

 The underground data entry screen contains dropdown menus that are used to enter the data for each of the 

parameters. In the core data screen, the user has the option 

of entering PLT test data and having the program automatically determine the mean UCS and diametral Is(50). 

Otherwise, the user can enter the mean strength values 

directly. 

 An important feature of the new program is a built-in 

interface with AutoCAD. Data from up to 200 locations 

can be entered and saved in a single file, along with their 

geographic location coordinates. The program can create a 

file for export that includes both the calculated CMRR 

values and the locations. A CMRR layer can then be 

created in AutoCAD for use in mine planning. 

APPLICATIONS OF THE CMRR 

 During the past 10 years, the CMRR has been used 

extensively in the United States. Figure 12 shows the 

current database, containing 264 observations from more 

than 200 mines. The figure reveals some very important 

regional trends. Weak roof predominates in the northern 

Appalachian and Illinois Basin coalfields, which are also 

areas where roof falls tend to occur more frequently 

[Pappas and Mark 2003]. Central Appalachian mines have 

a wide range of CMRR values, but the typical roof is of 

moderate strength. Utah mines tend to have the most 

competent roof in the United States. 

 A number of mine planning design tools based on the 

CMRR are discussed below. 

Analysis of Longwall Pillar Stability (ALPS) 

 The first, and perhaps the best known, application of 

the CMRR is the ALPS pillar design method [Mark et al. 

1994]. A large database of longwall case histories was 

collected from throughout the United States and subjected 

to statistical analysis. The results showed that when the 

roof was strong (CMRR>65), longwall chain pillars with 

an ALPS stability factor (SF) as low as 0.7 could provide 

satisfactory tailgate conditions (Figure 13). On the other 

hand, when the roof was weak (CMRR<45), the ALPS SF 

might need to be as high as 1.3. ALPS has been the 

standard technique employed to size pillars for most 

U.S. longwalls for many years. 

0

20

40

60

80

100

120

<=40 41 - 50 51 - 60 61 -70 >70

Coal Mine Roof Rating (CMRR)

No. of Observations

Northern Appalachia Southern Appalachia Midwest

CO,WY,NM Alabama Utah

N = 264

Figure 12.—Current CMRR database. 
105

Longwall Tailgate Design (Australia) 

 ALPS was the starting point for a project under the 

Australian Coal Association Research Program (ACARP) 

to develop an Australian chain pillar design methodology 

[Colwell et al. 1999]. The project aimed to calibrate ALPS 

for the different geotechnical and mine layouts used in 

Australia. Ultimately, case history data were collected 

from 60% of Australian longwall mines. 

 The study found strong relationships between the 

CMRR, the tailgate SF, and the installed level of primary 

support. Design equations were developed that reflected 

these trends. The final product, called the Analysis of 

Longwall Tailgate Serviceability (ALTS), was implemented in a computer program and has become widely 

used in Australia. Most recently, an expanded study 

resulted in an updated version called ALTS II [Colwell 

et al. 2003]. 

Stability of Extended Cuts 

 Place change mining, in which mining equipment 

moves from entry to entry as the section is advanced, is the 

standard development method in the United States. The 

traditional 6-m cut length was determined by the distance 

from the cutting head to the operator’s compartment. With 

the advent of remote-control continuous miners, extended 

cuts up to 12 m long have become common. However, 

many mines with extended-cut permits only take them 

when conditions allow. Where the roof is competent, 

extended cuts are routine. At the other extreme, when the 

roof is very poor, miners may not be able to complete a 

traditional 6-m cut before the roof collapses. 

 To help predict when conditions might be suitable for 

extended cuts, a study was conducted at 36 mines throughout the United States. The study found that when the 

CMRR was greater than 55, extended cuts were nearly 

always routine, but when the CMRR was less than 37, they 

were almost never taken [Mark 1999a]. The data also 

showed that extended cuts were less likely to be feasible as 

the roof span or the depth of cover increased (Figure 14). 

Roof Bolt Selection 

 To help develop scientific guidelines for selecting roof 

bolt systems, the National Institute for Occupational Safety 

and Health conducted a study of roof fall rates at 37 U.S. 

mines [Mark et al. 2001; Molinda et al. 2000]. The study 

evaluated five different roof bolt variables, including 

length, tension, grout length, capacity, and pattern. Roof 

spans and the CMRR were also measured. Performance 

was measured in terms of the number of roof falls that 

occurred per 3 km of drivage. 

 The study found that the depth of cover (which 

correlates with stress) and the roof quality (measured by 

the CMRR) were the most important parameters in determining roof bolting requirements. Intersection span was 

also critical. The study’s findings led to guidelines that can 

be used to select the proper span, bolt lengths, and bolt 

capacity based on the CMRR. The results have been 

implemented into a computer program called Analysis of 

Roof Bolt Systems (ARBS). 

Multiple-seam Mining 

 Interactions with previous mining in underlying or 

overlying seams are a major cause of ground instability in 

the United States. A statistical analysis of a database of 

more than 360 case histories found that the CMRR was 

highly significant in predicting the outcome of a multiseam 

interaction. Other significant variables include the pillar 

SF, the total pillar stress, whether the previous seam was 

above or below, and what type of pillar structure is present 

in the previous seam. 

 The statistical analysis became the foundation for the 

Analysis of Multiple-seam Stability (AMSS) software 

package. The output from AMSS is the critical interburden 

thickness that is necessary to avoid interactions. AMSS 

 Figure 13.—Relationship between the CMRR and the 

ALPS SF. 

20

30

40

50

60

70

80

90

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

ALPS STABILITY FACTOR

CMRR

Satisfactory Cases

Unsatisfactory Cases

ALPS Design Equation

 Figure 14.—Relationship between the CMRR and the 

feasibility of extended cuts. 

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600

Depth of Cover (m)

CMRR

Always Stable

Sometimes Stable

Never Stable

Extended Cut Equation
106

indicates that, all else being equal, a CMRR=45 roof 

requires approximately 15 m more interburden than a 

CMRR=65 roof. 

Longwall Mining Through Open Entries 

and Recovery Rooms 

 Unusual circumstances may require that a longwall 

retreat into or through a previously driven room. The 

operation is usually completed successfully, but there have 

been a number of spectacular failures. To help determine 

which factors contribute to such failures, an international 

database of 131 case histories was compiled [Oyler et al. 

1998]. The study found that the CMRR and the density of 

standing support were the two most important parameters 

in predicting severe weighting-type failures. These failures 

occurred only when the CMRR was less than 55 and when 

the support density was less than 0.5 MPa. When the 

CMRR was 40 or less, all of the successful cases employed 

a standing support density of at least 1.0 MPa. 

Roof Fall Evaluations (South Africa) 

 The CMRR featured prominently in an important 

research project sponsored by the Safety in Mines 

Research Advisory Committee (SIMRAC) and other leading industry, labor, and government organizations in South 

Africa. The goal of the project was to investigate the 

causes of fatal roof failures in South African coal mines. 

A team of recognized experts visited a broad spectrum of 

mines and collected data at 182 roof fall sites. The study 

found that roof falls were more likely where the roof was 

less competent in terms of the CMRR. Another finding 

was that the CMRR correlated well with roadway widths. 

Based on data presented by Mark [1999b] (see Figure 15), 

the study also concluded that “in South African coal 

mines, less support is used for comparable roof conditions 

than either the USA or Australia. This supports previous 

conclusions that in South African coal mines, the density 

of supports needs to be increased.” [van der Merwe 2001]. 

 Another SIMRAC study found the CMRR easy to use 

and robust enough to adequately describe the roof 

conditions at most South African collieries [Butcher 2001]. 

It took less than 4 hr for a trained geologist to become 

competent with the method. The results seemed more 

reasonable than those obtained from the RMR, which 

tended to overrate ground conditions by at least one class 

(20 points) due to its lack of sensitivity to the characteristics of bedded strata. Some improvements were suggested for the CMRR, including adjustments for joint 

orientation, blasting, and horizontal stress. 

Baseline Comparison of Ground Conditions 

(Canada) 

 The Canadian underground coal industry is small and 

geographically dispersed. To assist the mines in maintaining world-class safety standards, the Canada Centre for 

Mineral and Energy Technology (CANMET) established 

the Underground Coal Mine Safety Research Consortium. 

One of the consortium’s first projects was aimed at 

establishing a “best practice” baseline for conducting geological and geomechanical assessments and applying the 

findings to geotechnical design. 

 The CMRR was found to be particularly valuable in 

the assessment [Forgeron et al. 2001]. It allowed the 

Canadian underground mines to be compared with each 

other and with international benchmarks. Based on the 

CMRR, many ground control safety technologies developed in the United States were found to have direct 

application to Canadian mines. 

Other Applications 

• Highwall mining can become uneconomic if the roof 

is so weak that it collapses before the miner has been 

withdrawn from the hole. The CMRR has been used 

to evaluate potential highwall mining reserves and to 

identify potentially unsuitable areas [Hoelle 2003]. 

• Tailgate support guidelines incorporating the CMRR 

have been included in the Support Technology Optimization Program (STOP) [Barczak 2000]. 

• Input for numerical models have been derived from 

the CMRR [Karabin and Evanto 1999]. 

 Figure 15.—Relationship between the CMRR and roof bolt 

density in the United States, Australia, and South Africa. 

20

30

40

50

60

70

80

90

100

0 0.1 0.2 0.3 0.4 0.5 0.6

Primary Support Rating (PSUP)

Coal Mine Roof Rating (CMRR)

USA Case Histories

S. Afr. Case Histories

South African

Mines

Australian

Mines
107

CONCLUSIONS 

 Roof geology is central to almost every aspect of 

ground control. The CMRR makes it possible to quantify 

roof geology so that it can be included in mine planning 

decisions. Worldwide experience has shown that the 

CMRR is a reliable, meaningful, and repeatable measure 

of roof quality. 

 A wide variety of design tools based on the CMRR 

have now been developed. They address a broad range of 

ground control issues and rely upon large databases of 

actual mining case histories. Without the CMRR, it would 

not have been possible to capture this invaluable 

experience base. 

 The new core procedures and computer program 

further expand the potential of the CMRR. It is now possible to routinely collect CMRR data during geologic 

exploration or from underground mapping, complete the 

calculations, and integrate the results into mine mapping 

software. Foreknowledge of conditions means better mine 

planning and fewer unexpected hazards underground. 

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111

PROBLEMS WITH ROCK CLASSIFICATION 

FOR EMPIRICAL AND NUMERICAL DESIGN 

By Douglas Milne, Ph.D.1

ABSTRACT 

 Most empirical and numerical approaches to design in 

rock mechanics incorporate rock mass classification. 

Numerical design methods generally use classification values to calculate input parameters for stress-based failure 

criteria. Empirical methods use classification to allow 

comparisons between similar rock mass conditions, generally based on a graphical design technique that differentiates stable and failed opening geometries. 

 Classification systems are the best tool available for 

assessing rock mass properties; however, there are problems with classification systems that should be highlighted. Rock mass performance can only be realistically 

estimated by coupling a unique description of the rock 

mass with known loading conditions. Current classification 

systems cannot provide a unique classification value. The 

weightings applied to quantify rock mass properties for 

classification can result in significantly different rock 

masses having the same classification values. These 

weightings have been proven effective for tunnel design 

and support, but classification systems are now used for 

many more applications. 

 Rock classification systems evolved from a quick and 

easy field tool for estimating tunnel stability and support 

requirements. The need for a rapid field tool means that 

rock mass classification is relatively insensitive to 

improved methods of measuring rock mass properties. 

 Problems with classification systems and their application are highlighted in this paper. These problems must be 

recognized and documented before improvements can be 

made. An understanding of the evolution of classification 

systems and their application for both numerical and 

empirical design approaches is invaluable in highlighting 

current shortcomings. 

INTRODUCTION 

 Rock mass classification systems are the basic component of empirical mine design. They have been traditionally used to group areas of similar rock mass 

properties, to provide guidelines for stability performance, 

and to estimate support requirements. More recently, rock 

mass classification values have been used along with 

numerical modeling tools. Substantial work has been done 

linking classification values to various material properties 

such as Young’s modulus, as well as “m” and “s” for 

Hoek-Brown failure criteria and “φ” and “c” for MohrCoulomb criteria. These values are then used as input for 

numerical models. 

 There are many rock mass classification schemes, 

often developed for site-specific purposes. The most commonly used systems are the Rock Quality Designation 

(RQD) [Deere et al. 1967], forms of the Rock Mass Rating 

(RMR) system developed by Bieniawski [1973, 1976, 

1979, 1989], and the Norwegian Geotechnical Institute’s 

Q-system [Barton et al. 1974]. The RQD system is a measure of joint spacing and is incorporated as part of both the 

Q and RMR systems. More recently, the Geological 

Strength Index (GSI), which has evolved from the RMR 

system, is being used [Hoek et al. 1995]. The Q, RMR, and 

GSI systems are discussed in this paper. 

 There are problems and challenges with rock mass 

classification systems, primarily due to their numerous and 

conflicting goals. Initially, classification was done to give 

a quick and repeatable assessment of the rock mass to 

provide guidelines for underground opening stability and 

support requirements. It was made quick and easy to use 

by limiting the number of rock mass classification categories. A need for greater precision in the estimation of 

opening stability and support led to an increase in possible 

classification categories, resulting in increased time needed 

for classification and increased difficulty in obtaining 

repeatable results. An additional challenge for rock mass 

classification is the goal of providing an accurate 

assessment of rock mass behavior and properties for an 

increasing array of engineering applications. These 

include: 

• Tunnel and mine opening stability assessments 

• Tunnel and mine opening support requirements 

• Rock mass properties, including Young’s modulus, 

Poisson’s ratio, and strength 

• Rock mass failure criteria 

• Rock mass slope stability, as well as other varied 

applications 

 This paper summarizes some of these issues and suggests approaches for improving the application of field 

data for rock mechanics. 

 

 1

Associate professor of geological engineering, University of 

Saskatchewan, Saskatoon, Canada.
112

ROCK CLASSIFICATION VERSUS 

CHARACTERIZATION 

 Rock classification is used for many purposes in rock 

mechanics. Classification systems were originally developed as complete design packages for civil engineering 

tunnel applications. Given the rock mass classification 

value and tunnel span, support requirements and estimated 

tunnel stability could be obtained [Barton et al. 1974; 

Bieniawski 1976]. These classification systems often 

included factors to assess stress conditions and the orientation of discontinuities relative to the engineered structure. One of the main differences between tunneling and 

mining applications of rock classification is the large 

variation in orientation, depth, and geometry of underground openings in mining. Civil engineering applications 

are generally applied to tunnels at a fairly constant depth, 

orientation, and geometry. None of these conditions is constant in most mining applications. 

 If mining applications included joint orientation and 

stress conditions in rock classification, the same rock mass 

could have dozens of classification values throughout the 

mine depending on the drift orientation, mining level, and 

the excavation history [Milne et al. 1998]. This would lead 

to significant confusion and make the classification systems useless. Components of classification systems are 

often used in empirical mine design applications, with sitespecific or stress conditions assessed separately. Numerical 

design methods also often apply stress conditions with the 

design process, so the addition of stress factors within the 

classification system is redundant. Both the RMR and Q 

classification systems are frequently adjusted for mining 

applications. The Q′ system is used in numerous empirical 

design techniques and differs from the Q-system in that the 

stress reduction factor (SRF) is set to 1.0 [Potvin 1988; 

Clark 1998]. RMR′ system is often used for mining span 

design. The RMR′ system does not include the RMR 

correction for joint orientation. 

 Palmström et al. [2001] discuss the difference between 

rock classification and characterization. Rock mass characterization should consist of the intrinsic properties of the 

rock mass, which include intact rock properties, discontinuity spacing and pattern, as well as discontinuity 

properties. If rock characterization is used, loading or 

environmental factors such as stress or discontinuity 

orientation should be considered later in the design process. Rock classification systems, however, should be 

treated as complete design packages and are to be used 

with the appropriate empirical design charts (Figure 1). 

 There has been some discussion concerning the assessment of groundwater factors in rock mass classification 

and characterization. Palmström et al. [2001] suggested 

that groundwater be excluded from rock mass characterization and added later in the design process since water 

conditions can vary significantly in the same rock mass. 

Laubscher and Taylor [1976] incorporated water as a factor, reducing the strength properties on the discontinuity 

surfaces in their modified RMR system (MRMR). There is 

also some confusion as to the application of water conditions with the stability graph design method for underground openings [Potvin 1988]. Hoek et al. [1995] state 

the following concerning the application of Q′ for the stability graph method: “The system has not been applied in 

conditions with significant groundwater, so the joint water 

reduction factor Jw is commonly 1.0.” The groundwater 

term in the Q′ classification is often ignored when using 

the stability graph design method. This is not a safe 

approach because there is nowhere else to assess groundwater conditions in this design method. Similar confusion 

exists with determining “m” and “s” failure criteria for 

design [Hoek and Brown 1980]. The original “m” and “s” 

factors were based on RMR76 classification values, with 

 Figure 1.—Components of rock classification and rock characterization (after Milne and Hadjigeorgiou [2000] 

and Cai et al. [2004]). 

1Discrete features such as faults and shears are not generally assessed in rock classification schemes.

2

Ground water has been considered either a rock mass property or an environmental or loading condition.

Environmental Conditions

Ground water

Stress state

Rock Mass Properties

Intact rock properties (UCS, E, v)

Joint characteristics

Roughness, waviness, infilling, texture 

surface texture

Density and pattern of joints

RQD, spacing, continuity, length, 

block size, distribution and shape

Rock mass 

characterisation

Rock mass 

properties 

for design

Rock mass 

classification Design

1Discrete features such as faults and shears are not generally assessed in rock classification schemes.

2

Ground water has been considered either a rock mass property or an environmental or loading condition.

Environmental Conditions

Ground water

Stress state

Rock Mass Properties

Intact rock properties (UCS, E, v)

Joint characteristics

Roughness, waviness, infilling, texture 

surface texture

Density and pattern of joints

RQD, spacing, continuity, length, 

block size, distribution and shape

Rock mass 

characterisation

Rock mass 

properties 

for design

Rock mass 

classification Design

Environmental Conditions

Ground water

Stress state

Rock Mass Properties

Intact rock properties (UCS, E, v)

Joint characteristics

Roughness, waviness, infilling, texture 

surface texture

Density and pattern of joints

RQD, spacing, continuity, length, 

block size, distribution and shape

Rock mass 

characterisation

Rock mass 

properties 

for design

Rock mass 

classification Design
113

the groundwater factor set to dry conditions [Hoek et al. 

1995]. This was done to avoid counting groundwater conditions twice. It assumes that effective stress conditions 

will be used with numerical modeling “m” and “s” design 

approaches. 

 Groundwater conditions are not intrinsic properties of 

the rock mass and, ideally, groundwater would be assessed 

later in the design process. Unfortunately, there are few 

empirical or numerical design techniques that allow 

groundwater conditions to be added to the design process. 

It is not safe to remove groundwater from rock mass 

characterization unless it is known that the groundwater 

conditions will be assessed later in the design process. As a 

general rule, any factors known to influence stability 

should be included in either the rock characterization or 

the design process. 

QUALITATIVE VERSUS QUANTITATIVE 

ROCK MASS CHARACTERIZATION 

 One of the goals of classification systems is that they 

be a quick assessment of rock mass conditions for support 

design and stability assessment. A second goal of rock 

classification is to obtain repeatable results. The repeatability of rock classification can be achieved by assessing 

the rock mass in very broad, general categories at the cost 

of precision or by assessing the parameters that make up 

rock classification systems with quantitative measurements at the cost of speed and ease of use. 

 One of the earliest rock mass classification systems is 

attributed to Terzaghi [1946], who states: “From an engineering point of view, knowledge of the type and intensity 

of the rock defects may be much more important than the 

type of rock which will be encountered.” Terzaghi’s 

classification uses terms such as “moderately blocky and 

seamy” to describe the rock mass and is difficult to assess 

accurately due to its subjective description of the rock 

mass. This system was probably easy to use. There were, 

however, only seven categories of rock masses ranging 

from intact rock to swelling rock containing clays such as 

montmorillonite, so it could not be an overly precise 

assessment of ground conditions. 

 Modern classification systems, such as the Q and variations of the RMR systems, consist of assessments of the 

size and perhaps shape of intact blocks of rock bounded by 

discontinuities, the discontinuity surface condition or frictional properties, intact rock strength, and groundwater 

conditions. 

 The method of assessment of these categories has 

evolved from the mainly subjective assessment of factors 

in Terzaghi’s classification to more qualitative assessments. The RMR system is based on a numerical assessment of five parameters: 

• Intact rock unconfined compressive strength (UCS) 

• Rock Quality Designation (RQD) 

• Discontinuity spacing 

• Discontinuity surface condition and 

• Groundwater 

Intact rock strength, RQD, and groundwater are assessed 

in fairly quantitative terms. The discontinuity assessment 

term is more subjective and contains descriptions such as 

“very rough surfaces” and “slightly rough surfaces,” which 

require experience to differentiate between and do not provide a very precise assessment. 

 The Q classification system is probably the least 

subjective classification currently in common use. The 

more analytical and quantitative descriptions used in the 

Q-system are coupled with an assessment of more rock 

mass parameters, and these assessments are divided into 

many more categories. For instance, the RMR76 system 

describes the condition of discontinuities with five broad 

categories. The Q-system, with its assessment of smalland large-scale roughness, alteration, and infilling can 

differentiate between more than 60 conditions of joint surfaces. The Q-system can give very precise rock classification values; however, this results in making repeatability 

more difficult to achieve and also increases the time 

required to obtain an estimate of rock classification. 

