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International Journal of Mining Science and Technology 26 (2016) 12

Contents lists available at ScienceDirect

International Journal of Mining Science and Technology

journal homepage: www . elsevier . com/locate/ijmst

Guest EditorialSpecial issue on Ground Control in Mining

Michael M. Murphya,

, Gerald L. Finfingera, Syd S. Peng

b,

aNational Institute for Occupational Safety and Health, Division of Mining Research Operations, Pittsburgh, PA 15236-0070, USAbWest Virginia University, Morgantown 26506, USA

Ground control is the science of studying and controlling the behavior of

rock strata in response to mining operations. Ground control related researchhas made significant advancements over the last 35 years and these

accomplishments are well documented in the proceedings of the annual

International Conference on Ground Control in Mining (ICGCM) [1]. The

International Confer-ence on Ground Control in Mining is a forum to promote

closer communication among researchers, consultants, regulators, manu-

facturers, and mine operators to expedite solutions to ground con-trol

problems in mining. Fundamental research and advancements in ground

control science comprise the central core of the confer-ence mission.

Providing information to the mine operators is a pri-ority as the conference

goal is solution-oriented information. In addition, the conference has included

innovative technologies and ideas in mining related fields such as exploration,

geology, and surface and underground mining. Many new ground control

technologies and design standards adopted by the mining industry were first

discussed at the conference [26]. Therefore, this confer-ence is recognized

as the best forum for introducing new ground control related research and

products.

The 34th ICGCM was held on July 2830, 2015 in Morgantown, WV.

This years event had 240 attendees with significant represen-tation from

mine operators. The event included 48 speakers in 10 different sessions

during the three days of the conference. The international community was

well-represented with 34 attendees from 6 countries, with China sending 15

representatives and Aus-tralia sending 10 representatives. A special session

was held on the upcoming ground control conference to be held in China and

the session was chaired by Professor Xiexing Miao and Professor Jiachen

Wang of the China University of Mining and Technology of Xuzhou and

Beijing, respectively. A remarkable number of industry representatives

attended given the challenges currently faced by the mining industry.

Professor Syd Peng (West Virginia University), conference foun-der,

delivered an exceptional presentation on identifying current research needs in

coal mine ground control. Dr. Peng, on his own initiative, organized the First

Conference on Ground Control in Mining in the summer of 1981. Dr. Peng

keenly recognized that in order to advance the state-of-the-art in ground

control, a forum

Corresponding authors. Tel.: +1 412 386 4172.E-mail addresses: [email protected] (M.M. Murphy), [email protected] (S.S.

Peng).

was urgently needed whereby researchers, practitioners, equip-ment

manufacturers, and government regulators could meet regu-larly andexchange information in a timely manner. The conference legacy and

longevity is a tribute to Dr. Pengs tireless and persistent efforts to advance

the science of ground control. Dr. Pengs presen-tation at this years

conference highlighted the research yet to be done in all areas to continue to

advance the science of ground con-trol and develop solutions to problems that

have been persistent with current mine design, operational practices, and

engineering interventions.

The topics covered at this years conference included a wide-range of

subjects and of particular note were the papers presented in the opening day

sessions on ground control design tools and bump related research.

Researchers from National Institute for Occupational Safety and Healths

(NIOSH) Office of Mine Safety and Healths (OMSHR) Ground Control

Branch opened the confer-ence with three presentations on the latest design

tool that pro-vides insight into coal mine entry stability. Ted Klemetti

(OMSHR) presented A Procedure for the Rapid Assessment of Coal Mine

Roof Stability Against Large Roof Falls, which discussed a non-linear

regression equation for predicting the stability factor of supported entries for a

given set of geotechnical conditions. The non-linear equation was based on

analysis from over 600 FLAC3D numerical model results. Gabriel

Esterhuizen (OMSHR) presented Analysis of Alternatives for Using Cable

Bolts As Pri-mary Support at Two Low-seam Coal Mines, which discussed

the practicality of utilizing the strength reduction method to assist with

answering common questions asked by ground control prac-titioners. The

research describes cable bolting solutions at two coal mines in similar ground

conditions and the numerical model-based analysis demonstrated benefits of

various support systems, verified by careful observations in the field. Ihsan

Tulu (OMSHR) presented A Case Study of Multi-seam Coal Mine Entry

Stability Analysis with Strength Reduction Method, which discussed a case

study mine under highly variable topography which led to unex-pected roof

conditions. The research describes the unexpected roof conditions that were

encountered and solutions that were evalu-ated by the strength reduction

method to effectively assess the likely success of different roof supports and

coal mine entry stability.