 The RMR system has evolved to give the user the 

option of estimating rock classification values with more 

precision. The RMR89 system is the best example. It allows 

the user to use the same five categories assessing discontinuity surface conditions, but adds the option of 

describing joint surface conditions with five properties for 

assessment, which are: 

• Discontinuity length; 

• Discontinuity aperture or separation; 

• Discontinuity roughness; 

• Discontinuity infilling; and 

• Discontinuity weathering 

 Each of these 5 properties is broken down into 5 categories, giving a total of 25 possible joint surface assessments, compared to 60 categories in the Q-system and 

5 with RMR76. 

 The GSI system is the newest commonly used rock 

mass classification system. It make a conscious attempt to 

move away from classification systems that quantify or 

rate individual properties of the rock mass. The RMR 

classification system has evolved to be more quantifiable 

[Bieniawski 1989], and others have attempted to improve 

rock mass characterization by improving our ability to 

measure rock mass properties such as discontinuity surface 

properties [Milne et al. 1991; Hadjigeorgiou et al. 1994] 

and intact block size distributions [Hadjigeorgiou et al. 

1998]. In his discussion of the development of the GSI 
114

classification system, Hoek [2004] states: “It was also felt 

that a system based more heavily on fundamental geological observations and less on ‘numbers’ was needed.” The 

GSI classification system consists of six categories 

describing the size and shape of intact rock blocks and five 

categories describing the surface condition of discontinuities. This system is based on geological observations and 

avoids the engineering approach of dividing the properties 

of a rock mass into components and measuring these components as accurately as possible. 

 The GSI system has been developed to provide rock 

mass properties for numerical modeling, which may 

account for the different approach taken for assessing the 

rock mass. Practitioners are encouraged to avoid precise 

estimates of classification, but rather to give a range 

representative of the highly variable properties found in 

natural materials such as rock masses. This approach is 

also well suited to numerical modeling, where more precise estimates of rock mass properties may rely on back 

analysis of observed rock mass behavior. 

 The following section discusses input for classification 

systems. 

INPUT PROPERTIES FOR 

ROCK MASS CHARACTERIZATION 

 Goals of conventional classification systems include 

quickly obtaining as precise and repeatable an estimate of 

rock characterization values as possible. These goals are, 

to a certain extent, contradictory. Increased precision is 

difficult to duplicate, especially by different practitioners. 

An approach taken with some systems has been to break 

the properties of a rock mass into more easily quantified 

components, which improves the precision and repeatability, but may significantly increase time required to 

conduct rock characterization. A discussion of the more 

common components of rock mass characterization 

follows. 

Intact Rock Strength 

 Intact rock strength is included in all versions of the 

RMR system. It is an intrinsic part of rock mass characterization; however, in many systems like Q′ and GSI, the 

rock strength assessment is left for the design process. 

Groundwater Conditions 

 Groundwater conditions are part of most classification 

systems, such as Q, Q′, and RMR. If groundwater is not 

implicitly included in the design, it must be included in the 

rock mass classification/characterization. The conventional 

characterization systems assign a weighting to groundwater conditions based on categories such as— 

• Dry conditions; 

• Damp conditions; 

• Water inflow in liters per minute along 10 m of drift; 

or 

• A description or measurement of water pressure. 

 These descriptions of groundwater conditions seem 

sufficiently precise and easily quantified for rock characterization purposes. The GSI system does not include 

water in its basic classification, so it should be treated later 

as a correction or assessed in the design procedure used. 

Discontinuity Spacing and Intact Geometry 

 The rock mass RQD is used in both the RMR and 

Q classification systems to assess discontinuity spacing. 

The actual spacing of discontinuities is included with 

RMR, and Q looks at the number of joint sets present. The 

RQD assessment of spacing has significant drawbacks 

[Milne et al. 1998]. It is sensitive to the measuring direction; however, this can be corrected by using the equation 

by Palmström [1985] that relates the number of joints 

found in a cubic meter of rock (Jv ) to an average RQD: 

 RQD = 115 – 3.3 Jv (1) 

 Other problems with the RQD term include the fact 

that it is relatively insensitive to discontinuity spacings 

greater than 30 cm. The RMR system corrects for this 

by adding a measurement of discontinuity spacing. The 

Q-system couples RQD with an assessment of the number 

of joint sets present (Jn). It can easily be shown, however, 

that if three joint sets with equal spacing are present in a 

rock mass, the ratio of RQD/Jn becomes a constant at a 

joint spacing of greater than 0.7 m. 

 The assessment of joint spacing in classification systems is an attempt to indirectly define block size. Line 

mapping provides much of the data required for rock mass 

characterization and can provide realistic estimates of 

intact block size geometry. There is little justification for 

discarding much of the data collected by mapping programs simply because the data cannot be applied to 

currently used rock classification systems. Mapping data 

can generate realistic three-dimensional discontinuity systems, which can be used to develop more complete information, such as block size distribution, that better represent the discontinuous nature of a rock mass. Based on 

field work in several underground mines, Hadjigeorgiou 

et al. [1998] have shown that three-dimensional joint 

systems can provide a better estimate of block size than 

that provided by traditional rock classification systems. 

Figure 2 shows a correlation between RQD, block size, 

and the Jv term. 

 The actual geometry of intact blocks is not included in 

these systems; however, it is discussed in the GSI system, 

and other rock mass characterization systems, such as RMi 
115

[rockmass.net 2007], include an assessment of intact block 

geometry. 

DISCONTINUITY SURFACE CONDITION 

 The surface condition of discontinuities is a measure 

of how easily blocks can move relative to each other and is 

an important component of rock characterization. The 

Q-system has one of the most precise approaches for 

assessing this property and looks at it in terms of the 

following: 

• Large-scale roughness 

• Small-scale roughness 

• Alteration 

• Infilling thickness 

 The RMR89 system adds terms describing infilling 

aperture and length. There is some difficulty assessing the 

Q terms for roughness and alteration, and guidelines have 

been developed to assist with this. 

 The following discussion is taken from Milne and 

Hadjigeorgiou [2000] and Milne et al. [1992]. In the RMR 

system, joint roughness is part of the discontinuity 

description, with no distinction between small- and largescale roughness. The Q-system identifies two scales of 

joint roughness as distinct input into Jr. No qualitative 

methods of assessing roughness are included with the 

original classification methods; however, less subjective 

approaches have been applied to classification. 

 A study was conducted to improve the precision and 

repeatability possible for estimating values of joint roughness for the Q classification system. An extensive field 

data-gathering program was conducted to obtain discontinuity profiles using a 1-m-long “profile comb” 

(Figure 3) [Milne et al. 1991]. Based on the collection of 

more than 200 1-m-long discontinuity profiles from 

10 mines across Canada, a simple repeatable fieldmeasuring technique has been developed. The joint roughness coefficient (JRC) [Barton and Choubey 1977], 

coupled with these field data, has been applied to assess 

small-scale roughness for 10-cm profile lengths. Based on 

these field data, a JRC value of less than 10, or a joint 

profile amplitude of less than 2.5 mm over a 10-cm length, 

is defined as “smooth.” For JRC estimates greater than 10 

and amplitudes greater than 2.5 mm, surfaces are defined 

as “rough.” A small-scale roughness term, Jr/r, is used to 

represent small-scale roughness and is set to 1.0 and 1.5 

for smooth and rough joints, respectively. For large-scale 

waviness, Jr/w, three categories of waviness have been 

defined based on field data on profile amplitudes over a 

1-m length. Wavy joints have 1-m profile amplitudes of 

20 mm or more, and the Jr/w value is set to 2.0. Planar to 

wavy joints have amplitudes between 10 and 20 mm and 

a Jr/w value is set to 1.5. To obtain a Jr value for the 

Q classification, the Jr/r and Jr/w values are multiplied 

together (Equation 2). 

Jr = Jr /r × Jr /w (2) 

 Developing more quantitative assessments of rock 

mass classification systems is more complicated than 

simply developing improved methods of measuring rock 

mass properties and superimposing them on existing subjective descriptions. Improved measuring methods must be 

based on extensive field data collections and should ideally 

be coupled with a database of case histories. 

Summary of Input Parameters for Existing 

Rock Classification Systems 

 More detailed data on rock classification can be 

applied to some aspects of existing classification systems. 

In many cases, however, it would be difficult to know how 

to incorporate detailed information with existing classification. As an example, it is difficult to know how a measure 

 Figure 2.—Correlation between RQD, block size, and the 

joint volume term, Jv . 

1

10 0.1 1 10 100 0.1 1000 1 10 10 000 100 000

100 10

0 30 60 90 100

VOLUMETRIC

JOINT COUNT

BLOCK

VOLUME

RQD

1

50 20 5 2 0.5 0.2 joints/m3

dm3

m3 cm3

 Figure 3.—One-meter-long profile comb used to measure 

discontinuity roughness at 10 mines across Canada. 
116

of the rock mass block size can be incorporated with current systems that assess the rock mass in terms of joint 

spacing, RQD, and the number of discontinuity sets 

present. Work has been done to augment the GSI classification system with more quantifiable terms [Cai et al. 

2004]; however, it could be argued that the GSI system 

was specifically developed to avoid this approach. 

 The challenges with developing a more precise method 

of quantifying properties of a rock mass are overshadowed 

by problems with how classification systems use these 

data. Classification systems attempt to assess factors influencing rock mass performance and properties and 

represent that assessment as a single number. To do this, 

individual rock mass properties are given a weighting, 

which represents the relative importance of that rock mass 

property. The next section discusses the importance of 

these weightings. 

WEIGHTING ASSESSMENTS OF 

ROCK MASS PROPERTIES 

 Classification systems attempt to provide a basis for 

estimating deformation and strength properties for a rock 

mass, as well as provide data for estimating support 

requirements [Cai et al. 2004]. They are also used to assist 

in estimating the overall stability of excavations. To obtain 

a single classification/characterization value to represent a 

rock mass, weightings are given to the various rock mass 

properties. The weightings assigned to the individual 

properties of a rock mass are, in most cases, the same 

under all loading conditions. Table 1 summarizes some of 

these weightings expressed as the influence of each property on the total range in classification values. The assignment of a weighting, or degree of influence, is required to 

allow an engineer to represent rock mass properties as a 

single number. This is a significant weakness in rock 

mechanics. Apart from a fairly major change between the 

RMR76 and RMR89 systems, these weightings have not 

changed significantly since the early 1970s. The GSI system seems to differ; however, the main difference is that 

rock strength and groundwater conditions are left for later 

consideration. The remaining properties of block size and 

discontinuity condition are given weightings similar to 

those in the Q-system. 

 The application of weighting systems within classification schemes means that a rock mass with a relatively 

large intact block size and smooth slippery joint surfaces 

can have the same classification value as a rock mass with 

much smaller intact blocks and rough joints. Table 2 gives 

an example of two different rock masses (A and B) with 

different properties, but similar rock mass classification 

values. 

 Rock mass A and rock mass B would require significantly different support measures, and it is unlikely that 

maximum stable tunnel spans or the overall strength or 

deformation modulus of these two rock masses would be 

the same. Neither the Q′ nor GSI systems reflect this 

difference. Different variations in discontinuity spacings 

and conditions could have been chosen to give the same 

RMR classification values for similar differences in rock 

mass properties. 

 The influence of loading conditions and scale effect 

are two other factors that can have significantly different 

effects on the performance and properties of the two rock 

masses described. In a narrow tunnel situation with a span 

in the order of 3 m, rock mass A would, in most cases, 

perform much better than rock mass B. Very few intact 

rock blocks would be exposed in rock mass A, so the intact 

rock properties would have a greater influence on the 

overall rock mass compared to rock mass B. 

 It seems unrealistic to assume that the weightings 

applied to rock mass parameters will give accurate assessments of stability and rock mass properties at all scales of 

engineering applications and at all loading conditions. The 

Q-system reflects the importance of loading conditions 

with the assessment of intact rock strength. Intact rock 

strength is included in the SRF factor, and when the UCS 

Table 1.—Influence of rock mass properties on 

rock classification (after Milne and Hadjigeorgiou [2000]) 

Classification system 

Q′ Q RMR76 RMR89 GSI 

Unconfined compressive strength (UCS) 

% of total range....... 0 0 16 16 0 

Block size 

% of total range....... 41 33 46 35 50 

Discontinuity surface friction 

% of total range....... 38 30 27 33 50 

Groundwater 

% of total range....... 21 17 11 16 0 

Stress or UCS/stress ratio 

% of total range....... 0 20 0 0 0 

Table 2.—Classification assessment of two different 

rock masses 

 Rock mass A Rock mass B 

UCS.................................... 75 MPa 75 MPa 

Groundwater ...................... Dry Dry 

RQD ................................... 100% 20% 

No. of joint sets .................. 2 joint sets 3 joint sets 

Average joint spacing......... 2-m spacing 10-cm spacing 

10-cm scale JRC................ <10 >10 

Amplitude over 1 m ............ <1 cm >3 cm 

Alteration............................ Chlorite coating, 

can be dented with 

a fingernail 

Clean joint 

surfaces 

Q′........................................ ≈ 6 ≈ 6 

GSI ..................................... ≈ 60–65 ≈ 60–65 

RMR76................................. ≈ 74 ≈ 55 

RMR89................................. ≈ 77–82 ≈ 63 
117

of the intact rock divided by the induced stress exceeds 10, 

rock strength is not a factor in rock classification. Underground engineering structures range in size from drill holes 

to small tunnels to open stopes hundreds of meters in 

extent. It may be necessary to apply some scaling factor to 

rock classification assessments of intact block geometry to 

account for the scale of engineering applications. 

 The inherent weakness in the weighting factors applied 

in rock classification schemes can be illustrated with some 

typical design applications using some empirical design 

methods. 

Tunnel Roof Design 

 Both the Q classification system and the RMR76 system were originally developed to assess the stability, 

standup time, and support requirements of tunnels. Most of 

the rock mass properties, environmental conditions, and 

project-related features shown in Figure 1 are combined in 

some fashion to determine an empirical tunnel design. The 

success of these empirical design methods implies that all 

of the features used in the design process do actually 

influence tunnel stability. 

Pillar Design 

 There are many empirical pillar design approaches. 

Commonly used empirical design methods include those 

developed by Hedley and Grant [1972], Hudyma [1988], 

and Lunder [1994]. It is interesting to note that none of 

these empirical design approaches, based on more than 

17 mines and a wide range of rock mass properties, use 

any rock mass classification assessment as a factor influencing stability. These design methods rely only on the 

UCS of the rock, pillar geometry, and stress induced in the 

pillars. 

Stope Hanging Wall Design 

 Stope hanging wall stability involves large rock 

surfaces, often several thousands of square meters in 

extent. There are several empirical techniques used for 

estimating the stability and dilution of large stope hanging 

walls. The most commonly used empirical design methods 

are versions of the stability graph and dilution graph 

[Potvin 1988; Nickson 1992; Clark 1998; Capes et al. 

2005]. These design techniques have gained widespread 

application and rely upon a modified version of Barton’s 

rock quality Q classification system coupled with assessments of induced stresses, joint orientation and surface 

orientation, and hanging wall geometry. With this design 

method, neither the rock strength nor the induced stress 

influence the assessment of the relaxed, low-stress hanging 

wall condition. This indicates that the data collected for 

developing the design method were not sensitive to the 

induced stresses or rock strength [Wang et al. 2007]. 

CONCLUSIONS 

 Rock mass classification and characterization systems 

are the best tools available for assessing rock mass 

properties. They were designed to be easily used assessments of the properties believed to be most important for 

estimating the performance of excavations in rock. Most 

classification systems were originally developed for civil 

engineering tunnel design. The application of these systems has greatly increased to areas such as slope stability 

design and for providing rock mass property input for 

numerical modeling. Major problems in rock classification 

and characterization stem from their ease of use and their 

increasingly wide application. 

 The easy use of classification systems allows field 

engineers to quickly make support recommendations while 

a tunnel is being driven. This original goal for rock 

classification systems makes them insensitive to improved 

rock mechanics data. As an example, consider a clean, 

rough, and wavy joint surface. It would have a 

Q assessment of Jr/Ja equal to 3.0 and an RMR76

assessment of 20. Based on the original classification 

guidelines, field data estimating JRC, joint amplitude, and 

joint surface strength would have little or no influence on 

the classification values. Also, lab tests on discontinuities 

or even in situ shear tests would also have no effect on the 

classification values. Some attempts have been made to 

improve the sensitivity of classification systems to 

improved data, but this work has not become the industry 

standard, or even a widely recognized goal. 

 The process of determining a single number to represent a rock mass classification value necessitates that a 

weighting system be applied to assess the relative importance of rock strength, block size and geometry, and 

discontinuity strength. These weighting systems have 

proven to be effective for assessing support requirements 

and tunnel back stability. These classification systems now 

enjoy a wide range of applications and are assumed to be 

effective at almost any engineering scale and under a wide 

range of loading conditions. The lack of change in these 

weighting values over the last 30 years is a reflection of 

both the accuracy in their initial development as well as 

the difficulty in making changes to well-used classification 

schemes. A review of the weighting systems used in rock 

mass classification is needed. 

RECOMMENDATIONS 

 The individual properties of a rock mass should be 

assessed as accurately as possible. The relative importance 

of these individual properties should be determined empirically for a wide range of loading conditions, with a consideration of scale. Empirical data on pillar design suggest 

that intact rock strength is the only rock mass property that 

affects pillar stability for a wide range of rock classification values. It may be possible to make significant 

improvements to design if the individual properties of a 
118

rock mass are considered separately. We cannot expect to 

obtain good-quality rock mechanics lab and field data on 

rock mechanics properties unless the commonly used 

design tools can make use of these data. 

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119

WEAK ROCK MASS DESIGN FOR UNDERGROUND MINING OPERATIONS 

By Rimas Pakalnis, P.Eng.,1 Thomas M. Brady,2 Paul Hughes,3 Cristian Caceres,3

Andrea M. Ouchi,3

 and Mary M. MacLaughlin, Ph.D.4

ABSTRACT 

 A major focus of ground control research presently 

being conducted by the Geomechanics Group at the 

University of British Columbia, Canada, in conjunction 

with the National Institute for Occupational Safety and 

Health’s (NIOSH) Spokane Research Laboratory, is the 

development of design guidelines for underground mining 

within weak rock masses. The study expands upon the 

span design curve for man-entry operations and the stability graph for nonentry operations developed at UBC by 

extending the application to weak rock masses. The original database has been augmented by weak rock mass 

information from mines throughout the United States, 

Canada, Australia, Indonesia, and Europe. The common 

factor in all of these mines is the presence of a weak back 

and/or walls. This paper expands on the North American 

database and how the design curves have been employed at 

mining operations throughout the world. The definition of 

a weak rock mass for this study has been defined as having 

an RMR76 under 45% and/or a Q-value under 1.0. 

INTRODUCTION 

 A comparative analysis by the Mine Safety and Health 

Administration for Nevada gold mine operations for 1990–

2004 (Figure 1) shows that the number of injuries from 

roof falls in 13 Nevada underground gold mines ranged 

from a low of 8 in 1990 to a high of 28 in both 1995 and 

1997 [Hoch 2001]. This high injury rate was the prime 

motive for the initial study by NIOSH. The goal was to 

address the extremely difficult ground conditions associated with mining in a weak rock mass and provide mine 

operators with a database that could lead to a better 

understanding of the failure mechanism associated with 

mining within a weak rock mass. The database summarized in this paper is composed of seven [Potvin 1988] 

mines in Nevada and several others, which are summarized 

in Table 1. 

 A weak rock mass upon review of site conditions 

observed was identified as having an RMR76 less than 45% 

and/or a rock mass quality (Q) under 1.0. These values are 

largely described by Barton [2002] and Bieniawski [1976] 

as being “very poor” and “fair,” respectively, and are 

shown schematically in Figure 2. 

Table 1.—Weak rock mass database 

(RMR76 < 45%, Q < 1.0) 

East Carlin Mine (Newmont) ....... Nevada. 

Deep Post Mine (Newmont)........ Nevada. 

Midas Mine (Newmont) ............... Nevada. 

Rodeo Mine (Barrick) .................. Nevada. 

Turquoise Ridge Mine (Barrick) .. Nevada. 

Getchell Mine (Barrick)................ Nevada. 

Murray Mine (Queenstake) ......... Nevada. 

SSX Mine (Queenstake) ............. Nevada. 

Nye Operation (Stillwater)........... Montana. 

Eskay Creek Mine (Barrick) ........ British Columbia, Canada. 

Eagle Point Mine (Cameco) ........ Saskatchewan, Canada. 

Quinsam Mine (Hillsborough)...... British Columbia, Canada. 

Kencana Mine (Newcrest)........... Halmahera, Indonesia. 

 

 1

Associate professor of mining engineering, University of 

British Columbia, Vancouver, British Columbia, Canada. 

 2Deputy branch chief, Catastrophic Failure Detection and Prevention Branch, Spokane Research Laboratory, National Institute 

for Occupational Safety and Health, Spokane, WA. 

 3

Graduate student, Department of Mining Engineering, University of British Columbia, Vancouver, Canada. 

 4

Professor of geological engineering, Montana Tech of the 

University of Montana, Butte, MT. 

 Figure 1.—Injuries from rock falls in Nevada underground mining operations [Hoch 2001]. 
120 

 The mining methods practiced in Nevada are largely 

longhole mining, underhand cut-and-fill, and cut-and-fill. 

The underhand mining method relies upon an engineered 

back composed of rock/paste fill and is not addressed 

within this study, as mining under/adjacent to weak rock 

masses is largely negated. Underground mining methods 

as practiced in Nevada dictated which specific databases 

and stope design curves NIOSH would focus upon. Rock 

mass values were calculated during mine visits and varied 

from an RMR76 high of 70% to a low of 16% in goldbearing fault gouge. Several rock mass design curves 

developed by the Geomechanics Group at the University 

of British Columbia (UBC) [Pakalnis 2002] are available, 

but they were not thought to be relevant to the mining 

methods employed within the weak ground of Nevada gold 

mines and therefore not augmented. 