Due to the recent bump fatalities in the coal mining sector, coal bump

research was highlighted during the first day of the confer-ence. Both

Christopher Mark (MSHA) and Anthony Iannacchione

http://dx.doi.org/10.1016/j.ijmst.2015.11.0012095-2686/ 2015 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
http://dx.doi.org/10.1016/j.ijmst.2015.11.001http://dx.doi.org/10.1016/j.ijmst.2015.11.001http://dx.doi.org/10.1016/j.ijmst.2015.11.001http://www.sciencedirect.com/science/journal/20952686http://www.sciencedirect.com/science/journal/20952686http://www.elsevier.com/locate/ijmstmailto:%20%[email protected]:%20%[email protected]:%20%[email protected]:%20%[email protected]:%20%[email protected]:%20%[email protected]://www.elsevier.com/locate/ijmsthttp://www.sciencedirect.com/science/journal/20952686http://dx.doi.org/10.1016/j.ijmst.2015.11.001


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2 Guest Editorial / International Journal of Mining Science and Technology 26 (2016) 12

(University of Pittsburgh) opened the bump related research ses-sion with a historical perspective on the evaluation of the risk and control

of coal burst events in underground coal mines. Heather Lawson (OMSHR) and Eric Poeck (Colorado School of Mines) presented research

related to new findings in coal bump prediction. Lawson presented Dynamic Failure in Coal Seams: Implications of Coal Composition for

Bump Susceptibility, which establishes that coal may be more inherently prone to bumping due to certain characteristics in its

composition. Poeck presented Energy Concepts in the Analysis of Unstable Coal Pillar Failures, which used a numerical -based analysis

to illustrate that the wide-spread failure of several pillars in a compressive nature depends heavily upon the strength properties of the

coal/rock interface. The session also included a presentation by Peter Zhang (Alpha Natural Resources, Inc.) which discussed the

geotechnical risk management program at an operating room and pillar mine under deep cover to help prevent coal bump potential.

The conference also included discussions involving research related to underground limestone mines and a presentation enti-tled

Analysis of Roof and Pillar Failure Associated with Weak Floor at a Limestone Mine provided insight on the first well-studied case of a

weak floor leading to ground control issues in an under-ground limestone mine (presented by the Michael Murphy). The research showed

the effect of a weak floor on long-term stability of underground limestone working, a unique scenario for a stone mine. Brent Slaker

(OMSHR) demonstrated the practical application of photogrammetry, a new evaluation tool to assist with mon-itoring underground mine

displacements. The method successfully detected both small and large rib movements at the same under-ground limestone mine.

A number of the papers discussed above are included in this special issue of the International Journal of Mining Science and

Technology. All other papers from this years (and previous years) conference can be found on the International Conference on Ground

Control in Minings website. We hope th is special issue will provide useful references for engineers worldwide and for researchers and

scholars in the field of ground control.

References

[1] ICGCM website that stores all 33 conference proceedings since 1981 for free distribution is: www.icgcm.conferenceacademy.com.

[2] Peng SS. Topical areas of research needs in ground control: a state of the art review on coal mine ground control. Int J Mining Sci Technol 2015;25(1):16.

[3] Peng SS. Coal mine ground control. 3rd ed. Morgantown: Syd Peng Publisher; 2008.

[4] Peng SS. Ground control failures. Morgantown: Syd Peng Publisher; 2007.

[5] Heasley KA, Su DWH. 25 years of progressive in numerical modeling for ground control what have we accomplished and where do we go next? In: Proceedings of

the 25th international conference on ground control in mining, Morgantown; 2006. p. 117.

[6] Hasenfus GJ, Su DWH. Horizontal stress and coal mines: twenty five years of experience and perspective. In: Proceedings of the 25th international conference on

ground control in mining, Morgantown; 2006. p. 25667.
http://www.icgcm.conferenceacademy.com/http://www.icgcm.conferenceacademy.com/http://refhub.elsevier.com/S2095-2686(15)00182-2/h0010http://refhub.elsevier.com/S2095-2686(15)00182-2/h0010http://refhub.elsevier.com/S2095-2686(15)00182-2/h0010http://refhub.elsevier.com/S2095-2686(15)00182-2/h0010http://refhub.elsevier.com/S2095-2686(15)00182-2/h0015http://refhub.elsevier.com/S2095-2686(15)00182-2/h0015http://refhub.elsevier.com/S2095-2686(15)00182-2/h0020http://refhub.elsevier.com/S2095-2686(15)00182-2/h0020http://refhub.elsevier.com/S2095-2686(15)00182-2/h0020http://refhub.elsevier.com/S2095-2686(15)00182-2/h0015http://refhub.elsevier.com/S2095-2686(15)00182-2/h0015http://refhub.elsevier.com/S2095-2686(15)00182-2/h0010http://refhub.elsevier.com/S2095-2686(15)00182-2/h0010http://www.icgcm.conferenceacademy.com/