 Research began with visits to Nevada operators in June 

1999 to address concerns and determine where NIOSH 

would be able to assist. The first technical site visit was on 

June 12, 2002, and initial data were collected (Figure 1). 

The major objectives were to obtain information on weak 

rock masses and incorporate this information into existing 

design curves [Lang 1994] for back spans of manned 

entries and a stability graph [Clark and Pakalnis 1997] for 

longhole wall design for nonentry operations. The distribution of the original databases was based on Canadian mining data, as summarized in Figure 3, and shows the lack of 

data for weak rock masses. 

Figure 2.—Schematic showing transition of weak rock mass to stronger and existing databases.

Figure 3.—Distribution of the original database for back span (left) and stability graph (right).

Original Back Span Database (5) Original Stability Graph Database (7)
121 

SPAN DESIGN, MAN ENTRY 

 The initial span curve was developed by the UBC 

Geomechanics Group to evaluate back stability in cut-andfill mines. It consists of two straight lines that divide a 

graph into three zones: stable, potentially unstable, and 

unstable. The database for this graph initially consisted of 

172 data points from the Detour Lake Mine of Placer 

Dome, Inc., Ontario, Canada, with most of the points having RMR values in excess of 60% [Lang 1994]. The database was expanded to 292 observations in the year 2000 

with case histories from an additional six mines [Wang 

et al. 2000]. The successful use of empirical design techniques is based upon interpolation rather than extrapolation. Thus, a decision was made to develop a database 

for a critical span curve in weak rock masses. The term 

“critical span” refers to the largest circle that can be drawn 

within the boundaries of the excavation when seen in plan 

view (Figure 4). 

 The term “design span” refers to spans that have no 

support and/or spans incorporating a limited amount of 

local support (e.g., pattern bolting in which 1.8-m-long 

mechanical bolts are installed on a 1.2- by 1.2-m pattern). 

Local support is deemed as support used to confine blocks 

that may be loose or that might open or fall because of 

subsequent mining in surrounding areas. The Nevada study 

added an additional 44 observations to the span design 

curve as shown in Figure 4 and summarized by Brady 

et al. [2005], of which 35 had an RMR76 less than 45%. 

 The span design curve is used throughout North 

America. Three operations and their database are 

summarized in this paper: Stillwater Mining Co.’s Nye 

Operation in Montana, Barrick Gold, Inc.’s Eskay Creek in 

British Columbia, Canada, and Cameco, Ltd.’s Eagle Point 

Mine operation in Saskatchewan, Canada, with the design 

span curves shown in Figures 5–7, respectively. Of note in 

Figure 6, where Eskay Creek has established guidelines for 

support based on RMR as well as if conventional overhand 

mining versus underhand will be practiced as a function of 

rock mass and span. Figure 7 shows two data points within 

the unstable zone. In this area of the operations, these 

zones were observed to previously cave and required either 

increased support (dead weight) and/or mining employing 

“short rounds” in order to ensure stable conditions for 

subsequent mining. 

 Figure 4.—Critical span curve augmented with Nevada 

operations (Table 1) (45 observations).

NEVADA WEAK ROCK MASS SPAN DESIGN CURVE

(34 OBS) 

8

7

8

8

8

8

8

8 8

NEVADA WEAK ROCK MASS SPAN DESIGN CURVE

(34 OBS) (45 OBS) 

8

7

8

8

8

8

8

8 8

NEVADA WEAK ROCK MASS SPAN DESIGN CURVE

(34 OBS) 

8

7

8

8

8

8

8

8 8

NEVADA WEAK ROCK MASS SPAN DESIGN CURVE

(34 OBS) (45 OBS) 

8

7

8

8

8

8

8

8 8

 Figure 5.—Critical span curve at Stillwater Mining’s 

Nye Operation in Montana (292 observations).

 Figure 6.—Critical span curve at Barrick Gold, Inc.’s 

Eskay Creek Mine in British Columbia, Canada (292 observations).
122 

 A brief description of the use of the critical span curve 

is presented; more detail is outlined by Pakalnis [2002]. 

 Excavation stability is classified into three categories; 

each category is further divided into three subcategories. 

1. Stable excavation (S) 

a) No uncontrolled falls of ground have occurred. 

b) No movement of the back has been observed. 

c) No extraordinary support measures have been 

employed. 

2. Potentially unstable excavation 

a) Extra ground support has been installed to prevent falls of ground. 

b) Movement has occurred in the back. 

c) Increased frequency of ground movement has 

been observed. 

3. Unstable excavation (U) 

a) Area has collapsed. 

b) Depth of failure of the back is 0.5 times the span 

(in the absence of major structures). Within a 

weak rock mass, the depth of failure has been 

noted as 1 times the span and sometimes even 

greater. 

c) Limited local support was not effective in maintaining stability. 

 A minus-10 correction factor is applied to the final 

RMR when evaluating rock with shallow dipping or flat 

joints. However, the applicability of this factor in weak 

ground is being reassessed because of its amorphous 

nature. Where discrete ground wedges have been identified, they must be supported before employing the critical 

span curve. Stability is generally defined in terms of shortterm stability because the database is based largely on 

stoping methods that, by their nature, are of short duration. 

Movement of the back greater than 1 mm within a 24-hr 

period has also been defined as a critical amount of 

movement for safe access [Pakalnis 2002]. This value is 

also being addressed for weak rock masses as it applies to 

the initial database identified in Figure 4. This critical 

value may be much greater than 1 mm. 

STABILITY GRAPH METHOD: NONENTRY 

 The original stability method for open-stope design 

was based largely on Canadian operations and was proposed by Matthews et al. [1981], modified by Potvin 

[1988], and updated by Nickson [1992]. In all instances, 

stability was qualitatively assessed as being either stable, 

potentially unstable, or caved. Recent research at UBC has 

augmented the stability graph by using stope surveys in 

which cavity monitoring systems were employed [Clark 

and Pakalnis 1997]. This research has enabled the amount 

of dilution to be quantified. A parameter termed the 

“equivalent linear overbreak/slough” (ELOS) was introduced by Clark and Pakalnis [1997] and was used to 

express volumetric measurements of overbreak as an average depth over an entire stope surface. This has resulted in 

a design curve as shown in Figure 8. 

 A limited number of observations existed for RMR 

values under 45% (Figure 3). An additional 45 data points 

were added on the stability graph—nonentry from Nevada 

operations having an RMR under 45%. In addition, Mine 4 

(Table 1) reflects more than 338 observations that have 

been averaged to reflect the design points as discussed by 

Brady et al. [2005]. The stability graph relates hydraulic 

radius of the stope wall to empirical estimates of overbreak 

 Figure 7.—Critical span curve at Cameco, Ltd.’s Eagle 

Point Mine, Saskatchewan, Canada (292 observations). 

Figure 8.—Stability graph, after Clark and Pakalnis [1997].
123 

slough. Hydraulic radius is defined as the surface area of 

an opening divided by perimeter of the exposed wall being 

analyzed. 

 Equation 1 was used for calculating parameters for the 

database shown in Figure 8: 

N′ = Q′ * A * B * C (1) 

where N′ = modified stability number; 

 Q′ = modified Norwegian Geotechnical Institute (NGI) rock quality index [Unal 1983] 

where the stress reduction factor and joint 

water reduction factor are equal to 1, as 

they are accounted for separately within 

the analysis; 

 A = stress factor equal to 1.0 due to relaxed 

hanging wall; 

 B = rock defect factor. This value results from 

parallel jointing and the amorphous state 

of the weak rock mass being set to 0.2 and 

0.3, respectively [Brady et al. 2005]; 

and C = stope orientation factor as defined in 

Figures 5–7, i.e., C = 8 – 6 × cos φ (dip of 

hanging wall). 

 An initial observation from Figure 9 is that the classical design curves (ELOS) as shown in Figure 6 are 

inaccurate at low N′ and hydraulic radius values. If 

hydraulic radius is kept below 3.5 m in a weak rock mass, 

the ELOS value should remain under 1 m. It seems that a 

hydraulic radius under 3 m would not result in ELOS 

values much greater than 1 m. This result is being further 

evaluated. 

SUPPORT CAPACITY GUIDELINES 

 The development of support capacity guidelines is 

critical to the overall success of the mining method 

selected in terms of ensuring a safe workplace. Ground 

support in weak rock presents special challenges. Underdesign can lead to costly failures, whereas overdesign can 

lead to high costs for unneeded ground support. Figure 10 

depicts a classic wedge failure controlled by structure. It is 

critical to design for the dead weight of the wedge in terms 

of the breaking load of the support, as well as the bond 

strength associated with embedment length [Brady et al. 

2005]. 

Figure 9.—Wall stability graph as developed for Nevada operations (45 observations).

STABILITY NUMBER (N’)

NEVADA WEAK ROCK MASS - WALL STABILITY GRAPH

0 5 10

0.1

1

10

100

4 44

4

4 44

4

4

44 4

2

6

4

3

4

44

4

1

1

1

1

1

2

4 4 44 4 4

6

1 1

3

4 44

ELOS LEGEND

ELOS<0.5m

0.5m<ELOS<1.0m

1.0m<ELOS<2m

2.0m<ELOS ELOS=1m

ELOS>2m

ELOS<0.5m

HYDRAULIC RADIUS (m)

(45 obs)

STABILITY NUMBER (N’)

NEVADA WEAK ROCK MASS - WALL STABILITY GRAPH

0 5 10

0.1

1

10

100

4 44

4

4 44

4

4

44 4

2

6

4

3

4

44

4

1

1

1

1

1

2

4 4 44 4 4

6

1 1

3

4 44

ELOS LEGEND

ELOS<0.5m

0.5m<ELOS<1.0m

1.0m<ELOS<2m

2.0m<ELOS ELOS=1m

ELOS>2m

ELOS<0.5m

HYDRAULIC RADIUS (m)

(45 obs)
124 

 More than 400,000 Split Set [Brady et al. 2005] 

friction bolts are used in Nevada mines as primary support. 

Friction bolts are particularly useful in fissile, buckling, or 

sheared ground where it is difficult to secure a point 

anchor. Caution must be used with this method of primary 

support because of the low bond strength between broken 

rock and the bolt and because of the susceptibility of the 

bolt to corrosion. In Mine 4, Split Set bolts had a life of 6 

months because of corrosion resulting from acidic ground 

conditions. An analysis of the performance of friction bolts 

in mines with weak rock (as determined by RMR) needed 

to be conducted. With one exception, Nevada mines use 

39-mm Split Set bolts (the exception uses 46-mm Split Set 

bolts). Mines in Canada, however, use 33-mm Split Set 

bolts. Canadian mines generally use these bolts only in the 

walls and not in the back. The 46-mm bolts are common in 

Indonesian operations (Newcrest’s Kencana Mine) and 

Australian underground mines. 

Data points gathered from several pull tests in weak 

rock were plotted as shown in Figure 11, with bond 

strengths (SS39) shown for Mine 4 in Figure 12. The graph 

shows a strong trend between RMR and bond strength. 

 Variability in test results shows the difficulty in assessing overall support for a given heading. Thus, it is important that mines develop a database with respect to the 

support used so they can design for variable ground conditions. Factors critical to design, such as bond strength, 

hole size, support type, bond length, and RMR, should be 

recorded in order to determine where they lie on the design 

curve. Table 2 shows the design bond strengths determined 

through field testing at Newcrest’s Kencana Mine in Indonesia. Design values differed for grouted versus ungrouted 

split sets [Villaescusa and Wright 1997] and enabled one 

to assess the benefit with respect to alternative support, 

such as Swellex bolts. The Pm12 Swellex bolt provided 

8.6 t/m of bond for RMR76 values ranging from 25% to 

55%. These values compare similarly to design field 

strengths observed at Barrick’s Eskay Creek Mine in British Columbia, Canada. 

Table 2.—Bond strength SS46/Pm12: 

Kencana Mine, Indonesia 

Bond strength (SS46 mm) 

Type RMR76 Ungrouted 

(tonnes/m) 

Grouted 

(tonnes/m) 

I............ RMR > 55% 4.1 7.2 

II........... 35% < RMR ≤ 55% 2.6 5.8 

III.......... 25% ≤ RMR ≤ 35% 1.5 4.4 

Swellex Pm12 bond strength = 8.6 t/m (RMR 25%–55%). 

 Table 3 summarizes the updated support table incorporating the weak mass pull-test results conducted at 

operations throughout this database (Table 1). 

Figure 10.—Structurally controlled wedge.

Figure 11.—Pullout load versus RMR for SS39. 

Figure 12.—Pullout load versus RMR for SS39 (Mine 4). 
125 

MINING OPERATIONS GUIDELINES 

 With weak rock masses, blast control is critical to 

ensure that the weak rock mass is not further disturbed 

from overblasting. Guidelines for blasting based on RMR 

values for Queenstake’s SSX Mine and Barrick’s Goldstrike operation are summarized in Figures 13–14, 

respectively. In addition, the length of round pulled is 

related to the rock mass rating for a 5-m supported back 

span (Figure 13). For example, when the RMR is 15%–

20%, only a 1.2-m (4-ft) advance is possible; otherwise, 

failure of the unsupported back will result. Spiling is 

recommended at these RMR thresholds. Figure 14 shows 

the degree of loading of a development round at Barrick’s 

Goldstrike Mine for a 5-m by 5-m heading with respect to 

the RMR. 

DEPTH OF FAILURE 

 Recent parametric analysis employing discrete-element 

methods of numerical modeling of discontinuous materials 

were employed by MacLaughlin et al. [2005], whereby 

Table 3.—Updated support capacity 

p pp p y

Rock properties, tonnes Screen Bag strength, tonnes 

Bolt strength Yield strength Breaking strength 4- by 4-in welded mesh, 4 gauge 3.6 

5/8-in mechanical 6.1 10.2 4- by 4-in welded mesh, 6 gauge 3.3 

Split-Set (SS 33) 8.5 10.6 4- by 4-in welded mesh, 9 gauge 1.9 

Split Set (SS 39) 12.7 14.0 4- by 2-in welded mesh, 12 gauge 1.4 

Standard Swellex NA 11.0 2-in chain link, 11 gauge, bare metal 2.9 

Yielding Swellex NA 9.5 2-in chain link, 11 gauge, galvanized 1.7 

Super Swellex NA 22.o 2-in chain link, 9 gauge, bare metal 3.7 

*20-mm rebar, No. 6 12.4 18.5 2-in chain link, 9 gauge, galvanized 3.2 

*22-mm rebar, No. 7 16.o 23 

*25-mm rebar, No. 8 20.5 30.8 

No. 6 Dywidag 11.9 18.0 

No. 7 Dywidag 16.3 24.5 

Note: 4 gauge = 0.23-in diameter; 6 gauge = 0.20-in diameter; 

9 gauge = 0.16-in diameter; 11 gauge = 0.125-in diameter; 12 

gauge = 0.11-in diameter 

No. 8 Dywidag 21.5 32.3 Shotcrete shear strength = 2 MPa (200 t/m2)

No. 9 Dywidag 27.2 40.9 Bond strength 

No. 10 Dywidag 34.6 52.0 Split-Set, hard rock 0.75-1.5 mt per 0.3 m 

1/2-in cable bolt 15.9 18.8 Split-Set, weak ground 0.25-1.2 mt per 0.3 m 

5/8-in cable bolt 21.6 25.5 Swellex, hard rock 2.70-4.6 mt per 0.3 m 

1/4 by 4-in strap 25.o 39.0 Swellex, weak rock 3-3.5 mt per 0.3 m 

Note: No. 6 gauge = 6/8-in diameter; No. 7 gauge = 7/8- Super Swellex, weak rock >4 mt per 0.3 m 

in diameter; No. 8 gauge = 1-in diameter. 5/8-in cable bolt, hard rock 26 mt per 1 m 

NA = Not applicable. No. 6 rebar, hard rock 18 mt per 0.3 m, ~12-in 

 granite 

 Figure 13.—RMR versus round advance at 

Queenstake’s SSX Mine.

 Figure 14.—Loading of 5-m by 5-m face at Barrick’s Goldstrike Mine.
126 

UDEC5

 was employed to determine the depth of failure for 

a characterized rock mass ranging in RMR76 from 26% to 

77% with variable joint orientations. The results of the 

modeling showed that the failure mode was highly 

correlated with both the RMR value and joint condition. 

The depth of failure was largely found to be a function of 

drift geometry, with depth largely explained by the Unal 

[1983] relationship, as shown in Figure 15. This study also 

showed that the depth of failure was largely confined to 

0.5 times the span for RMR values modeled. 

 In practice [Unal 1983] it was found that for weak rock 

masses, arching the back dramatically increased the overall 

stability for a given span. This is schematically shown in 

Figure 16, whereby the potential wedge volume is significantly decreased by employing an arched back and the 

effectiveness of the applied support increased as a greater 

length of bolt passes beyond the failure plane. This has 

been shown to be a major contributing factor to the overall 

stability of mines operating within weak rock masses. 

 

5

Universal Distinct Element Code. 

CONCLUSIONS 

 The NIOSH Spokane Research Laboratory and the 

UBC Geomechanics Group are focusing on developing 

safe and cost-effective underground design guidelines for 

weak rock masses having an RMR in the range of 15%–

45%. Weak ground conditions, ground support, and 

mining methods used in several North American underground mines were observed. The RMR76 values were 

calculated to update the span design graph and the stability 

graph to weak rock mass conditions. The greatest benefit is 

the implementation of these design relationships and 

methodologies at the participating mines, as their relevance and ability to predict design requirements have been 

shown to be workable, safe, and cost-effective. 

 Variability in field conditions showed the difficulty in 

assessing overall support for a given heading. It is imperative that mines develop their own databases based on the 

type of support used in their mines so that unexpected 

ground conditions can be analyzed and mine stability predicted. The results from augmented design curves and 

pullout tests are presented in the hope that they will aid 

mine professionals in their task of designing a safe workplace. A systematic approach allows an operator to understand overall failure mechanisms and resultant loads that 

could affect the system. This approach would allow an 

engineer to develop an optimal support strategy for the 

mining method employed. 

 This work would not have been possible without the 

partnership between NIOSH, the UBC Geomechanics 

Group, and North American mining company personnel. 

This continued partnership is critical to the development of 

safe and cost-effective mine design strategies. Figure 1 

shows that since the inception of the team approach and 

resultant collaboration, injury statistics have declined 

dramatically in Nevada. This decline may be a result of 

many factors; however, it is clear that this approach is 

important and relevant to mine operations. 

ACKNOWLEDGMENTS 

 Specific thanks go to Patrick Carroll, chief engineer, 

Newmont’s Midas Mine; J. J. Hunter, chief rock mechanics engineer, Newmont’s East Carlin and Deep Post Mines; 

Monica Dodd, chief engineer, Newmont’s Deep Post 

Mine; Simon Jackson, chief engineer, Placer Dome, Turquoise Ridge Joint Venture; Shawn Stickler, chief ground 

control engineer, Queenstake’s Murray Mine; and Rad 

Langston, senior rock mechanics engineer, Stillwater Mining Co., Drew Marr at Barrick’s Eskay Creek Mine, Indra 

Febrian at Newcrest’s Kencana Mine, Kresho Galovich at 

Hillsborough’s Quinsam Mine, and Ken Dunne and Jan 

Romanowski at Cameco’s Eagle Point Mine. 

 Figure 15.—Modeled depth of failure as a function of 

RMR [Villaescusa and Wright 1997].

0

0.5

1

1.5

2

2.5

3

0 20 40 60 80 100 120

RMR

Depth of Failure / Span

Unal's formula

UDEC Model Data -

shallow joints

UDEC Model Data -

steep joints

Unal: Depth of Failure = (100- RMR) x SPAN))/100

0

0.5

1

1.5

2

2.5

3

0 20 40 60 80 100 120

RMR

Depth of Failure / Span

Unal's formula

UDEC Model Data -

shallow joints

UDEC Model Data -

steep joints

Unal: Depth of Failure = (100- RMR) x SPAN))/100

 Figure 16.—Illustration of reduction of wedge volume 

due to arching of back. Depth of failure has been approximated by 0.5 times the span. Note arch has removed the 

potential for dead-weight failure. 

(A) (B)
127 

REFERENCES 

 Barton N [2002]. Some new Q-value correlations to 

assist in site characterization and tunnel design. Int J Rock 

Mech Min Sci 39(2):185–216. 

 Bieniawski ZT [1976]. Rock mass classifications in 

rock engineering. In: Proceedings of the Symposium on 

Exploration for Rock Engineering (Johannesburg, South 

Africa), pp. 97–106. 

 Brady TM, Pakalnis R, Clark L [2005]. Design in 

weak rock masses: Nevada underground mining operations. SME preprint 05–43. Littleton, CO: Society for Mining, Metallurgy, and Exploration, Inc. 

 Clark L, Pakalnis R [1997]. An empirical design 

approach for estimating unplanned dilution from open 

stope hangingwalls and footwalls. Presented at the 99th 

Canadian Institute of Mining Annual Conference (Vancouver, British Columbia, Canada). 

 Hoch MT [2001]. Ground control seminar for underground gold mines (Elko, NV, Sept. 20, 2001). Sponsored 

by the Mine Safety and Health Administration, Roof Control Division, Pittsburgh, PA. 

 Lang B [1994]. Span design for entry type excavations. 

[Thesis]. Vancouver, British Columbia, Canada: University of British Columbia. 

 MacLaughlin MM, Pakalnis R, Brady TM [2005]. 