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Dynamic failure in coal seams: Implications of coal composition for

bump susceptibility

Lawson Heathera,

, Weakley Andrewb

, Miller Arthura

cOffice of Mine Safety and Health Research, NIOSH, Spokane 99207, USAdDepartment of Chemical and Materials Engineering, University of Idaho, Boise 83705, USA

a r t i c l e i n f o

Article history:Received 28 July 2015Received in revised form 3 October 2015

Accepted 20 October 2015Available online 28 December 2015

Keywords:CoalBumpBounceDynamic failurePillar burst

a b s t r a c t

As a contributing factor in the dynamic failure (bumping) of coal pillars, a bump-prone coal seam has been described as one

that is uncleated or poorly cleated, strong. . .that sustains high stresses. Despite extensive research regarding engineering

controls to help reduce the risk for coal bumps, there is a paucity of research related to the properties of coal itself and how

those properties might contribute to the mechanics of failures. Geographic distribution of reportable dynamic failure events

reveals a highly localized clustering of incidents despite widespread mining activities. This suggests that unique, contributing

geologic characteristics exist within these regions that are less prevalent elsewhere. To investigate a new approach for

identifying coal characteristics that might lead to bumping, a principal component analysis (PCA) was performed on 306 coal

records from the Pennsylvania State Coal Sample database to determine which characteristics were most closely linked with a

positive history of reportable bumping. Selected material properties from the data records for coal samples were chosen as

variables for the PCA and included petrographic, elemental, and molecular properties. Results of the PCA suggest a clear

correlation between low organic sulfur content and the occurrence of dynamic failure, and a secondary correlation between

volatile matter and dynamic failure phenomena. The ratio of vola-tile matter to sulfur in the samples shows strong correlation

with bump-prone regions, with a minimum threshold value of approximately 20, while correlations determined for other

petrographic and elemental variables were more ambiguous. Results suggest that the composition of the coal itself is directlylinked to how likely a coal is to have experienced a reportable dynamic failure event. These compositional controls are distinct

from other previously established engineering and geologic criteria and represent a missing piece to the bump prediction

puzzle.

2015 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

1. Introduction

Dynamic failure events in an underground coal mine, or bumps, are

defined as the sudden, violent bursts of coal from a pillar or pillars or a

block of coal, resulting in a section, the whole pillars, or the solid block ofcoal being thrown into an open entry [1].Reports of disastrous and often

fatal dynamic failure eventsdate back over one hundred years in the United

States. Mining practices and technologies have significantly evolved over the

course of the last century, yet these events continue to occur. The events at

Crandall Canyon, Utah and Brody No.1 Mine in West Virginia are two recent

failure events that resulted in a total of ele-ven fatalities. These events testify

to the fact that dynamic failure remains an imperative safety concern [2,3].

Furthermore, their

[7] Corresponding author. Tel.: +1 509 3548061.

E-mail address: [email protected](H. Lawson).

continued occurrences indicate that engineering controls have pro-ven

inadequate at wholly mitigating the problem.Multiple conditions have been associated with the occurrence of dynamic

failure phenomena, including:

(1) Thick and competent strata that can create a bridging effect, resulting

in high abutment stresses [410].

(2) Overburden thicknesses greater than 150210 [1,7].

(3) A strong coal that is resistant to crushing or that is uncleated or

poorly cleated, strong. . .sustains high stress and tends to fail

suddenly [4,8,7].

(4) Presence of sandstone channels or rolls that can serve to concentrate

stresses [4,6].

(5) Fracturing of strong units above or below the coal seam [10].

(6) Slip along pre-existing discontinuities [10,11].

(7) Multiple seam mining interactions [1,6,12,13].