A distinct element parametric study of failure modes 

around an underground opening in rock masses of varying 

quality. In: Proceedings of the 40th Symposium on Engineering Geology and Geotechnical Engineering (Logan, 

UT). 

 Matthews KE, Hoek E, Wylie D, Stewart M [1981]. 

Prediction of stable excavation spans for mining at depths 

below 1,000 m in hard rock. Ottawa, Ontario, Canada: 

Canada Centre for Mineral and Energy Technology 

(CANMET), DSS Serial No. 0sQ80–00081. 

 Nickson SD [1992]. Cable support guidelines for 

underground hard-rock mine operations [Thesis]. Vancouver, British Columbia, Canada: University of British 

Columbia. 

 Pakalnis R [2002]. Empirical design methods: UBC 

geomechanics update. In: Hammah R, Bawden W, Curran J, 

Telesnicki M, eds. Proceedings of NARMS–TAC 2002: 

Mining and Tunneling Innovation and Opportunity 

(Toronto, Ontario, Canada, July 7–10, 2002), Vol. 1. 

 Potvin Y [1988]. Empirical open-stope design in 

Canada [Dissertation]. Vancouver, British Columbia, 

Canada: University of British Columbia. 

 Unal E [1983]. Design guidelines and roof control 

standards for coal mine roofs [Dissertation]. University 

Park, PA: The Pennsylvania State University. 

 Villaescusa E, Wright J [1997]. Permanent excavation 

reinforcement using cemented grouted Split Set bolts. In: 

AusIMM Proceedings (Carlton, Victoria, Australia), Vol. 1, 

pp. 65–69. 

 Wang J, Pakalnis R, Milne D, Lang B [2000]. Empirical underground entry type excavation span design modification. In: Proceedings of the 53rd Annual Conference, 

Canadian Geotechnical Society. 

129

APPLICATION OF THE Q-SYSTEM TO AUSTRALIAN UNDERGROUND METAL MINES 

By Warren A. Peck1

 and Max F. Lee1

ABSTRACT 

 The applicability of the Q-system [Barton et al. 1974] 

to Australian underground metal mines is discussed with 

reference to two common design issues: ground support 

for horizontal mine development, and assessing the stability of bored raises. 

 Installed ground support in mine development is compared to empirical estimates using the Q-system and 

associated support capacity calculations. Data are graphically presented from 59 specially selected sites at 

15 contributing mines. 

 The actual performance of large-diameter raise-bored 

shafts is also compared to empirical stability assessments 

using a modified version of Q (Qr, after McCracken and 

Stacey [1989]). Lower-bound Qr values are plotted against 

raise diameter for 47 selected sites at 23 mines in Australia 

and Papua New Guinea. 

 The influence on Q and Qr of some geotechnical 

aspects of the Australian landscape, the dynamic nature of 

mines (compared to civil construction), and occupational 

health and safety regulations are discussed. 

 Stability and support assessments that are based just on 

Q or Qr are not always conclusive. It is often necessary to 

consider other rock mass parameters, the regulatory 

environment, and risk issues. 

 These results are interim; further data collection and 

analysis are required with regard to comparing actual performance versus empirical assessments. 

INTRODUCTION 

 This study examines the applicability of two aspects of 

the Q-system [Barton et al. 1974] to Australian underground metal mines: 

• For horizontal development, actual installed ground 

support versus empirical predictions using the 

Q-system and associated support pressure calculations; 

• For raise-bored shafts, actual performance versus 

empirical predictions of stability using the modified 

version of Q published by McCracken and Stacey 

[1989]. 

 The data presented in this paper, with one exception, 

have been supplied by mine management with the understanding that individual mines would not be identified in 

any report or published paper. Individual cases are discussed in a way that would not permit their locations to be 

identified. 

AUSTRALIAN GEOTECHNICAL ENVIRONMENT 

 Several geotechnical aspects of mines and the Australian occupational health and safety (OH&S) environment have a significant influence on the stability of 

openings and ground support requirements. 

Deep Weathering 

 Australia is an old, stable, continental mass. It has 

undergone numerous climate changes without appreciable 

erosion or glacial removal of weathered rocks. Weak 

weathered rocks often extend to depths of up to 90 m 

below the surface. 

 Extremely weathered near-surface rocks can be 

stronger due to the deposition of silica, carbonates, and 

iron oxides to form “caps” of stronger materials. Below the 

harder caps, weathered rocks often form a variety of rocklike saprolites through to weak clays. Soil mechanics 

rather than rock mechanics principles are more applicable 

to some of these weaker materials. 

 While the present groundwater level is often near the 

base of complete oxidation, this may not be the same as 

the base of weathering. 

 With regard to shafts, weak near-surface rocks are 

often the biggest challenge for site investigations, design, 

and construction. Failures have been relatively common 

due to a generally poor understanding of their behavior, 

their highly variable nature, and risk-taking during construction. In some cases, high rock quality designation 

(RQD) ratings have been erroneously assigned to 

extremely weathered rocks that were neither hard nor 

sound and therefore should have been assigned a nominal 

RQD rating of 10%. Groundwater or wet materials are also 

often implicated in these failures. 

Alteration and Weak Sheared Contacts 

 Altered rocks and thick weak sheared contacts are a 

common feature of some ore body styles, e.g., volcanogenic lead-zinc deposits and hydrothermal copper or gold 

deposits. It is often necessary to mine access development 

 

 1Principal geotechnical engineer, AMC Consultants Pty. Ltd., 

Melbourne, Victoria, Australia.
130 

and stopes along these weak contacts. Some of the 

common joint infill minerals include quartz, carbonates, 

chlorites, sericite, talc, zeolites, clays (both swelling and 

nonswelling), and gypsum. 

 In the case of porphyry copper deposits, the associated 

alteration is typically more pervasive and associated with 

intense fracturing. The entire rock mass is often either 

silicified, carbonated, or sericitized, and weak joint infill 

materials are common. Normally, strong igneous rock can 

be decomposed to clay at depths of hundreds of meters 

below the ground surface, while the surrounding jointed 

rock may be recemented by the gypsum released in the 

hydrothermal alteration process. Graphite is a common 

(low-friction) joint infill material in carbonate-hosted base 

metal deposits. 

Weak Ultramafic Host Rocks 

 Soft, weak ultramafic rocks are a feature of Australia’s 

(Achaean) nickel mines. Their geologic history is varied 

and complex, as is their behavior, which is often significantly time-dependent. 

 A wide variety of soft, weak talc-rich ultramafic lavas 

are present due to serpentinization very soon after 

eruption, variable grades of metamorphism, possible 

carbonation or potassium metasomatism, and finally deep 

weathering. Some rocks contain the very water-sensitive 

mineral brucite (magnesium hydroxide). 

 Ground conditions are very challenging, especially as 

soft, weak ultramafics can abut much stiffer and stronger 

rocks. Ground conditions and ground support are often 

extreme. Significant squeezing has occurred in ultramafics 

as shallow as 250 m, but as the ores can have very high 

values, mining has, thus far, reached 1,400 m below surface in one mine. 

Groundwater 

 Although high groundwater pressures are not common 

in mines, groundwater is often acidic or saline. Both influence the longevity of installed support, which typically 

must have an effective life of 10+ years. Point corrosion or 

rusting of bolts, possibly leading to premature failure, is 

also assisted by the tendency of mine rock masses to crack 

and loosen with time, especially when they are adjacent to 

stoping areas. 

 The effective life of support can be extended by using 

galvanized elements, fully grouted bolts (resin or cement), 

or plastic sheaves or (very expensive) low-grade stainless 

steel support elements. However, experience has shown 

that none of these measures guarantees the long-term 

integrity and effectiveness of support. There is also, presently, no foolproof method of testing or monitoring the 

adequacy of acid- or salt-challenged bolts with time. 

High Horizontal Stresses 

 In contrast to stresses in other tectonic plates, in situ 

measurements of premining rock stresses in Australian 

mines have demonstrated large variations in principal 

stress magnitudes with depth (Figure 1). While the major 

principal stress is often horizontal, its orientation can also 

vary widely between local regions [Lee et al. 2006]. 

 Compared to similar mining provinces in eastern Australia and in other tectonic plates, anomalously high and 

deviatoric horizontal stresses are a feature of the Achaean 

Yilgarn Craton in the southwestern corner of Western 

Australia. This area hosts numerous gold, nickel, and 

copper-zinc mines, which currently stope to depths of up 

to 1,500 m. Mine openings typically have very high 

tangential stresses in development backs and shaft walls, 

often close to the strength of the rock mass [Lee et al. 

2001]. 

 Mining-induced seismicity is common in some of the 

deeper mines in strong, often jointed, stiff rocks due to 

both violent fracture through intact rock and shearing on 

 Figure 1.—Australian principal stress magnitudes 

versus depth. 
131 

structures. In stark contrast, some of the softer and weaker 

talc-rich rocks and schist tend to squeeze, even at shallow 

depths. 

Mining-induced Stress Changes 

 Stress changes around openings are not usually an 

issue in civil engineering projects, but they are an 

important feature of mining. Due to nearby stoping, an 

opening might first be subjected to high or excessive 

abutment stresses, then low confining stresses as the opening is shielded by stoping. Both can encourage local 

shearing on structures, with associated cracking of the 

intact rock, and dilation plus loosening of the rock mass. 

 The installed support must be able to accommodate all 

of the associated movements and loosening, yet still 

adequately support the rock mass. Areas affected by stoping can therefore often seem to be oversupported. 

Mining-induced Seismicity and Blast Damage 

 For deep mines or those considered to be prone to 

seismic damage, significantly more support is often 

installed. It usually comprises the following: 

• Fibercrete (minimum 50 mm) + rebars (backs) and 

friction anchors (walls); then: 

• Mesh + friction anchors (backs + walls); and then 

maybe: 

• An extra 50 mm of fibercrete. 

 The Q-system does not presently have a facility to 

assess ground behavior in potentially seismically active 

areas. Ground support design in such areas usually considers the toughness or energy absorption (kilojoules per 

square meter) capacity of the support system, rather than 

the support capacity of bolts in tonnes per square meter. 

 Vibration damage from large-stope production blasts is 

sometimes addressed in the same manner as seismicity. In 

addition to the support of credible or worst-case wedges 

defined by structures, it is usually sufficient to install mesh 

with friction anchor bolts in backs and walls that might be 

exposed to blast damage. 

OH&S Regulations 

 OH&S regulations and company policy often dictate 

the minimum support that must be installed, irrespective of 

ground conditions. 

 Under the “general duty of care” provisions of the 

Western Australian Mines Safety and Inspection Act 1994: 

• “An employer must, as far as practicable, provide a 

work environment in which employees are not 

exposed to hazards and provide information, instruction, training and supervision; 

• Employees must take reasonable care for their own 

safety and health, and that of others, at work; and 

• Self-employed persons must, as far as practicable, 

ensure their work does not adversely affect the safety 

and health of others.” 

 In terms of human exposure to possible falls of ground, 

guidelines relating to the above provisions imply that 

“nobody is allowed to work beneath unsupported ground,” 

no matter how competent the ground may seem. Mining 

companies and the governments of the other Australian 

states have generally adopted this policy. 

 In terms of ground support requirements, guidelines 

relating to the above provisions imply that “all development backs must be scaled or adequately supported down 

to a height of 3.5 m, unless a report by a competent person 

justifies otherwise.” After firing, development backs in 

Australian mines are now either routinely meshed or 

sprayed with a minimum 50-mm thickness of fibercrete, 

then bolted. 

 The above regulations and policies also influence the 

choice of excavation support ratio (ESR) when determining ground support using the Q-system chart. There is a 

growing awareness in Australian mines that the appropriate minimum ESR value is 1.3 for all human-access 

development, whether it is permanent or just a temporary 

stope access, because miners must travel and work in both. 

The only difference to the installed support in permanent, 

versus temporary, openings might be the use of galvanized 

support elements and fully grouted bolts to improve their 

longevity. Recognition of the limited life of ground support is not embodied into the existing Q-system chart. 

HORIZONTAL DEVELOPMENT 

 Prior to the early 1980s, development openings in most 

Australian mines were typically small, up to 4.5 by 4.5 m, 

and often manually mined and supported. Backs were also 

often flat, and only a few bolt types were available and 

used, mostly 2.4-m mechanical point-anchored bolts. 

Openings were routinely check-scaled, and mesh was only 

used in exceptional circumstances. 

 When decline access and large trackless equipment 

became popular, development was mined and supported 

using multiboom jumbos and their size increased to be 

nominally 5.5 by 5.5 m. But this small increase in development width implied a large increase in the required support 

capacity (approximately 50%), which was partly offset by 

arching the development backs. Friction anchor bolts 

(often referred to as Split Sets) also became popular 

because they are cheaper (per unit), easily installed using 

the jumbo, and they are an excellent bolt to pin mesh 

tightly to irregular development backs and walls. However, 

they have a much lower end-anchorage capacity than 

point-anchored solid bar bolts. 
132 

 Unless short friction anchors are used to just pin mesh, 

by inserting them in previously installed longer friction 

anchors, the minimum standard bolt length is typically 

2.4 m. Ground support typically comprises a mix of friction anchors, solid rebar bolts, and cable dowels. 

 Falls of ground became more common. OH&S regulations were reviewed. Mines also began to focus on the 

design and adequacy of ground support. 

 Barton’s Q database is dominated by civil engineering 

examples, not mining ones, where ground conditions can 

be more dynamic. It contains few, if any, cases from mining in Australia under the current legislative environment. 

 It is a significant challenge to provide adequate ground 

support for all of the diverse areas and situations in (underground metalliferous) mines all the time. Unlike most civil 

projects, ground conditions can change with time because 

of weathering and oxidation of minerals and the rock mass, 

variations in moisture content due to seasonal or ventilation changes, stress history, and/or damage to exposures 

due to nearby stope blasting, seismicity, etc. 

Previous Australian Studies 

 Mikula and Lee [2003] considered that “applying Q to 

a mine is like importing a knowledge database to a mine. 

Because the knowledge was compiled elsewhere, it should 

be confirmed to ensure relevance and correct use in the 

new environment.” They reported that Q is a suitable 

design tool for assisting ground support selection at the 

Mt. Charlotte gold mine, provided appropriate stress 

reduction factor (SRF) values are used and allowance is 

made for stress field anisotropy. 

 An unpublished 1999–2002 survey of 183 km of 

development headings with spans of 4.0–5.5 m in 20 Australian mine sites concluded that significantly more support 

was being used than is recommended by the Q-system 

tunnel reinforcement design chart (after Grimstad and 

Barton [1993]). Support usage was obtained from warehouse documentation, and Q values were averaged over 

several months of mining development headings. Q values 

ranged from 34 to 0.01. 

 The following examples indicate that significant 

amounts of support are being used at some Australian 

mines: 

• Development in ultramafic rock at depths in excess 

of 1 km, with average Q = 1.0 and a minimum Q = 

0.54. Support averaged 32 friction anchors (commonly called Split Sets) per meter advance plus 

75 mm of fibercrete. This is about twice the number 

of friction anchors suggested by the Q chart for Q = 

0.5, when the predicted number of solid bar bolts is 

converted to an equivalent number of friction 

anchors having the same support capacity. 

• Development in folded and faulted Paleozoic volcanics at depths in excess of 900 m, with Q ranging 

between 6.0 and 16.2. Support averaged nine friction 

anchors per meter plus 50 mm of fibercrete. The Q 

chart suggests that minimal support is required, and 

to satisfy OH&S regulations the minimum back support is only about seven anchors plus mesh per meter 

advance. 

 A major shortcoming of the 1999–2002 survey was 

that actual support patterns were not correlated with specific ground conditions and actual Q values. 

Applicability of Q in Horizontal Development 

 A significant concern with the way the Australian mining industry has used the Q versus the equivalent 

dimension design chart [Grimstad and Barton 1993] has 

been the assumption that “bolts” referred to in the design 

chart has included friction anchors. However, the bolts in 

the Q chart were nominal 20-mm-diam solid mild steel 

bolts, fully grouted using either cement or resin. 

 Fortunately, there is a growing awareness in the Australian mining industry that pattern bolting using only friction anchors often provides insufficient support for many 

situations, and supplementary solid steel bars are required. 

Data Collection 

 The 1999–2002 survey mentioned above used averaged Q values. As this approach can skew the data, it has 

not been used below. 

 General mine-wide databases of ground condition 

versus minimum support standards have also been ignored. 

It is was considered that company policy and government 

OH&S requirements dictate the quantity of ground support 

installed. This is especially a concern in good-quality rock, 

which might not technically require support to ensure the 

stability of some openings. 

 This survey uses specific Q values determined by 

mapping at the same location where the actual installed 

support was also recorded. To maximize the impact of the 

local ground conditions for support selection, numerous 

sites in the poorer rock classes were included, especially 

where the following was true: 

• The installed support was considered to be just sufficient for the ground conditions; or 

• There had been a fall of ground and the support had 

been upgraded. 

 The database contains 59 data sets from 15 mine sites 

drawn from all six Australian states and the Northern 

Territory. Nearly all of the data apply to 5.5-m-wide and 

variably arched development. 
133 

Installed Support Capacity Versus Q 

 The end-anchored capacity (at yield) of installed support was calculated in tonnes per meter squared. The 

following assumptions were used for the three commonly 

installed support types: 

• Friction anchors = 3 tonnes 

• Solid bar bolts = 15 tonnes 

• Cable dowels = 21 tonnes 

 No capacity allowance was made for mesh or fibercrete, as they rarely extend down the walls to invert level, 

often stopping about 3 m above floor level. Their main 

function is to retain loosened pieces of rock and transfer 

the weight of loosened blocks to the rock bolts. Except in 

areas prone to seismicity, both are often capable of supporting the deadweight of any loose material or small 

wedges that might develop between reasonably spaced bolt 

collars. 

 Q values are plotted against the installed support 

capacities in Figure 2. In four cases, two data points plot 

on top of each other. 

 While one might expect the installed support capacity 

to vary inversely with the Q values, there is significant 

scatter in Figure 2, particularly in the 30 data points at bolt 

capacities less than 3 t/m2

. Friction anchors were used at 

27 of these 30 sites. 

 The almost Australia-wide requirement (government 

OH&S regulations and/or company policy) for backs to be 

screened and supported (with either mesh plus bolts or 

fibercrete plus bolts) implies a minimum bolting pattern of 

either 1.1 m by 1.1 m or 1.1 m by 1.4 m for either 2.4-m 

by 2.4-m or 2.4-m by 3.0-m mesh, allowing for overlap. 

Thus, if only friction anchors are used, the minimum endanchored bolt capacity is in the range of 2–2.5 t/m2

. 

 The data in Figure 2 show that a bolt capacity of less 

than 3 t/m2 was used in some Australian mines for Q values ranging from 90 to 0.3, and the Q-system predicted 

support capacity is up to several times the actual installed 

capacity. The extent to which this is the result of substituting friction anchors for the solid bar bolts intended by 

Grimstad and Barton’s 1993 chart is unknown. 

 As only 3 sites in this group of 30 sites required 

rehabilitation, it is possible that local site experience has 

shown that some sites are sufficiently stable for mining 

purposes with less support than predicted by the Q-system. 

Alternatively, the Q values may have been optimistically 

estimated. 

 There is a large zone in Figure 2 where the installed 

support capacity is less than half the empirical predicted 

requirements. There is also a diagonal band where the ratio 

of actual to predicted support capacity ranges from 0.5 to 

<3.0. Finally, there is a zone where the ratio of the 

installed to predicted support capacity exceeds 3.0. 

 If Q values have been accurately assessed and if it can 

be assumed that the installed support is just adequate, the 

data in Figure 2 suggest that there is a tendency in the 

Australian mining environment for the Q-system to 

underestimate the support required for the “good” and 

“very good” rock classes, probably due to the OH&S 

considerations discussed above, and to overestimate 

support requirements for “poor” to “extremely poor” rock 

classes. 

Figure 2.—Installed support capacity versus Q.
134 

Installed Versus Predicted Support Capacity 

 Predicted support capacity, P, has been calculated in 

tonnes of required bolt capacity per square meter prior to 

bolt yield, using the following relationships involving Q, 

Jn , and Jr published by Barton et al. [1974]: 

 P = (20 Jn

1/2 Q–1/3) / 3 Jr (for fewer than three joint 

sets, Jn < 9) (1) 

P = (20 Q–1/3) / Jr (for three or more joint sets, Jn ≥ 9) (2) 

 Installed versus predicted support capacities are shown 

in 3 of the 15 surveyed mines (Figure 3). The cases where 

resupport was needed as part of a rehabilitation program 

have been highlighted. 

 It can be immediately recognized that only a few of the 

data points plot close to the line representing the condition 

where the predicted support requirements were matched by 

what the mine actually used. For ease of discussion, three 

general areas, or zones, have been delineated. 

• Zone 1 contains those data points where the actual 

installed support capacity is less than 3 t/m2

 and the 

Q-system predicted support capacity was less than 

12 t/m2. 

• Zone 2 contains data where the installed support is 

greater than predicted. 

• Zone 3 contains data points where the installed support is less than predicted. 

Zone 1 

 There are 23 data points within Zone 1, all of which 

have used friction anchors and 21 of which are sites 

shallower than 500 m depth. Only three sites plot close to 

the line, indicating that the predicted support capacity 

equals that actually used. Although the remaining 20 sites 

have predicted support capacity requirements up to five 

times the installed support capacity, only two of them 

required rehabilitation. This suggests that at depths of less 

than 500 m, local site experience should be used in 

conjunction with the Q-system to dimension ground support requirements for mines. 

Zone 2 

 There are 16 points in Zone 2, 12 of which are located 

at depths >900 m; 3 of the 12 have required rehabilitation. 

SRF values used by the site technical staff are regarded as 

being low, but SRF values suggested by strength versus 

stress relationships do not move these data points much 

closer to the line where the installed support capacity 

equals that predicted by the Q-system. All 12 sites experienced high stresses, and some experienced seismic events. 

 The remaining four data points in Zone 2 occur at 

depths of 200–550 m, and all required rehabilitation. The 

SRF values at three of them have been underestimated, but 

only in one case is the revision sufficient to get the 

predicted support capacity within 50% of that actually 

used. 

Figure 3.—Installed versus Q-predicted support capacities. 
135 

Zone 3 

 There are 12 data points in Zone 3 at depths ranging 

between 145 and 1,600 m, 7 of which required rehabilitation. This is not surprising at three sites where the installed 

support capacity was less than 25% of the predicted support capacity required. 

Suggested Modifications to Q-system 

Investigations 

 In a number of the cases already discussed, both the 

predicted support requirement and the SRF seem to have 

been underestimated. While underestimation of the SRF 

does not seem to be the sole reason for underestimating 

support requirements, it is significant. Peck [2000] discusses the problem of determining the SRF of highly 

stressed, jointed rock. The authors strongly recommend 

that practitioners calculate the support requirement using 

Barton’s 1974 equations (see Equations 1–2 above), particularly as friction anchors are not considered in the 

Grimstad and Barton 1993 chart. 

RAISE-BORED SHAFTS 

 Working in vertical openings is recognized as being 

more hazardous than horizontal development. Thus, there 

has been a concerted effort in Australia to reduce miners’ 

exposure to vertical openings [Minahan 1974]. 

 If a raise-bored shaft can be successfully completed 

without damage to, or loss of, the in-hole equipment, the 

potential savings over conventional shaft sinking can be up 

to about 30%. Unfortunately, raise boring in Australia has 

not been universally successful, and failures can be very 

expensive in terms of lost equipment and delays to production. Not only have some raises collapsed during reaming, 

some of the 310-mm-diam pilot holes have also been lost. 

Methods of reliably assessing the unaided stability of 

raise-bored shafts are therefore required. 

 The McCracken and Stacey [1989] method of assessing geotechnical risk for large-diameter raise-bored shafts 

has been widely used in Australia. It was successfully 

applied in 1989 to the planned Airshaft No. 5 at Broken 

Hill. The method predicted severe instability if raise boring 

was attempted at the planned 6-m diameter. Some overbreak was even predicted at a raise-bore diameter of 1.8 m. 

The method gained significant credibility when the predicted overbreak occurred during reaming at 1.8 m diameter prior to enlargement to 6.7 m by V-moling [Bennet 

and de Bruin 1993]. 

McCracken and Stacey Method 

 McCracken and Stacey [1989] applied the principles of 

the Q-system [Barton et al. 1974] to the problem of 

assessing raise-bore stability following the collapse of a 

number of large-diameter shafts during raise boring. The 

Q-system had developed a relationship between Q and the 

Maximum Stable Unsupported Span (MSUS). Additionally, the Q-system had established the ESR to account for 

different degrees of allowable instability based on excavation service life and usage. McCracken and Stacey used 

these concepts to develop the relationship between Raise 

Rock Quality (Qr) and the maximum stable raise diameter 

(Figure 4). Qr is based on the Q value obtained using 

Kirsten’s [1983] approach to determine the SRF, with 

further corrections to accommodate adversely oriented sets 

of discontinuities, weathering, and alteration. 

 Kirsten [1983] developed a relationship between the 

SRF and the extent to which the rock is overstressed. His 

approach calculates an SRF value for the ground stress 

condition and another value for rock mass loosening and 

uses whichever SRF value is greater. Peck [2000] published similar SRF equations for Australian conditions. 

 Figure 4 demonstrates that the required Qr value for 

stability significantly increases with increasing raise 

diameter. While a 2-m raise is likely to be stable in poorquality rock (Qr = 1 to 4), a 5-m raise requires fair-quality 

rock and a 6-m raise requires fair- to good-quality rock 

(Qr > 8). McCracken and Stacey also defined raise-bore 

rock quality in terms of block size (RQD/Jn) and low interblock shear strength (Jr/Ja). Their paper drew attention to 

the fact that problems may be expected in large-diameter 

raises if the critical parameter values for RQD/Jn and Jr/Ja

are poor or worse, using the guidelines they published. 

 Figure 4.—Qr, raise diameter and stability (after 

McCracken and Stacey [1989]).
136 

Stability and Standup Time 

 Ideally, raises should be located in rock and sized so 

that they are permanently stable. Unfortunately, this is not 

always possible, and progressively larger raises are being 

bored as equipment is being improved. 

 While the Q database contains no shafts or raises, it 

does include data for walls of caverns. Bored shafts and 

raises with their circular cross-sections are inherently more 

stable than planar cavern walls. Different ESRs might also 

be more applicable to shaft walls (often progressively 

exposed, manually supported, and then permanently lined) 

than bored raises, which must often be permanently stable 

immediately after exposure. 

 Standup time is also important. Where a raise has 

intersected a marginally stable horizon, several weeks may 

elapse before it can be supported, e.g., by either installing 

a lining, manually bolting the raise walls, or remotely 

spraying fibercrete. In some cases, prereinforcement of 

marginally stable sections is also possible. 

 Unstable rock excavations without support will collapse in time, ranging from less than an hour to more than 

a year. The time-dependent behavior of unstable rock 

masses is complex and as yet poorly understood. The Qsystem does not include any correlation between Q values 

and standup times. 

 Bieniawski [1993] published a correlation between the 

span of an opening, maximum standup time, and RMR 

value based on a study of a large number of mine openings 

and tunnels. Unfortunately, RMR values are not easily 

related to Q values, as not all of the same parameters are 

used to assess rock quality. For example, only Q gives a 

rating for the ground stress condition, while only RMR 

rates the persistence or continuity of the individual rock 

defects such as joints. 

 Bieniawski’s chart suggests that to stand unsupported 

for 6 months, a 3-m span needs an RMR of at least 58 and 

a 6-m span requires an RMR of at least 64. As the RMR 

system rates good rock as having values between 61 and 

80, fair to good rock is needed for 3- to 6-m spans to stand 

unsupported for 6 months. 

Lower-bound Geotechnical Conditions 

 The lower-bound Qr value is a key geotechnical 

parameter in the McCracken and Stacey method of determining the maximum diameter at which a raise can be 

reamed without exceeding the acceptable probability of 

failure. Unfortunately, they did not define the logging 

intervals, which is perhaps relative to the intended raise 

diameter, over which the lower-bound Qr value should 

apply. For example, a 1-m-thick sheared or blocky zone 

might give a very low Qr value, but when it is included 

with 4 m of good-quality rock, the average may be greater 

than the lower-bound Qr. The orientation and thickness of 

the sheared or broken zone are also important. A 1-m-thick 

sheared zone may be of no consequence if it is shallowdipping and confined between good-quality rock. Conversely, a thin, weak, and continuous steeply dipping structure within a poor zone may control significant unraveling. 

 Figure 5 shows two images from closed-circuit television monitoring of the walls of a recently completed and 

unlined raise. Figure 5A shows the relatively smooth raise 

walls in a section of the raise where no overbreak has 

occurred. Figure 5B shows the result of overbreak along 

joints. 

 Comparisons of preexcavation borehole logs with 

video camera inspections of completed raises has enabled 

estimates to be made of the minimum thickness of poorquality rock in otherwise good-quality rock, which is 

needed to destabilize the walls of a raise. 

 Figure 5.—No overbreak (A) versus overbreak (B) in a 

raise-bored shaft.
137 

 While thin shears and zones of blocky rock might produce some localized overbreak, zones of poor-quality rock 

need to be greater than 3 m to significantly impact the 

stability of raise walls. It is therefore recommended that 

core logging and analysis be done over lengths of about 1–

1.5 m. Raise stability assessments should then use “rolling 

average” techniques to average rock quality over 3-m 

increments, i.e., to calculate lower-bound Qr values. 

Stability Assessment 

 McCracken and Stacey stated that the preliminary 

geotechnical assessment should be aimed at determining 

the average and lower-bound geotechnical conditions: 

“The range and distribution of the raise-bore rock quality 

Qr, and the most important RQD/Jn and Jr/Ja parameters 

must be compared to the required minima for stability at 

the proposed shaft diameter.” It is not sufficient to simply 

look at the variation of MSUS with depth. 

 “At the preliminary evaluation stage the risk should 

only be deemed ‘acceptable’ if the tunnelling quality is 

consistently indicated to significantly exceed (i.e., be in the 

next class up from), the required quality throughout its 

length.” Their paper goes on to state that marginal cases 

occur where the indicated quality Qr either straddles the 

required value for stability or is not confidently known. 

 McCracken and Stacey also state: “In addition to 

simply assessing the range of predicted Qr values against 

those required, the rock mass properties and discontinuity 

orientations would be used as input to detailed stability 

analysis.” 

 Figure 6 presents a detailed analysis for a proposed 

large-diameter raise where the apparent dip is shown for 

every discontinuity that had not been rehealed. The core 

was obtained from a diamond drill hole bored down the 

proposed alignment of the raise and was oriented using the 

metamorphic foliation, which was known to have a consistent orientation in that part of the mine. 

 This example is from the 1989 analysis for Airshaft 

No. 5 at Broken Hill. The small joint-block sizes within 

some structures, the numerous steeply dipping joints with 

low-friction coatings, such as talc, and the rapid deterioration of some of the core in the core boxes clearly indicated 

the potential to collapse if bored at 6 m diameter. Overbreak occurred in the interval shown in Figure 6 when it 

was raise-bored at a diameter of 1.8 m. However, this 

overbreak was not sufficient to cause general collapse 

prior to enlargement of the raise by V-moling and lining. 

Presenting McCracken and Stacey Results 

 Figure 7 presents a typical plot of MSUS (expressed as 

a diameter) versus depth. Zones having small block size 

and low interblock shear strength have been identified. 

Two potentially unstable zones are identified, the first at 

the shaft collar (0–25 m) and the second between 60 and 

70 m. Both were reinforced prior to raise boring. A 1-mthick zone with an MSUS of only 2 m occurs at a depth of 

about 103 m. The Qr value in this zone is only 0.5, but the 

Qr values above and below it are of the order of 25. It was 

concluded that these zones would provide adequate stability for the thin weak layer. The raise was successfully 

bored at the planned 4-m diameter. 

Highly Stressed Rock 

 In highly stressed rock, induced rock fracturing can 

occur in advance of the reaming head and in opposite walls 

of the completed raise. The generation of loose slabs at the 

cutting face can mean overbreak of large blocks into the 

cutters. Then high and irregular torque on the drilling 

string is possible during reaming. 

 Stacey and Harte [1989] described this failure mechanism for raises at depths in excess of 2 km in southern 

Figure 6.—Detailed stability analysis for a proposed raise. Africa. They analyzed spontaneous fracturing ahead of the 
138 

face and derived a means of predicting its extent. O’Toole 

and Sidea [2005] demonstrated that significant fracturing 

was possible ahead of the raise-bore face at depths of 

880 m in Australia. 

 High or deviatoric horizontal stresses can mean very 

high and low stresses around the raise wall. Both can assist 

loosening of rock masses by local shearing and dilation on 

joints. Deep high-stress fracturing and overbreak are possible where wall stresses exceed the strength of the rock 

mass. Rock mass strength can vary with rock type and 

blockiness, but it is typically half the strength of intact 

rock [Lee et al. 2001]. 

 O’Toole and Sidea [2005] concluded: “[D]ue to the 

high risk of in-hole equipment damage and increased 

maintenance, the full costs associated with raise boring in 

highly stressed rock are likely to be significantly higher 

than reaming the equivalent strength rock in a low-stress 

environment.” 

 The use of stress/strength-based SRF values in the 

McCracken and Stacey method and the consequential 

reduction in Qr values should alert geotechnical engineers 

to the existence of potential high-stress issues. It is now 

common for raise-boring contractors to torque-limit their 

machines. It may occasionally be necessary to lower the 

cutting head and remove large slabs of rock from the 

cutting head whenever high torque demand is reported. 

 Postconstruction videos of raise walls are also becoming increasing common in Australia to compare predicted 

and actual performance. Selected wall support may be 

necessary and can be provided by remotely spraying 

fibercrete. 

Contrasting Face Conditions 

 When the raise-bore reaming head encounters a steeply 

dipping interface between rocks of contrasting strength, 

such as weak siltstone overlain by a steeply dipping strong 

sandstone, the head attempts to remain in the weaker 

material. This generates unbalanced forces that cause the 

head to tilt and the raise-bore drill rods to bend. Mechanical failure of the head or drill rods is possible, sometimes 

with the head plus rods falling to the bottom of the raise. 

 The McCracken and Stacey method does not provide 

any warning of this possibility. Other geotechnical investigations are required to complement Q-based analyses. 

Australian Raise Performance Versus 

Predicted Qr 

 A database of Australia raise-boring experience has 

been compiled and is plotted in Figure 8. It comprises 47 

data points of raise diameter, actual performance and 

component Q values for lower-bound Qr situations from 

23 mine sites in Australia and Papua New Guinea. All of 

the raises plotted are known to at least one of the authors 

who also had access to the site investigation reports and 

borehole logs. For consistency, the lower-bound Qr values 

data presented in Figure 8 were determined by the authors 

using a “rolling average” of 3-m increments, as described 

above. 

 The following trends are illustrated in Figure 8: 

• For lower-bound Qr values of less than 0.10, there is 

a high chance (9 in 10) of raise collapse or significant overbreak, irrespective of the proposed raise 

diameter. 

• For raise diameters between 3 and 6 m and if the 

lower-bound Qr value is between 0.1 and 1.0, raisebore performance ranges from stable to collapsed. 

A detailed stability analysis is recommended using 

the McCracken and Stacey method. 

• For a raise diameter of less than 5 m and a lowerbound Qr value greater than 1.0, there is an excellent 

chance of constructing a stable raise (10 stable and 

3 stable with support, out of 13). A detailed stability 

analysis should still be carried out if the rock structure rating (RSR) is greater than 1.3 for the desired 

raise diameter. 

 Figure 7.—Typical plot of MSUS (diameter) and critical 

rock quality. 
139 

• There are 11 data points on the unstable side of RSR 

= 3.0, including 5 collapsed raises. McCracken and 

Stacey considered there was a probability of failure 

of 1 in 4 for an RSR = 3.0. 

 The intermingling of collapsed and stable raises for Qr 

values between 0.05 and 1.0 and RSR values ≥2.0 demonstrates the need to acquire and closely consider additional 

geotechnical data for these cases. This was recommended 

by McCracken and Stacey where the proposed raise 

plotted on the unstable side of RSR = 1.3. 

CONCLUSIONS 

 Despite the harsh geotechnical environment in many 

Australian underground metal mines, there is a reasonable 

correlation between actual ground performance and Q and 

Qr values. However, Q and Qr are not always conclusive if 

considered in isolation from other rock mass parameters. 

While significantly less support is being used in some 

areas than is indicated by the Q-system, there are other 

areas where much more support is required than the Qsystem would indicate, e.g., deeper than 900 m below 

surface. 

 The calculated MSUS does not always indicate actual 

raise stability and additional investigation is required, 

particularly in cases of marginal stability. For these 

situations, greater emphasis should be given to the ratios 

RQD/Jn, Jr/Ja, and σc/σ1 and the possibility of timedependent behavior of some joint infillings, such as 

gypsum, chlorite, sericite, and talc. 

 Although there are several deficiencies for special 

cases, it is concluded that the Q-system is a suitable 

method of assessing rock mass conditions. It can be used 

to assess the likely stability of openings and the selection 

of ground support requirements, provided appropriate SRF 

values are used and other geotechnical parameters are 

considered in conjunction with Q and Qr. Local site 

experience is a valuable component of the process. 

ACKNOWLEDGMENTS 

 The authors wish to acknowledge the many and varied 

helpful inputs from our colleagues at AMC Consultants 

Pty. Ltd. We also wish to acknowledge the support of the 

management of the 23 mines that kindly supplied valuable 

data to this research and the contributions from our professional colleagues who are members of the Eastern 

Australia Ground Control Group (Marnie Pascoe, Chair; 

Andrew Campbell, Secretary). 

Figure 8.—Raise-bore diameter versus lower-bound Qr values. 
140 

REFERENCES 

 Barton N, Lien R, Lunde J [1974]. Engineering classification of rock masses for the design of tunnel support. 

Rock Mech 6(4):189–236. 

 Bennet AG, de Bruin NJH [1993]. Investigation and 

development of Pasminco No. 5 ventilation shaft, Broken 

Hill. In: Proceedings of the Eighth Australian Tunnelling 

Conference (Sydney, New South Wales, Australia). Australasian Institute of Mining and Metallurgy (AusIMM), 

pp. 55–63. 

 Bieniawski ZT [1993]. Classification of rock masses 

for engineering: the RMR system and future trends. In: 

Hudson J, ed. Comprehensive rock engineering. Vol. 3. 

Pergamon Press, pp. 553–573. 

 Grimstad E, Barton N [1993]. Updating the Q-system 

for NMT. In: Kompen, Opsahl, Berg, eds. Proceedings of 

the International Symposium on Sprayed Concrete. Oslo, 

Norway: Norwegian Concrete Association. 

 Kirsten HAD [1983]. The combined Q–NATM 

system: the design and specification of primary tunnel 

support. S Afr Tunnelling 6(1). 

 Lee MF, Mollison LJ, Mikula PM, Pascoe MJ [2006]. 

In situ rock stress measurements in western Australia’s 

Yilgarn Craton. In: Proceedings of the International Symposium on In Situ Rock Stress (Trondheim, Norway, June 

18–24, 2006). International Society for Rock Mechanics. 

 Lee MF, Pascoe MJ, Mikula PA [2001]. Virgin rock 

stresses versus rock mass strength in western Australia’s 

Yilgarn Greenstones. In: Proceedings of the Western 

Australia Ground Control Workshop (Perth, Western Australia, June 22, 2001). 

 McCracken A, Stacey TR [1989]. Geotechnical risk 

assessment of large diameter raise-bored shafts. Shaft 

Engineering, Inst Min Met, pp. 309–316. 

 Mikula PA, Lee MF [2003]. Confirmation of Q classification for use at Mt. Charlotte mine. In: Proceedings of 

the First Australasian Ground Control in Mining Conference (Sydney, New South Wales, Australia, November 

11–12, 2003), pp. 179–183. 

 Minahan PJ [1974]. The impact of raiseboring on a 

major mining operation. AusIMM Regional Meeting on 

Recent Technical and Social Advances in the North Australian Minerals Industry, Mount Isa. 

 O’Toole D, Sidea D [2005]. Recent developments in 

Australian shaft excavation practice: raise bored shafts in 

highly stressed rock. In: Proceedings of the Ninth Underground Operators’ Conference (Perth, Western Australia, 

March 7–9, 2005). Australasian Institute of Mining and 

Metallurgy (AusIMM), pp. 29–32. 

 Peck WA [2000]. Determining the stress reduction 

factor in highly stressed jointed rock. Aust Geomech 

35(2). 

 Stacey TR, Harte ND [1989]. Deep level raise boring: 

prediction of rock problems. In: Maury V, Fourmaintraux D, 

eds. Rock at great depth. Rotterdam, Netherlands: 

Balkema, pp. 583–588. 
141

ROCK MASS CLASSIFICATION IN GERMAN HARD-COAL MINING: 

STANDARDS AND APPLICATION 

By Holger Witthaus, Dr.-Ing.,1

 and Nikolaos Polysos, Dr. rer. nat.1 

INTRODUCTION 

 The classification system for German coal mining is 

the result of approximately 100 years of experience in 

roadway development and longwall mining. It is also 

based on different research projects covered by national 

and European research programs. 

 Over the past 30 years, more then 600,000 m of roadways have been driven and employed for mining activities. 

To properly describe the German rock mass classification 

system, therefore, it is useful to take a look at the main 

geomechanical preferences and common support systems. 

 The decision about the most effective development 

technique and support system is based on a synthesis of 

rock mass classification and geomechanical analysis. The 

properties of surrounding rock, the in situ stress, and the 

influence of mining activities in several seams at each German mine must be considered for the evaluation of the 

expected deformation of the roadway. 

 The mine layout, the requirements of ventilation and 

fire prevention, as well as the need to maintain emergency 

escape routes for the miners, require that the gate roads 

remain usable after the passage of the longwall face in 

most cases. Moreover, the gate roads must be maintained 

despite the high stresses that are applied during longwall 

retreat mining. Therefore, gate road design must address a 

broad spectrum of potential deformation environments. 

 In the past, when gate roads were supported solely 

with yielding steel arches, lithologic descriptions of the 

surrounding strata conditions were adequate for the dimensioning of support and the prediction of the roadway 

deformation. The current conditions of multiple-seam 

mining at depths of up to 1,700 m require combined 

support systems, including pattern bolting and backfilled 

steel arches. 

 Rock bolt support is used for development, after which 

(typically 50–100 m outby the face) the steel arches are 

installed and backfilled with building material (concrete) 

in order to achieve an optimized development rate. 

 The rock mass classification system described below 

was developed especially for the conditions of German 

hard-coal mining. It includes the stress distribution caused 

by multiseam workings (including crossing goaf edges of 

former longwalls), as well as in situ stresses due to great 

depth and the presence of tectonic faults. It is based on the 

evaluation of four parameters: 

• Geotechnical analysis of drill cores 

• Geotechnical observation of the development face 

• Geotechnical classification of tectonic structures 

(faults) 

• Standard classification derived from geotechnical 

assessment and evaluation of stress conditions 

(using numerical modeling for stress calculation) 

ROCK STRENGTH 

 One of the most important input parameters for 

describing strata conditions is the strength of the rock. The 

German classification system is based on a description of 

lithotypes. This method has been used successfully since 

the 1950s and is based mainly on the uniaxial compressive strength (UCS) of the material. 

 An evaluation of the rock strength observed in a 

survey of approximately 82,500 samples of rock core 

yielded the results shown in Figure 1. The three most 

common coal measure rock types are mudstone, siltstone, 

and sandstone. Each shows a specific mean UCS level and 

a different spread between the minimum and maximum 

values. Sandstones, in particular, have a wide spectrum of 

compressive strength, ranging from approximately 40 MPa 

to greater than 130 MPa, with an average of 85 MPa. The 

causes of this wide range include different sedimentological preconditions, facies, and digenetic processes. 

 

 1

Certified expert in geomechanics and support systems, 

Deutsche Steinkohle AG (DSK), Herne, Germany. 

sandstone

siltstone

mudstone

Percentage %

uniaxial strength MPa

sandstone

siltstone

mudstone

Percentage %

uniaxial strength MPa

 Figure 1.—Frequency distribution of uniaxial strength 

for typical lithotypes from German coal mines. 
142 

GEOTECHNICAL ROCK EVALUATION 

 The assessment of strata conditions, which can be 

conducted either by analyzing drill cores or by evaluating 

the development face, requires data on 14 parameters. Figure 2 gives an overview of this system of rock evaluation. 

Each parameter is rated as “poor,” “mean,” or “good,” 

depending on the characteristic that is being analyzed. For 

example, the intensity of stratification is rated according to 

the structure of bedding, as follows: 

• Good conditions: No significant stratification or 

no regular stratification; 

• Mean conditions: Some typical regular bedding 

areas; or 

• Poor conditions: Regular stratification of thin beds 

in a banded bedding structure. 

While parameters 1–8 and 13 can be determined either 

from drill core or from observations made in the headings, 

parameters 9–12 and 14 can only be obtained from underground observations. 

 The evaluation focuses on the rock properties to provide some idea of the rock strength, the character of the 

stratification, and the description of separation. These elements are used for description of the expected deformation 

of a roadway under the influence of high stress. 

Figure 2.—Matrix for geotechnical rock evaluation. 

 Geotechnical rock evaluation 

 

 1. Structure of bedding 

1. ug = massive 

2. ur = irregular bedded 

3. re = regular bedded 

4. we = alternate bedding with 

 Sandstone bands 

5. gb = banded 

 2. Bed thickness structure 

1. B100 > 0,80 [m] 

2. B80 0,80 - 0,60 [m] 

3. B60 0,60 - 0,40 [m] 

4. B40 0,40 - 0,20 [m] 

5. B20 0,20 - 0,10 [m] 

6. B10 < 0,10 [m] 

 3. Rock properties 

1. ko = compact 

2. ab = sanding (rubbing) 

3. ge = friable 

4. zr = fractrured/ sheared 

5. le = lettenlike/ mylonitized 

4. Character of separation plane surface 

 1. ra = rough 

2. FA = fossil separation planes 

 (e.g. plant layer, shell bank) 

3. ar = abrasion 

4. SH = slickenside surface 

5. KA = coaly layers 

 (e.g. vitrain, coal streaks) 

5.

Additional description of separation 

planes

 1. ag = stepped 

2. wl = undulating 

3. gg = bent 

4. gr = straight 

5. ha = conchoidal 

6. Character of separation plane 

 1. g = closed 

2. o = open 

3. k = cavernous 

4. z = fractured 

5. m = mylonitized 

7. Character of tectonic separation

 1. ra = rough 

2. e = straight 

3. ar = abrasion 

4. H = slickenside surface 

5. SpH = polished slickenside 

8. 

RQLD (Rock Quality Lithologic 

Designation) 

 

1. > 90 

2. > 75 

3. > 50 

4. < 50 

 5. > 25 

9. Degree of natural internal separation 

1. I 

2. II 

3. III 

4. IV 

 5. V 

10. Relative elongation of separation planes 

1. < 0,2 

2. 0,2 up to 0,4 

3. 0,4 up to 0,6 

4. 0,6 up to 8 

 5. 0,8 up to 1 

11. Relative degree of bedding 

1. > 1 

2. 1 to 0,5 

3. 0,5 to 0,3 

4. 0,3 to 0,2 

 5. < 0,2 

12. Relative degree of tectonic separation 

1. < 1 

2. 1 up to 2 

3. 2 up to 3 

4. 3 up to 4 

 5. 4 up to 5 

13. Water resistance 

1. No influence 

2. Debonding 

 3. Loosening 

4. Decomposition 

 5. Collapse 

14. Formation water 

1. dry 

2. moist 

3. wet 

4. dripping 

 5. running 

1 2 3 4 

 good mean poor 
143 

 The strata evaluation is conducted in three areas 

around a roadway: 

• The floor area (0–6 m below the roadway); 

• The mined strata (between floor and roof of the roadway); and 

• The roof area (0–6 m above the roof of the roadway). 

For larger roadways, with widths of more than 6 m, the 

area evaluated is increased to a distance equal to the roadway width above and below the roadway. 

RATING STRUCTURAL FAULTS 

 The typical German hard-coal deposit includes a lot of 

tectonic faults. The panel layout has to consider these 

features, but, in some cases, it is not possible to avoid having a longwall cross a fault. Experience has shown that 

faults can cause a wide range of effects on the mining 

process and the supports. The rating matrix shown in Figure 3 was developed to evaluate faults. The fault classification is based on a geometrical description of the fault itself, 

together with underground observations of the separation 

and strength of surrounding strata. 

 The objective of the fault classification is to provide an 

idea of the consequences for the roadway, in terms of the 

expected deformation and support requirements for the 

face and the face entry T-junction, in the area of the tectonically disturbed strata. 

ROCK MASS CLASSIFICATION USING 

BOREHOLE GEOPHYSICS 

 Data collection for classification includes applied geophysical methods. Borehole geophysics provides information about the lithologic and physical parameters of the 

strata. 

 The most important geophysical logs include natural 

gamma, density, electrical resistance, seismic velocity and 

reflection, acoustic imaging, and caliper. By combining 

these logs and processing them together with information 

about the lithology, it is possible to obtain data on the 

structure of the rock mass, the elastic parameters of rock, 

and the location and properties of weak areas. Table 1 

gives an overview of the criteria used in the geophysical 

classification of coal measure strata in German coal mines. 

EVALUATION AND REPORTING OF 

GEOTECHNICAL PARAMETERS 

 The strata assessment method described above allows 

a comparison between geotechnical data derived from drill 

core and underground observations made in the headings. 

The results can be used to optimize the support system. In 

addition, the universal character of the classification 

method allows for a broad range of applications, including: 

Mine:

Exploration:

Drilling depth/roadway section:

Fault type/displacement:

Good Medium Poor

Strike relative to direction of drivage perpendicular diagonal parallel

Dip relative to direction of drivage in against transverse

Width of fault < 1 W 1 W > 1 W

relative to roadway width (W) 

Fault characteristic Slickenside 

surface

Fractured zone Gouge zone

Tectonic stress of rock strata low medium strong

Fault associated structure parallel diagonal perpendicular

Structure characteristics Slickenside 

surface

Fractured zone Gouge zone

Water delivery dry moist trickling

Seam distance W heading face W floor W roof

Potential caving ≤ 1⁄4 W Up to 1⁄2 W ≥ 1 W

Degree of rock separation II III IV

(ahead of fault) 

Degree of rock separation II III IV

(beyond fault) 

Rock strength (N/mm2) ≥ 67 66–41 ≤ 40

Roof 

Heading face 

Floor 

Figure 3.—Geotechnical fault rating matrix. 
144 

• Estimation of critical support loads; 

• Analysis of strata movement after development and 

during retreat mining activities; and 

• Roof control in the face and in the area of face end 

T-junction.

 The goal of geological and geotechnical description is 

to quantify the relevant rock properties. The description 

has to be coordinated with the mining activities and roadway development, and it has to include the survey by drill 

core analysis. A special method based on COREDAT software was developed for German coal mining, taking into 

consideration the parameters shown in Figure 4. The form 

provides a matrix of description that includes the orientation of separation planes, a geotechnical description of 

lithological elements and stratification, and different 

classification elements for the bedding separations. 

 In addition to the core description, the geotechnical 

parameters can be derived from an evaluation of a 

development face using the form shown in Figure 5. The 

evaluation includes the amount of caving in the roof and 

sides, the lithotypes observed, the presence of separation 

planes, and relevant geometrical data. The degree of separation can be classified very simply by measuring the 

spacing between the lithologic and tectonic separation 

planes. Another important piece of information is the 

inclination and orientation of jointing. This information is 

used to estimate the maximum support loads from 

potential wedges within the sides and roof of the roadway, 

and it can be compared to the output from the drill core 

analysis. 

Figure 4.—Form of geotechnical analysis from drill cores by COREDAT software (DMT). 

core

Section Stratification

Map view Compressive 

strength

Separation planes Character Rock properties / core Separation planes Slickensides Character Mineralization Bedding planes Stratification Character of core drill

Tensile

strength

Table 1.—Criteria for geophysical classification of coal measure rock in German mines 

Classification 

Good Mean Poor 

Caliper (mm)............................................. 127–128 127–135 130–135 

Stiffness modulus M (GPa) ...................... 90–95 40–50 <40 

FEL (Ωm).................................................. 1.5–1.9 1.3–1.5 <1.4 

DipLog Spur 1........................................... <9,000 (FEL > 1.6 and 

reflectivity >2,000) 9,000–11,000 >11,000 (FEL <1.3 and 

reflectivity <1,700) 

BHTV reflectivity....................................... 1,700–2,500 1,300–1,700 <1,300 

dtvp (μsec/m)............................................ 260–230 260–230 >250 

dtvs (μsec/m)............................................ 350–410 450–410 >430 

Density (g/cm3

) ......................................... 2.6–2.7 2.5–2.6 2–2.5 
145 

Figure 5.—Form for geotechnical observation of a heading front. 
146 

 The form shown in Figure 5 was developed to complement basic geotechnical engineering and allows a comparison to the information obtained from the survey. The 

combined data set gives a detailed overview of the rock 

properties in, above, and below the roadway section. 

 The synoptic imaging of an analyzed geophysical drill 

core is shown in Figure 6. The meaning of each curve is 

described in the figure. Taken together, these data provide 

the basic information needed for geological engineering, 

design of panel layouts, dimensioning of roadway support, 

and support of the face T-junction. 

Column No. Heading note Description

1 Core Core description 

2 (composite log) Porosity - sand, clay, coal 

3 Diplog Dipping 

4 Hdev Horizontal orthographic deviation 

5 Fscore Fissures / m 

M Stiffness modulus 

E Elastic modulus 

K Compression modulus 

6 

U Poisson - rate 

BHTV ref Reflectivity from BHTV measurement 

Dip 1 resistance Diplog Pad 1 

Cal borehole caliper 

7 

FEL Focused Electric Log 

8 dtvp Run duration P – wave 

dtvs Run duration S – wave 

9 GR Gamma ray 

Density Density 

Figure 6.—Geophysical drill core analysis. 
147 

RELIABILITY OF GEOMECHANICAL PARAMETERS 

 In geomechanical planning work, it is important to 

know how reliable the data are for the specific strata being 

evaluated. The probability of occurrence is mainly influenced by the lithofacies, the tectonic conditions, and the 

density of survey. When the degree of reliability is known, 

a risk analysis can be conducted by analyzing each measured input parameter. 

 Table 2 shows a reliability ranking based on the density and quality of the survey. The five classes reflect the 

different levels of reliability and give a simple scheme for 

assessing geologic and geotechnical information. Combining the information from boreholes and roadway observations normally leads to a reliability rating of “confident” or 

“probably.” 

Table 2.—Influence of exposure-density 

on probability of occurrence 

 Probability of occurrence Distance of 

exposures 

1 .......... Confident 

Margin of error 10% 

Probability of occurrence 90% 

Up to 200 m. 

2 .......... Probably 

Margin of error 20% 

Probability of occurrence 75%–90% 

Up to 300 m. 

3 .......... Potential 

Margin of error 30% 

Probability of occurrence 50%–75% 

Up to 400 m. 

4 .......... Indicated 

Margin of error >30% 

Probability of occurrence 30%–50% 

Up to 500 m. 

5 .......... Supposed 

Margin of error >50% 

Probability of occurrence <30% 

More than 200 m.

 For any specific pattern of boreholes, the need for 

additional boreholes can be determined by evaluating the 

characteristics of the deposit and the longwall panel layout 

with respect to the sedimentologic analysis and lithofacies. 

This is particularly important when dealing with layers of 

sandstone whose thickness can change over short 

distances. 

 The result of the geomechanical classification is only 

as good as the quality of the information on which it is 

based. An important part of the process is to identify the 

remaining risk and manage it. Therefore, the current 

geomechanical planning standard is designed not only with 

the aim of defining the operational required parameters, 

but also to point out the risks that could arise during the 

development and use of the roadways. The procedure 

provides a basis for the design of support and the engineering of reinforcement measures. 

ROCK MASS CLASSIFICATION 

 The German rock mass classification system includes 

both geotechnical rock analysis and geomechanical prediction of stress and roadway convergence. The prediction 

of the stress level is based on numerical modeling of the 

stress field under the influence of multiple-seam mining. 

A system of equations for predicting convergence and 

roadway deformation during development and longwall 

mining is based on empirical analysis of measurements 

that have been collected since the 1960s. 

 The most important issue for classification is that it 

must be flexible enough to adapt to a broad range of mining scenarios (e.g., panel design and dimensioning of support). It must also be able to adapt to the different types of 

input data that are available from drill cores and underground observations. Since the rock rating system can use 

data from either drill cores or underground observations, 

it provides maximum flexibility in advance of mining for 

optimization of support or panel layout. 

 Drawing upon mining experience gained in the past 

5 decades, the German rock mass classification system 

represents a compromise between practicality and the best 

possible characterization. The parameters that are included 

are sufficient to represent the actual geological and rock 

mechanics conditions, and they can be determined from 

the currently available technical survey methods. 

 The rock rating clearly identifies the critical combination of geotechnical parameters within a large quantity of 

data. However, using it requires experience and a multidisciplinary knowledge. In doing so, one has to recognize 

that each parameter has different influence on the different 

mining tasks. The rating system developed by Deutsche 

Steinkohle AG (DSK) contains 21 parameters, each of 

which is evaluated individually. 

 Figure 7 shows how the process works. Each parameter is given one of five rating levels (A through E). 

Associated with each individual rating is a numerical 

evaluation index. The 21 individual evaluation indices are 

then summed to obtain an overall rating for the rock. 

Depending on the overall rating, the rock is classified into 

one of five types, ranging from “stable” to “squeezing,” 

as shown on the bottom line of Figure 7. The rating for the 

example in Figure 7 is 535.5, which is considered 

“squeezing” rock quality. 
148 

 The evaluation is done continuously, and the results 

are merged into a single rock quality class for the analyzed 

section of roadway during drivage. The aim of the rock 

rating, besides determining the rock quality class, is to 

define the support class. Both the rock quality and support 

classes are dependent on mining technology and excavation technique. Increasing rock quality means lower 

support requirements. Depending on the required support, 

it may be possible to optimize the support installation 

timing, for example, by installing temporary bolting at the 

time of initial installation and completing the bolting pattern later with the setting of long tendons. The rock quality 

Figure 7.—German mining standard rock mass rating matrix. 

Rock Classification 

 

No

. 

Parameters A Index B Index C Index D Index E Index

1 Bonding strength 

Roof [N/mm2] 80 4.6 60 9.3 45 18.5 30 37 ≤25 74 

2 Bonding strength 

Face [N/mm2] 80 4.1 60 8.3 45 16.5 30 33 ≤25 66 

3 Bonding strength 

Floor [N/mm2] 80 3.7 60 7.3 45 14.5 30 29 ≤25 58 

4 0.4 Bs R 4.5 0.6 Bs R 9.0 0.8 Bs R 18.0 1 Bs R 36 >1 Bs R 72 

5 0.4 Bs F 3.9 0.6 Bs F 7.8 0.8 Bs F 15.5 1 Bs F 31 >1 Bs F 62 

6 

Drivingpressure 

 [MPa] 0.4 Bs Fl 3.3 0.6 Bs Fl 6.5 0.8 Bs Fl 13.0 1 Bs Fl 26 >1 Bs Fl 52 

7 Course and no. of 

working boundaries 0 4.1 FAB 8.3 PAB 16.5 SAN 33 PAN 66 

8 Distance to bound. of 

overlying workings 

 [m] 

≥350 4.4 >200 8.8 >100 17.5 >50 35 ≤50 70 

9 Distance to bound. of 

underlying workings 

 [m] 

≥350 4.5 >200 9.0 >100 18.0 >50 36 ≤50 72 

10 Age of working 

boundaries ≥10 

years 

4.5 ≥5 years 9.0 ≥2 years 18.0 ≥1 years 36 during 

driving 72 

11 Distance between 

fault and roadway ≤4B 3.0 ≤3B 6.0 ≤2B 12.0 ≤B 24 ≤0.5B 48 

12 Distance between fold 

and roadway ≤4B 2.5 ≤3B 5.0 ≤2B 10.0 ≤B 20 ≤0.5B 40 

13 Seam distance roof ≤3B 4.1 ≤2B 8.3 =B 16.5 ≤0.5B 33 ≤0.25B 66 

14 Seam distance floor ≤3B 2.5 ≤2B 5.0 =B 10.0 ≤0.5B 20 ≤0.25B 40 

15 Bed thickn. structure thickbedded 2.7 bedded 5.3 laminated 10.5 thin-lamin. 21 flaky 42 

16 Character of separat. 

plane surface irregular 2.2 undulating 4.3 even 8.5 slickensided 17 polished 34 

17 Degree of natural 

internal separation I 3.2 II 6.3 III 12.5 IV 25 V 50 

18 Slickensided/ 

“Lösen”-surfaces ≤2B 3.0 ≤B 6.0 ≤0.5B 12.0 ≤0.25B 24 In roadway 

crosssection 

48 

19 Room to move of 

jointed rock body 

relative to roadway 

joints 

and 

bedding 

visible 

3.2 

along 

bedding 6.3 

along bed. 

and 1 joint 

direction 

12.5 

along bed. 

and 2 joint 

directions 

25 

Along bed. 

And several 

joint 

directions 

50 

20 Formation water dry 4.4 moist 8.8 wet 17.5 dripping 35 running 70 

21 Resistance to water not 

soluble 

2.7 sanding 5.3 loose 10.5 deconsolid. 21 Disintegrate

d 

42 

Remarks ∑Indices : 535.5
149 

can also indicate the maximum allowable distance between 

the face and where the arches must be backfilled, which 

can help optimize the mining method to achieve the best 

development rate. 

APPLICATION OF GEOTECHNICAL ANALYSIS 

AND ROCK MASS RATING 

 Today, the German rock mass rating system is used in 

geomechanical planning work for design of development 

headings, selection of support classes, and risk management. Rock mass rating is an important element of a closed 

loop of planning work for strata control. This closed loop 

was defined in the 1970s for German coal mining when it 

was recognized that optimized planning is based on performance review by monitoring. Another aspect is the 

successful development of planning tools and support systems. Both require a performance review because they are 

based on empirical processing. 

 Operational experience with the application of the rock 

mass rating system gives the results summarized in 

Table 3. A rating of up to 131 points indicates stable rock 

quality. Only minimal roadway deformation is expected, 

and just a few local displacements are likely. The separation planes, either joints or bedding planes, are closed and 

maintain high frictional strength. These conditions require 

the lowest level of support system with, in principle, only a 

need for lagging to prevent small pieces of rock from falling out of the roof. Unfortunately, these conditions are seldom encountered in German hard-coal mining. 

 The next class of rating, between 132 and 264 points, 

indicates caving rock quality. Poor rock quality is designated as “friable” (264–521) or “squeezing” (>521). 

Increasing roadway deformation that starts within the 

heading process must be taken into account. In most cases, 

combined support systems with both rock bolt systems and 

additional backfilled steel arches are used for roadway 

support. 

 The rock mass rating has to be interpreted for different 

assignments of tasks. Looking at the roadway support for 

gate roads, for example, a rating of 434 is the limit of the 

applicability of backfilled steel arches as exclusive 

support. Below this rating, for rectangular starting rooms, 

it is typical to employ combined support with rock bolts 

and additional steel canopies and hydraulic props. For 

detailed design of support patterns in this range, it is 

necessary to take a close look at the geomechanical 

parameters. 

 As the rock mass rating increases, the quality of the 

rock decreases. This results in an increasing effort for 

roadway support. Table 4 gives some examples of the rock 

bolting densities required in different rock qualities. The 

example shown is for an arched-shaped roadway with a 

width of 6.4 m and a height of 4.5 m. 

Table 3.—Rock classification and rock types 

Rating index Class Rock type 

Up to 80.................. Ia 

Up to 131................ lb 

Stable rock: 

Local displacement, closed joints 

and bedding elements (separation 

planes) 

Up to 196................ lla 

Up to 264................ llb 

Caving rock: 

Local displacement and sporadic 

caving areas up to decimeter size in 

the roof and the upper sides, 

particular separation planes 

Up to 304................ llla 

Up to 347................ lllb 

Friable rock: 

Increased separation results in 

displacements and caving up to 

meter size, separation planes 

pronounced and partially opened 

Up to 434................ lVa 

Up to 521................ lVb 

Very friable rock: 

High density of jointing and 

intensive transaction results in 

regular displacement caving up to 

1 m sliding gravity wedges 

Up to 621................ Va 

>621 ....................... Vb 

Squeezing rock: 

Local gouge zones and squeezing 

areas, opened separation plane, 

high density of separation and 

intensive transaction, loosening of 

strata, and high mobility of gravity 

wedges 

Table 4.—Required bolting density for support depending on rock mass quality 

Rock mass quality Bolting density in bolts/m2

of bolted roof and side 

Number of bolts per meter 

of roadway length 

Support resistance of 

bolting pattern (kN/m2

) 

Stable ........................... 0.8 6.5 231 

Caving ........................... 1.3 10.5 370 

Friable .......................... 1.6 13 463 

Very friable .................... 2.0 15.6 552 

Squeezing ..................... 2.4 19 678 
150 

 In this case, for stable rock (i.e., competent strata condition), a bolt density of approximately 0.8 bolts/m2

 of 

arch peripheral area is specified. This bolt density is only 

one-third of that needed for the squeezing rock quality, 

which requires approximately 2.4 bolts/m2

. 

 In Figure 8, the range of common support classes for 

German coal mines is assigned according to the rock mass 

rating and rock quality. The increasing effort required for 

support is clear in this figure. An optimized roadway 

development rate can be achieved with a multiphased 

support installation. The sequential installation of different 

support measures behind the face requires quite good rock 

conditions. In contrast, poor conditions require immediate 

support during development. 

CONCLUSIONS 

 The German rock mass rating system for hard-coal 

mining has been developed particularly for application in 

multiple-seam mining at great depth and for use with a 

variety of support systems. It takes into account the service 

function of the roadways, including not only retreat longwall mining, but also reuse of the roadways after the passage of one longwall face. 

 The system is a compromise between the best possible 

rock mass description and the practical limitations of 

available measurement methods. Because the aim of 

classification is to provide the basic information needed 

for dimensioning of support, it takes into account both the 

in situ stress and the stresses caused by multiple-seam 

mining activities. Basic experiences from German hardcoal mining are included in the system through lithologic 

descriptions. 

 The system has been routinely applied in all DSK 

mines for the past 3 years. The class number determined 

by the rock classification provides the essential information needed for a detailed support design. It also provides 

an opportunity for comparing different seam conditions 

across borderlines between mines and panel layouts. The 

classification is an important addition to the descriptive 

geologic parameters. 

 The class number also gives a sense of rock quality 

and, therefore, it helps in assessment support requirements. 

However, it is not possible to predict roadway convergence 

with a single number, particularly for the later phases of 

roadway use. This means that it is still necessary to 

analyze the classification parameters and measure deformation to determine the maximum amount of convergence 

during roadway use. It is important to consider that the 

German method of mining employs single roadway 

systems and requires reuse of the roadway after passage of 

the face. 

Figure 8.—Rock mass quality and support measures. 

≤ 196 stable – caving 

> 434 very friable – squeezing 

≤ friable – very friable 434 

Support class/ Rock mass quality support system

Rock mass 

rating 

Phase 1 Phase 2 Phase 3 

Phase 1 Phase 2 Phase 3 
151 

 Every time the standard is used, the classifying parameters from drill cores and roadway observations allow a 

retrospective evaluation of the main factors influencing 

support performance. This information is documented and 

maintained in a central data pool for knowledge management within DSK and, therefore, serves as an important 

tool for future support designs. 

BIBLIOGRAPHY 

 Everling G, Meyer A-G [1972]. Ein Gebirgsdruckrechenmodell als Planungshilfe (in German). GlückaufForschungshefte 33:81–88. 

 Jacobi O [1981]. Praxis der Gebirgsbeherrschung 

(in German). 2. Aufl. Essen, Germany: Verlag Glückauf. 

 Junker M, et al. [2006]. Gebirgsbeherrschung von 

Flözstrecken (in German). Glückauf-Verlag. 

 Müller W [1989]. Messung der absoluten 

Gebirgsspannungen im Steinkohlenbergbau (in German). Glückauf-Forschungshefte 50:105–112. 

 Opolony K, Witthaus H [2003]. Comparison of 

multiple- and single-entry roadway systems for highly 

stressed longwalls. In: Peng SS, Mark C, Khair AW, 

Heasley KA, eds. Proceedings of the 22nd International 

Conference on Ground Control in Mining. Morgantown, 

WV: West Virginia University, pp. 33–36. 

 Opolony K, Polysos N, Bartel R, Lüttig F [2000]. 

Ankertechnik bei der DSK Theorie und Praxis (in German). Glückauf 136. 

 Polysos N, Peters S [2002]. Application of geotechnical and geophysical parameters to improve planning 

certainty in roadway driving. In: Peng SS, Mark C, Khair 

AW, eds. Proceedings of the 21st International Conference 

on Ground Control in Mining. Morgantown, WV: West 

Virginia University, pp. 302–309. 

 Polysos N, Brandt K–H, Peters S [2003]. Geotechnische Gebirgsbewertung im Hinblick auf die 

Hangendbegherrschung im Strebraum (in German). 

2. Intern. Colloquium on High-performance Mining Production (Aachen, Germany). 

 Witthaus H [2001]. Die Planung von Ankerausbau in 

Flözstrecken (in German). 4. Intern. Colloquium: 

Ankerausbau im Bergbau (Aachen, Germany). 

 Witthaus H [2006]. Entwicklung einer Planungsmethode für Ankerausbau im deutschen Steinkohlenbergbau unter Berücksichtigung der Gebirgsstruktur und 

der Gebirgsbeanspruchung [Dissertation] (in German). 

Aachen, Germany: Verlag Mainz. 

 Witthaus H, Adams M, Junker M [2000]. Die ausbautechnische Planung für vorbauartig und doppelt genutzte 

Ankerstrecken (in German). Glückauf 136. 

 Witthaus H, Polysos N, Peters S [2006]. Applied geomechanics for support design in German deep coal mines. 

In: Peng SS, Mark C, Finfinger GL, Tadolini SC, Khair 

AW, Heasley KA, Luo Y, eds. Proceedings of the 25th 

International Conference on Ground Control in Mining. 

Morgantown, WV: West Virginia University, pp. 208–212. 

153

NUMERICAL MODELING PROCEDURES FOR PRACTICAL COAL MINE DESIGN 

By R. Karl Zipf, Jr., Ph.D., P.E.1

ABSTRACT 

 A method is presented for creating realistic numerical 

models for practical coal mine ground control. The method 

includes procedures to collect the necessary mechanical 

input parameters from a geologic core log, procedures to 

set up a model, and procedures to interpret calculation 

results. The input parameters come from a detailed geologic core log and extensive point load tests of estimate 

rock layer strength. A suite of material property input 

parameters is proposed that allows the user to go from core 

log to numerical model inputs. Rock bolt anchorage 

properties are also linked to the material properties of each 

geologic layer in the model. Following this procedure 

leads to very realistic calculations of the rock failure 

process and rock support system behavior. These calculations in turn enable realistic comparison of the effectiveness of alternative rock support systems. 

INTRODUCTION 

 Reducing ground failure fatalities and injuries is a 

priority of the National Institute for Occupational Safety 

and Health’s (NIOSH) mine safety and health research 

program. Ground failures have historically accounted for 

up to 50% of the fatalities in U.S. underground mines, and 

nonfatal injuries due to ground failure are almost always 

severe. Ground failures helped trigger recent mine 

disasters in Alabama (2001) and Utah (2000) by disrupting 

ventilation that led to gas explosions. Together, these 

incidents claimed the lives of 15 coal miners. In 2006, 

10 underground coal miners lost their lives in 7 roof falls, 

2 rib rolls, and 1 coal mine bump. 

 To reduce fatalities and injuries due to ground failure, 

NIOSH researchers are working toward improved understanding of rock mass failure mechanics using numerical 

analysis models. Promoting more widespread use of 

numerical models for ground control engineering may lead 

to the desired safety improvements; however, several 

barriers exist toward that end. Considerable guidance is 

needed for collecting necessary input data, setting up a 

model, and finally interpreting the analysis results. Such 

guidance should have the agreement of all parties involved 

in practical ground control, including mining companies, 

consultants, suppliers, and regulatory authorities. To 

enable better communication among mining engineers 

working in coal mine ground control, NIOSH researchers 

have made progress toward a set of input parameters for 

use in FLAC2

 [Itasca Consulting Group 1994] that result in 

very realistic models of coal mine rock behavior and rock 

bolts. Finally, the suggested guidance is not intended as a 

substitute for sound engineering judgment. 

 Obtaining the input parameters requires collection 

of certain information from rock core. The input parameters include material properties for a strain-softening, 

ubiquitous-joint constitutive model, rock bolt properties, 

and model initialization and loading. Use of these input 

parameters seems to lead automatically to (1) realistic 

modeling of the failure mechanics, (2) calculation of 

displacement and stress that are consistent with field 

measurements, and (3) a reasonable forecast of the 

effectiveness of rock support alternatives. This paper discusses a core logging procedure to obtain numerical model 

input parameters, presents a suite of input parameters for 

practical coal mine models, and demonstrates their use 

with a practical example. 

CORE LOGGING FOR INPUT PARAMETERS 

 Obtaining meaningful results from a numerical model 

begins with the collection of adequate geologic information. The method described for translating a geologic core 

log into input parameters for a numerical model follows a 

philosophy developed by Gale and Tarrant [1997] of 

“letting the rocks tell us their behavior.” For numerical 

modeling of coal mines, the logger must record two 

essential details, namely, individual geologic layers of 

homogeneous character and the strength of those geologic 

layers. Figure 1 shows a typical section of core with several distinct layers and other essential features to record. 

 The logging detail necessary depends on the scale of 

the numerical model. Small-scale models of coal mine 

entry behavior may require logging geologic layers as 

small as 50 mm. Larger-scale coal mine models for subsidence prediction may require less logging detail. Of particular importance to note are the soft clay layers or major 

bedding planes with weak infilling as indicated in Figure 1. 

 Having defined the geologic layering in sufficient 

detail, the logger must next estimate the strength of those 

layers, including the strength of the rock material and the 

strength of bedding plane discontinuities. Uniaxial compressive strength (UCS) tests, triaxial tests, or multistage 

 

2

Fast Lagrangian Analysis of Continua. 

 

 1Senior research mining engineer, Pittsburgh Research 

Laboratory, National Institute for Occupational Safety and 

Health, Pittsburgh, PA. 
154 

triaxial tests on core specimens oriented both perpendicular to bedding and at a 30° angle to bedding are the best 

way to measure cohesion and friction angle for the rock 

material and bedding plane discontinuities. However, conducting extensive tests is rarely a feasible option. Index 

tests are the preferred option and have the distinct advantage of providing multiple strength estimates for each geologic layer. Basic soil and rock descriptions of the 

International Society for Rock Mechanics [ISRM 1981] 

can provide a crude estimate of strength. Other options 

include simple hammer blow tests [ISRM 1981; Molinda 

and Mark 1996] or the Schmidt Hammer test for stronger 

materials [ISRM 1993]. The Point Load Index [ISRM 

1985] seems to be the simplest and most reliable method at 

present to estimate rock material and bedding plane 

strength through an axial or diametral point load test, 

respectively. Based upon thousands of tests, reliable 

correlations between Point Load Index and UCS have been 

developed for a variety of coal mine rocks throughout the 

United States [Rusnak and Mark 2000]. Techniques to 

estimate rock layer strength based on downhole geophysical measurements are also well developed [Medhurst 

and Hatherly 2005]; however, the methods have never 

been adopted widely by the U.S. coal industry. Figure 2 

shows estimates of the rock material and bedding plane 

strength for each geologic layer based on point load tests. 

 Detailed geologic logging for numerical modeling 

purposes has a relation to the Coal Mine Roof Rating 

(CMRR) used to describe coal mine roof rock in practical 

ground control [Mark et al. 2002b]. The CMRR Unit 

Rating for each rock layer is composed of two parts. The 

UCS rating for the rock material strength ranges from 5 to 

30 for a range of strengths between 0 and 138 MPa as 

determined from axial point load tests. The discontinuity 

rating for the bedding plane strength ranges from 25 to 60 

corresponding to strength of about 6–52 MPa based on 

diametral point load tests. 

MATERIAL PROPERTIES 

 For general modeling of rock behavior in coal mine 

ground control, the FLAC program [Itasca Consulting 

Group 1994] contains many useful features, in particular, 

the SU constitutive model. “SU” stands for the strainsoftening, ubiquitous-joint model and is ideal for simulating laminated coal measure rocks. In essence, this constitutive model allows for strain-softening behavior of the 

rock matrix and/or failure along a predefined weakness 

plane such as bedding planes. Failure through the rock 

matrix or along a bedding plane can occur via shear or 

tension, and the dominant failure mode can change at any 

time. The “state” variable within FLAC tracks the failure 

mode in each model element as either shear or tensile 

failure through the rock matrix or along a bedding plane. 

 The SU constitutive model requires four major input 

parameters, namely, cohesion, friction angle, dilation 

angle, and tensile strength for both the rock matrix and the 

bedding planes. Based on a Mohr-Coulomb strength 

model, the UCS of a rock depends on cohesion and friction 

angle as 

− φ

φ = 1 sin2 c cos UCS (1) 

where c is the cohesion and φ is the friction angle. Careful 

geologic core logging along with point load testing to 

estimate the UCS of each rock layer provides a rational 

basis to estimate the most important input parameters to 

the SU constitutive model. 

 Tables 1 and 2 summarize the name, UCS, and the 

initial value for input parameters of a proposed suite of 

“numerical rocks,” along with a corresponding geologic 

description of the rock. The UCS values indicated in 

Tables 1 and 2 are field-scale or model-scale values that 

are reduced from the laboratory-scale values determined 

from point load tests during geologic logging. Following 

the lead of Gale and Tarrant [1997] again, these laboratory 

values of UCS for rock and coal, but not soil, are reduced 

by a factor of 0.56 to produce the field-scale UCS and 

hence the input parameters to the numerical model. This 

scaling factor works well for rock masses associated with 

coal mining; however, it does not apply outside this 

narrow scope. 

 Figure 2.—Typical strength data along rock core from 

axial and diametral point load tests (PLT). 

0

5

10

15

20

25

0 20 40 60 80 100 120 140

UCS from axial and diametral PLT

Height into roof - m .

Axial PLT Diametral PLT

 Figure 1.—Photograph of core showing different rock 

layers and a prominent clay layer from 1.4 to 1.5 ft. 
155 

 The material suite shown in Tables 1 and 2 includes 

very weak soils and claylike materials with a UCS of 

0.02 MPa and weak, medium, and strong rocks with a UCS 

of about 150 MPa. Also included is coal, which ranges 

from the most friable with a UCS of 2 MPa to a strong coal 

with a UCS of 12 MPa. The soil material models are isotropic, i.e., the soil matrix properties are the same as those 

for the horizontal weakness plane. However, the rock 

models exhibit anisotropy since the strength along bedding 

planes is less than the UCS of the rock matrix. Following 

results of point load tests by Molinda and Mark [1996], 

weak rocks are the most anisotropic, with the strength 

along bedding planes about 50% of the rock matrix UCS, 

while stronger rocks have less anisotropy, with the strength 

along bedding planes about 90% of the rock matrix. The 

coal models have a similar trend in strength anisotropy, 

with the stronger coal less anisotropic than the weaker 

coal. For the stronger coal, the ratio of axial strength to 

strength parallel to bedding is about 1.5 to 1, whereas for 

the weaker coal the ratio is about 2.2 to 1. The weaker coal 

models would apply to more cleated coal, i.e., containing 

more closely spaced joints. The extensive material property suite for coal mine rocks proposed in Tables 1 and 2 is 

Table 1.—Initial values for rock material input parameters 

Material name Description Lab UCS 

(MPa) 

Field UCS 

(MPa) 

Young’s 

modulus 

(GPa) 

Cohesion 

(MPa) 

Friction 

angle, ° 

Dilation 

angle, ° 

Tensile 

strength 

(MPa) 

Soil 1.................... Paste 0.04 0.02 1 0.007 21 10 0.002 

Soil 2.................... Very soft soil 0.07 0.04 1 0.014 21 10 0.004 

Soil 3.................... Soft soil 0.14 0.08 1 0.028 21 10 0.008 

Soil 4.................... Firm soil 0.29 0.16 1.5 0.055 21 10 0.016 

Soil 5.................... Stiff soil 0.63 0.35 2 0.120 21 10 0.035 

Soil 6.................... Very stiff soil 3.6 2.0 2.5 0.69 21 10 0.20 

Rock 1 ................. Claystone, fireclay 6.4 3.6 3 1.2 22 10 0.3 

Rock 2 ................. Black shale 11 6 4 2.0 23 10 0.6 

Rock 3 ................. Black shale, gray shale 18 10 5 3.3 24 10 1.0 

Rock 4 ................. Gray shale 25 14 6 4.5 25 10 1.4 

Rock 5 ................. Siltstone, gray shale 34 19 7 6 26 10 1.9 

Rock 6 ................. Siltstone 48 27 8 8 28 10 2.7 

Rock 7 ................. Siltstone, sandstone 63 35 10 10 30 10 3.5 

Rock 8 ................. Sandstone, limestone 77 43 12 12 32 10 4.2 

Rock 9 ................. Sandstone 95 53 15 14 34 10 5.2 

Rock 10 ............... Limestone 139 78 20 20 36 10 7.7 

Coal 1 .................. Banded, bright coal 3.6 2.0 2.5 0.6 29 10 0.17 

Coal 2 .................. Banded coal 6.3 3.5 2.5 1.0 30 10 0.29 

Coal 3 .................. Banded, dull coal 12 6.7 2.5 1.9 31 10 0.60 

Coal 4 .................. Dull coal 17 9.7 2.5 2.7 32 10 0.85 

Table 2.—Initial values for bedding plane input parameters 

Material Name Description 

Lab 

strength 

(MPa) 

Field 

strength 

(MPa) 

Young’s 

modulus 

(GPa) 

Cohesion 

(MPa) 

Friction 

angle, ° 

Dilation 

angle, ° 

Tensile 

strength 

(MPa) 

Soil 1 ............... Paste 0.04 0.02 1 0.007 21 10 0.002 

Soil 2 ............... Very soft soil 0.07 0.04 1 0.014 21 10 0.004 

Soil 3 ............... Soft soil 0.14 0.08 1 0.028 21 10 0.008 

Soil 4 ............... Firm soil 0.29 0.16 1.5 0.055 21 10 0.016 

Soil 5 ............... Stiff soil 0.63 0.35 2 0.120 21 10 0.035 

Soil 6 ............... Very stiff soil 1.4 0.80 2.5 0.27 21 10 0.080 

Rock 1 ............. Claystone, fireclay 2.7 1.5 3 0.5 21 10 0.15 

Rock 2 ............. Black shale 5.4 3.0 4 1.0 22 10 0.30 

Rock 3 ............. Black shale, gray shale 10 5.7 5 1.9 23 10 0.60 

Rock 4 ............. Gray shale 18 10 6 3.3 24 10 1.0 

Rock 5 ............. Siltstone, gray shale 25 14 7 4.5 25 10 1.4 

Rock 6 ............. Siltstone 32 18 8 5.5 26 10 1.7 

Rock 7 ............. Siltstone, sandstone 41 23 10 7 27 10 2.3 

Rock 8 ............. Sandstone, limestone 59 33 12 10 28 10 3.3 

Rock 9 ............. Sandstone 86 48 15 14 29 10 4.8 

Rock 10 ........... Limestone 123 69 20 20 30 10 6.8 

Coal 1.............. Banded, bright coal 1.6 0.9 2.5 0.3 25 10 0.08 

Coal 2.............. Banded coal 2.9 1.6 2.5 0.5 26 10 0.15 

Coal 3.............. Banded, dull coal 6.4 3.6 2.5 1.1 27 10 0.30 

Coal 4.............. Dull coal 12 6.7 2.5 2.0 28 10 0.60 
156 

generally consistent with a smaller set of properties 

proposed by Reddish et al. [2000]. 

 Note that in proposing this suite of numerical rock 

properties, the UCS of the rock matrix is independent from 

the strength of the bedding planes. In the absence of 

specific data, the user will usually specify the rock matrix 

and bedding plane strength as a pair with strength ratio 

similar to that noted by Molinda and Mark [1996] for an 

extensive database of axial and diametral point load tests. 

However, the strength values for the rock matrix and 

bedding planes are independent in the material property 

suite, and the user can specify any value for the bedding 

plane strength up to that of the rock matrix UCS. 

 In creating the material model suites, friction angle for 

the matrix and bedding planes are assumed to vary as 

shown in Tables 1 and 2, respectively. These assumptions 

for friction angle along with Equation 1 then imply the 

values for peak cohesion shown in Tables 1 and 2. Thus, 

the UCS of the rock matrix and the bedding plane strength 

provide two of the four major input parameters to the SU 

constitutive model in FLAC. 

 Assumed friction angle values for the rock matrix 

ranges are 21° for soil- and claylike materials up to 36° for 

the strongest rocks. These values may be somewhat low 

compared to published values of Jaeger and Cook [1979] 

and Farmer [1968]. Later revisions of this material 

property suite may include a one friction angle range for 

application at low confinement and another for application 

at high confinement. Assumed friction angle values for the 

bedding plane are 21° for soil- and claylike materials up to 

30° for the strongest rocks. These values are consistent 

with data developed by Barton and summarized by Hoek 

et al. [1995]. 

 Other major assumptions within this material model 

suite are as follows: 

1. Moduli for the materials range from 1 to 20 GPa. 

Weaker materials have a lower modulus, while 

stronger materials have a higher modulus. The ratio 

of modulus to UCS of the rock matrix varies from 

about 1,000 for the weakest to about 100 for the 

strongest materials. The moduli for the material and 

the modulus-to-UCS ratio are consistent with data 

shown in Jaeger and Cook [1979] and Gale and 

Fabjanczyk [1993]. 

2. Cohesion decreases from its peak value given in 

Tables 1 and 2 to a residual value of 10% of peak 

over 5 millistrain of postfailure strain. It is this 

decrease in cohesion with postfailure strain that 

gives rise to strain-softening behavior of both the 

rock matrix and the bedding planes. 

3. Friction angle remains constant at the values shown 

in Tables 1 and 2, even in the postfailure regime. 

4. Tensile strength is equal to cohesion for the soil 

materials and decreases to 0 over 1 millistrain of 

postfailure strain. 

5. Tensile strength values are generally about 10% of 

UCS. It also decreases to 0 over 1 millistrain of postfailure strain. This strength ratio is again consistent 

with rock strength data shown in Jaeger and Cook 

[1979] and Farmer [1968]. 

6. Dilation angle is initially 10° and decreases to 0° 

over 5 millistrain of postfailure strain. 

ROCK BOLT PROPERTIES 

 In addition to its robust constitutive models, FLAC 

includes various structural support elements. The structural 

element called “cable” represents rock support as an axial 

force along a line, and this approach suffices for most rock 

or cable bolts in practical coal mining applications. If the 

shear or moment resistance of a rock bolt is significant, the 

“pile” structural element may be a more appropriate 

choice. 

 Properties required by the “cable” element are the 

structural characteristics of the steel, namely, elastic 

modulus, cross-sectional area, and yield strength, along 

with the structural characteristics of the anchor. Resin 

along with some cement grout now dominates most 

anchors used with rock and cable bolts in U.S. mines 

[Dolinar and Bhatt 2000]. Two properties represent the 

anchor characteristics in FLAC: “Kbond,” which is the 

stiffness of the grout, and “Sbond,” which is its cohesive 

strength. 

 Kbond, or anchorage stiffness, depends on grout properties and the annulus thickness, i.e., hole radius minus 

bolt radius. Based on numerical studies by St. John and 

Van Dillen [1983] of the grout-rock interface, the FLAC 

manuals [Itasca Consulting Group 1994] suggest the 

following expression for a practical estimate of Kbond for 

use in FLAC: 

10 ln(1 2t / D)

2 G Kbond

+

π

≅ (2) 

where G is the grout shear modulus, D is the bolt diameter, 

and t is the annulus thickness. 

 Farmer [1975] reports a value of 2.25 GPa (455,000 

psi) for the Young’s modulus of resin grout. For a typical 

19-mm (0.75-in) rock bolt in a 28.6-mm (1.125-in) hole, 

Kbond is approximately 1.4 × 109

 N/m/m. Over the practical range of rock bolt and hole diameters and the likely 

range for grout modulus, Kbond varies at most from about 

1 to 2 × 109

 N/m/m. 

 Numerical modeling of laboratory measurements of 

rock bolt behavior confirms this estimate of Kbond. 

Numerous researchers [Kwitowski and Wade 1980; 

Serbousek and Signer 1987; Signer 1990; Tadolini 1986] 

used strain gauges to measure the load distribution along 

fully grouted, 1-m-long rock bolts embedded in large 

blocks of limestone, shale, or concrete. Figure 3 shows 
157 

various measured load profiles where the bolt load at zero 

distance along the bolt is the actual applied load. Note the 

exponential decay of bolt load with distance, which is 

consistent with analytical models proposed by Farmer 

[1975] and Serbousek and Signer [1987]. A simple FLAC 

model of these laboratory pull tests was used to calculate 

the bolt load distribution for Kbond values of 0.5, 1, and 

2 × 109

 N/m/m and an applied load of 60 kN. As seen in 

Figure 4, Kbond equal to 1×109

 N/m/m matches the 

laboratory measurements well. 

 Sbond is also known as bond factor, anchor factor, or 

grip factor and has a typical value of about 350 kN/m 

(1 ton/in) in coal mine rocks. Its value depends on the 

likely failure mode of the bolt anchor. If the grout is weak, 

shear failure occurs along the bolt-grout interface, and 

Sbond depends on the grout cohesion and the perimeter of 

the bolt. Farmer [1975] reports a value of 160 MPa for the 

compressive strength of resin grout. Assuming that the 

cohesion is one-third of this value, Sbond at the bolt-grout 

interface for a typical 19-mm (0.75-in) bolt is about 

3.2 MN/m. 

 However, in coal mine rocks, shear failure typically 

occurs along the grout-rock interface, where Sbond 

depends on the lesser of the rock or grout cohesion and the 

perimeter of the hole. Table 1 indicates that rock cohesion 

varies from 1.2 to 20 MPa and is even less for the occasional thin clay layers. Thus, for a hole diameter in the 25- 

to 35-mm range, Sbond varies from 80 kN/m to 2.2 MN/m 

(0.2 to 4.5 tons/in) depending on the rock material 

strength. Table 3 shows the range of Sbond values for various rock materials. For practical coal mine modeling with 

FLAC, the user should specify bolt sections that correspond to the top and bottom of a geologic layer and then 

assign an Sbond value for that section consistent with the 

rock material properties for that layer. Table 4 shows 

Sbond values for various rocks either measured directly or 

inferred from select pull test data. Values range from 77 to 

1,225 kN/m and are consistent with the Sbond input 

parameters shown in Table 3. Note that the values for 

Kbond and Sbond discussed here assume a unit bolt spacing of 1 m between rows of bolts. These rock bolt properties and others require scaling according to the actual rock 

bolt spacing. 

Table 3.—Sbond values for various rock materials 

Material 

name Description Cohesion 

(MPa) 

Sbond for 

25-mm hole 

(N/m) 

Sbond for 

35-mm hole 

(N/m) 

Soil 1........... Paste 0.007 559 770 

Soil 2........... Very soft 

soil 

0.014 1,120 1,540 

Soil 3........... Soft soil 0.028 2,230 3,080 

Soil 4........... Firm soil 0.055 4,390 6,050 

Soil 5........... Stiff soil 0.120 9,580 13,200 

Soil 6........... Very stiff 

soil 

0.69 55,100 75,900 

Rock 1 ........ Claystone, 

fireclay 

1.2 95,800 132,000 

Rock 2 ........ Black shale 2.0 160,000 220,000 

Rock 3 ........ Black shale, 

gray shale 

3.3 263,000 363,000 

Rock 4 ........ Gray shale 4.5 359,000 495,000 

Rock 5 ........ Siltstone, 

gray shale 

6 479,000 660,000 

Rock 6 ........ Siltstone 8 638,000 880,000 

Rock 7 ........ Siltstone, 

sandstone 

10 798,000 1,100,000 

Rock 8 ........ Sandstone, 

limestone 

12 958,000 1,320,000 

Rock 9 ........ Sandstone 14 1,120,000 1,540,000 

Rock 10 ...... Limestone 20 1,600,000 2,200,000 

Coal 1 ......... Banded, 

bright coal 

0.6 47,900 66,000 

Coal 2 ......... Banded 

coal 

1.0 79,800 110,000 

Coal 3 ......... Banded, 

dull coal 

1.9 152,000 209,000 

Coal 4 ......... Dull coal 2.7 215,000 297,000 

 Figure 4.—Anchor length required for 100-kN capacity for

various Sbond. 

0

20

40

60

80

100

120

140

0.00E+00 1.00E+05 2.00E+05 3.00E+05 4.00E+05 5.00E+05 6.00E+05 7.00E+05 8.00E+05 9.00E+05 1.00E+06

Sbond of Anchor - N/m

Anchor Length for 100 kN - cm .

Anchor slip in this region 

Steel yield in this region 

0

10

20

30

40

50

60

70

80

0 0.2 0.4 0.6 0.8 1 1.2

Distance along bolt - meters

Bolt load - kN .

Kwitowski and Wade [1980]

Serbousek and Signer [1987]

Signer [1990]

Tadolini [1986]

Kbond = 500,000,000

Kbond = 1,000,000,000

Kbond = 2,000,000,000

 Figure 3.—Measured and calculated load profiles along 

rock bolts. 
158 

 Additional simple FLAC models calculated the minimum anchor length to hold 100 kN (about 10 tons) without 

slipping. Again, these models consider a 19-mm bolt of 

varying length and assumed yield strength for the steel of 

200 kN to ensure anchorage slip and not steel failure. 

Consistent with expectations, the critical anchor length 

ranged from 1 m at a low Sbond value of 100 kN/m down 

to 10 cm with a high Sbond value of 1,000 kN/m, 

as shown in Figure 4. For a given Sbond, a bolt with 

anchor length more than this critical value will fail by 

yield of the bolt steel, and with anchor length less than this 

critical value, anchor slip will occur. Figure 4 suggests that 

for stronger rocks with Sbond more than 350 kN/m 

(1 ton/in), short encapsulation pull tests with anchor length 

of much less than 30 cm (1 ft) are necessary to measure 

Sbond directly. 

INITIALIZATION AND LOADING CONDITIONS 

 A recent summary of horizontal stress measurements 

in U.S. coal mines by Dolinar [2003] demonstrated that the 

horizontal stress magnitude depends on the elastic modulus 

of the rock layers. Horizontal stress varies according to the 

relative stiffness of each geologic layer, such that stiff 

limestone or sandstone layers attract higher horizontal 

stress than less stiff black shale or claystone layers. 

 To initialize horizontal stress in a model, the analyst 

must first calculate the average horizontal strain as 

average

H average

H average E

σ

ε = (3)

where σH average is the average horizontal tectonic stress and 

Εaverage is the average modulus. Using Dolinar’s approach, 

a tectonic strain could also be used directly for the initial 

far-field boundary condition. Alternatively, if the horizontal stress and modulus are known for a particular layer 

within a model, the horizontal strain can be calculated on 

that basis. 

 Horizontal stress for each layer in the model has a 

tectonic component and a Poisson component and is calculated as 

( ) 1 ( )(E ) H i H average i σν i ⎟

⎠

⎞ ⎜

⎝

⎛

− ν

ν

σ = ε + (4) 

where Εi is the Young’s modulus for a layer, ν is Poisson’s 

ratio, and σν i is the vertical stress in a layer. Vertical stress 

in each layer depends on depth in the usual way. Figure 5 

shows a layered model of coal mine rocks initialized with 

this procedure. Average initial vertical and horizontal 

stress is 5 and 8 MPa. 

PUTTING IT ALL TOGETHER: AN EXAMPLE 

 This example demonstrates the complete modeling 

procedure for a coal mine gate road entry in the Pittsburgh 

Coalbed that is first subject to initial development loading, 

then additional loading from mining the first longwall 

panel, and finally more loading as a second longwall panel 

approaches. Again, Figure 2 shows estimates of axial and 

diametral point load strength as measured along a core. 

The point load tests used to estimate the UCS of the rock 

matrix and the bedding plane strength lead directly to 

material property assignments based on Tables 1 and 2. 

Table 5 summarizes a section of the geologic column, 

strength values from point load tests, and the resulting 

material property inputs for the model. Figure 5 reflects 

the layering detail in the overall model. Initial horizontal 

stress magnitude applied to the model generally correlates 

to high- or low-strength rock layers. The rock bolts in the 

model are composed of many sections, where each section 

corresponds to the top and bottom of a geologic layer. 

Each bolt section is then assigned an Sbond value consistent with the rock material properties for that layer. 

 Table 6 indicates the average horizontal and vertical 

stress applied to the model at different stages. The stresses 

shown in Table 6 are a two-dimensional approximation to 

a complex three-dimensional problem. In the gate road 

development phase, applied stresses are the same as in situ 

stresses. Mining the first longwall panel effectively 

induces higher horizontal and vertical stresses far field 

from the model coal mine entry. The approaching second 

longwall panel and passage of that second panel induces 

additional horizontal and vertical stresses. Again, the stress 

path indicated in Table 6 is only a simple two-dimensional 

approximation of the actual complex three-dimensional 

stress field applied to the coal mine entry. 

Table 4.—Measured Sbond in various rocks. 

Rock Sbond (N/m) Reference 

Shale-concrete .... 77,000 Bartels and Pappas [1985]. 

Plaster ................. 126,000 Bartels and Pappas [1985]. 

Chalk ................... 193,000 Franklin and Woodfield [1971].

Dark gray 

 fireclay.............. 220,500 Mark et al. [2002a]. 

Layered dark 

 gray shale......... 252,000 Mark et al. [2002a]. 

Sandstone ........... 289,000 Franklin and Woodfield [1971].

Concrete blocks... 290,000 Pettibone [1987]. 

Thinly banded 

 gray shale......... 290,500 Mark et al. [2002a]. 

Clay, claystone.... 304,500 Mark et al. [2002a]. 

Dark gray shale ... 364,000 Mark et al. [2002a]. 

Coal..................... 385,000 Franklin and Woodfield [1971].

Gypsum............... 385,000 Dunham [1974]. 

Limestone............ 400,000 Dunham [1974]. 

Anhydrite ............. 526,000 Dunham [1974]. 

Limestone............ 1,225,000 Franklin and Woodfield [1971].

Coal/shale ........... 300,000– 

900,000 Yearby [1991]. 

Sandstone/ 

 limestone..........

1,000,000– 

2,500,000 Yearby [1991]. 
159 

 To apply these additional horizontal and vertical 

stresses to the model, equivalent average strains are calculated based on a weighted average modulus for the model. 

Based on the overall model dimensions, equivalent displacements at the model boundary are calculated. These 

displacements are then achieved in the model by slowly 

applying a velocity at the boundary for a prescribed number of computational steps. Velocity at the model boundary 

is then set to zero for additional computational steps to 

achieve equilibrium. 

 The modeling analyzes two alternative support systems, namely, 2.4-m fully grouted rock bolts alone and 

with 4-m-long cable bolts. Figure 6 compares these alternatives by showing rock bolt loads, rock bolt anchor slip, 

rock bolt breakage, and rock mass shear failure superimposed on the UCS of the rock matrix. Different shades 

represent rock layers of different rock matrix strength. 

Generally in the Pittsburgh Coalbed, the immediate roof 

rock is low-strength black shale, thin coal layers, and 

claystone. Above the immediate roof rock is somewhat 

higher-strength gray shale and siltstone beds. Rock mass 

failure has occurred throughout the immediate roof. Zones 

Table 5.—Going from core log to numerical model input 

parameters 

Height 

into roof 

(m) 

Rock 

type 

UCS 

axial 

PLT 

(MPa) 

Bedding 

strength 

diam. PLT 

(MPa) 

Rock 

matrix 

code 

(Table 1)

Bedding 

plane 

code 

(Table 2)

3.00..... Sandy bl sh 33.70 12.40 RM5 RBP3 

2.90..... Sandy bl sh 33.70 12.40 RM5 RBP3 

2.80..... Sandy bl sh 33.70 12.40 RM5 RBP3 

2.70..... Sandy bl sh 33.70 12.40 RM5 RBP3 

2.55..... Sandy bl sh 33.70 12.40 RM5 RBP3 

2.40..... Coal 12.00 6.70 CM3 CBP3 

2.30..... Coal 12.00 6.70 CM3 CBP3 

2.20..... Coal 12.00 6.70 CM3 CBP3 

2.10..... Coal 12.00 6.70 CM3 CBP3 

2.03..... Coal 12.00 6.70 CM3 CBP3 

1.90..... Bl sh + coal 18.00 4.00 RM3 RBP2 

1.80..... Bl sh + coal 18.00 4.00 RM3 RBP2 

1.69..... Bl sh + coal 18.00 4.00 RM3 RBP2 

1.60..... Claystone 8.00 2.00 RM2 RBP1 

1.50..... Claystone 8.00 2.00 RM2 RBP1 

1.40..... Claystone 8.00 2.00 RM2 RBP1 

1.30..... Claystone 8.00 2.00 RM2 RBP1 

1.18..... Claystone 8.00 2.00 RM2 RBP1 

1.08..... Black shale 18.00 4.00 RM3 RBP2 

0.98..... Coal 12.00 6.70 CM3 CBP3 

0.88..... Coal 12.00 6.70 CM3 CBP3 

0.76..... Black shale 18.00 4.00 RM3 RBP2 

0.64..... Black shale 18.00 4.00 RM3 RBP2 

0.52..... Coal 12.00 6.70 CM3 CBP3 

0.40..... Coal 12.00 6.70 CM3 CBP3 

0.28..... Black shale 18.00 4.00 RM3 RBP2 

0.16..... Black shale 18.00 4.00 RM3 RBP2 

0.00..... Coal 12.00 6.70 CM3 CBP3 

Bl sh = Black shale. PLT = point load test. 

Table 6.—Applied stress path at model boundary 

Loading condition Average horizontal 

stress (MPa) 

Average vertical 

stress (MPa) 

Development ................ 8 5 

1st panel mining ........... 14 9 

2nd panel mining .......... 17.6 11.4 

Postmining.................... 20 13 

 Figure 5.—Initial horizontal stresses. Warm colors indicate high horizontal stress in stiffer layers; cool colors indicate low 

horizontal stress in less stiff layers. The future entry is shown at center. 
160 

of intense bedding plane slip exist above the upper corners 

of the entry, and these zones propagate 2 to 3 m into the 

roof. Bedding plane separation has also developed 1.5, 2.5, 

and 4.5 m into the roof rock, as shown in Figure 7. 

Compressive failure of the immediate roof rock has localized into several “shear bands,” as indicated in Figure 6 

with the shear strain index parameter in FLAC. These 

shear bands are more developed with the lighter support 

system consisting of bolts alone. The failure has also 

tended to favor one side of the roof more than the other. 

Downward roof movement is much greater on the left than 

on the right. The magnitude of rock bolt load is plotted as a 

percentage of yield strength of the steel. For the 

untensioned, fully grouted rock bolts used in this model, 

the load increases from zero at the bolt head, rises to a 

maximum somewhere in the middle, and decreases back to 

zero at the anchorage end. The shape of the load profile 

follows the measured laboratory experiments, as shown in 

Figure 3. All bolt loads are tensile, no matter whether the 

load is plotted left or right of the bolt. Anchorage slip is 

indicated by crosses along the bolt. At the highest load 

applied to the model, anchor slip has occurred almost 

everywhere along the rock bolts and the lower portion of 

the cable bolts. Rock bolt or cable bolt breakage can occur 

if load on the bolt equals the yield load and if strain in the 

bolt exceeds 2%. Bolt breakage occurs in the left and 

center bolts for the bolts-alone case and only in the center 

bolt if cable bolts are also installed. Although the broken 

section of bolt is not visible in Figure 6, the low axial loads 

on either side of the shear zone mark the location of the 

broken bolt section. 

 Figure 7 shows the effectiveness of the two alternative 

rock support systems for controlling immediate roof 

movement under progressively higher load conditions. 

Under development conditions with horizontal and vertical 

stresses of 8 and 5 MPa, respectively, roof displacement is 

less than 10 mm and both bolt alternatives behave 

identically. Mining the first longwall panel increases horizontal and vertical stresses to 14 and 9 MPa; however, 

calculated roof displacements remain under 30 mm, and 

there is still negligible difference between the two 

alternatives. When the second longwall panel approaches, 

the necessity of the cable bolts becomes evident. In the 

alternative without cables, downward roof displacement at 

 Figure 7.—Immediate roof displacement response for two 

rock support alternatives.

1 - Development, SH = 8 MPa, SV = 5 MPa

2 - 1st panel mining, SH = 14 MPa, SV = 9 MPa

3 - 2nd panel mining, SH = 17.6 MPa, SV = 11.4 MPa

4 - SH = 20 MPa, SV = 13 MPa

0

1

2

3

4

5

6

7

0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160 0.180

Roof displacement at centerline - m 

Height into roof - m .

2.4 m bolts, no cables

2.4 m bolts with cables

 Figure 6.—Support system performance with 2.4-m bolts 

alone (top) and with 2.4-m bolts with 4-m cables (bottom). 

Rock layers of different strength are shaded. Shear zones are 

contoured. Maximum shear strain contour is 0.5. Rock bolt 

load is indicated. 
161 

2-m horizon approaches 70 mm, whereas with cables 

movement at this horizon is about 30 mm. Total downward 

roof movement in excess of 50 mm and sudden jumps in 

that movement with small increases in the applied load on 

the model are indicative of roof instability and ineffective 

roof support. 

CONCLUSIONS 

 This paper presents progress toward a standard 

method for the use of numerical models in practical ground 

control planning. The method includes procedures for 

collecting the needed input data, setting up a model, and 

interpreting the results of calculations. 

 Collecting the input data needed for a numerical 

model begins with development of a detailed geologic core 

log. This core log must capture geologic layers of similar 

mechanical properties and also note particular features 

such as exceptionally weak clay layers. Point load testing 

is a convenient method to estimate the UCS of the rock 

matrix and the bedding plane strength for each geologic 

layer. 

 This paper proposes a suite of material property input 

parameters aimed at the SU constitutive model in FLAC. 

This suite of “numerical rocks” includes very weak soils 

and weak rocks to the strongest rocks found in coal 

mining. Having estimates of UCS and bedding plane 

strength for each geologic layer, the user can readily create 

a numerical model that correctly reflects the geologic 

situation. The suggested procedure has the distinct advantage of being organized and reproducible. In principle, two 

different individuals could examine a geologic section, 

describe it, test it, and develop the same numerical model 

inputs for the field conditions. 

 This paper also presents select properties needed to 

represent rock supports in a numerical model. The 

significant feature of the rock bolt properties is the linkage 

between rock bolt anchorage and the specific geologic 

layer containing that section of the rock bolt. Sections of a 

rock bolt in weak rocks have low anchor strength and 

vice versa in stronger rocks. 

 A practical example of a numerical model that follows 

the proposed procedure leads to very realistic results. The 

calculations capture the rock failure process correctly and 

agree with failure observations in the field. Calculated 

stresses and displacements in the model are consistent with 

field measurements of the same. 

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