(8) Mining sequences that can cause anomalously high stress

concentrations [6,12].

http://dx.doi.org/10.1016/j.ijmst.2015.11.002
http://dx.doi.org/10.1016/j.ijmst.2015.11.002http://dx.doi.org/10.1016/j.ijmst.2015.11.002http://dx.doi.org/10.1016/j.ijmst.2015.11.002http://www.sciencedirect.com/science/journal/20952686http://www.sciencedirect.com/science/journal/20952686http://www.elsevier.com/locate/ijmstmailto:[email protected]:[email protected]:[email protected]:[email protected]://www.elsevier.com/locate/ijmsthttp://www.sciencedirect.com/science/journal/20952686http://dx.doi.org/10.1016/j.ijmst.2015.11.002


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2095-2686/ 2015 Published by Elsevier B.V. on behalf of China University of Mining & Technology.


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4 H. Lawson et al. / International Journal of Mining Science and Technology 26 (2016) 38

This list represents a compilation of factors that have histori-cally been

associated with the occurrence of dynamic failure phe-nomena. Peng states

that, a bump may occur even though one or more . . .[generally accepted]

geological conditions are not present [1].Rice suggested that a combination

of factors, rather than one or two specific circumstances, is required to

facilitate a bump-ing event [7]. Identifying a set of conditions that will

consistently produce bumping, however, has proven elusive; conditions

gener-ally associated with dynamic failure might produce an event at one sitebut not another. Conversely and more troubling, dynamic fail-ure could occur

where relatively few of these factors exist, although some are usually present.

In conventional coal pillar design, coal is often treated as an

approximately homogenous material with a uniaxial compressive strength of

6205 kPa [14]. While this practice is generally accepted, coal deposits are, in

reality, heterogeneous. While treating coal as a substance that exhibits

consistent material properties provides effective tools for mine design, these

tools have proven ineffective at completely eradicating dynamic failure events

[15,16]. In fact, it could be that the differences between coal deposits hold the

key to answer the question of why some coals appear to fail violently more

frequently than others.

Dynamic failure events have a propensity to occur regionally or locally as

indicated by the geographic clustering of bump inci-dences, shown in Fig. 1.

This supposition is supported by anecdotal evidence: Peperakis describes

notable cases from the Sunnyside Mine in Utah where failure events occurred

during the develop-ment in virgin ground, in localities a long way from

active pillar workingsconditions not normally associated with dynamic

fail-ure phenomena [17]. He states that these events could have been

facilitated by the presence of faulting. However, faults certainly exist in other

regions, yet bumps during the development phase of mining are extremely

rare. This observation corroborates those of Babcock and Bickel who

proposed that some coals, notably those from western coalfields, could be

inherently more prone to exhibit bursting-type behavior in a laboratory

environment [18]. This sug-gests that some coals could be more inherently

susceptible to bumping than others, creating a greater risk when coupled with

the factors which are already known to contribute to bumping phenomena.

Previous efforts to understand and model coal bumping have focused on

the mechanical properties of coal (among other factors). Some of these have

included unconfined compressive strength

(UCS) and stiffness as primary variables [1,7,12,19]. Agapito and Goodrich

indicate that cleat density could also contribute to dynamic failure in Western

coal mines [4]. While these researchers have approached the problem from

different angles, it seems that the ultimate goal of these observations is to

describe the capability of a coal to retain energy prior to failure and thereby

resist crush-ing. This energy could be subsequently released kinetically, in the

form of a dynamic failure event. Thus far, however, these observa-tions have

failed to yield a consistent set of physical parameters that produce bumping.

Furthermore, the tests required to attain these values could be time-

consuming, difficult, or costly. There-fore, it would be prudent to examine

other, more accessible coal attributes for correlation with bump susceptibility.

Significant success has been achieved in correlating the material

properties of coals with their elemental and petrographic characteristics.

Laubach et al. defined an empirical relationship between vitrinite reflectance

and cleat density [17]. Van Krevelen, Van Krevelen and Schuyer describe

empirical relationships between the chemical composition of coal and

acoustic properties, Hardgrove grind ability index (HGI), thermal and electric

conduc-tivity, porosity, calorific value, and other attributes [20,21]. Mathews

et al. provide an overview of empirically determined relationships between

both elemental and petrographic parame-ters of coal composition and many of

these physical properties [22]. Given that coal composition directly

influences the optical, physical, and material properties of coal, we

hypothesize that ele-mental and/or molecular variables are fundamentally

linked to dynamic failure events. This concept is not without precedent;

Brauner makes the observation that bumps were not observed in coals with

less than 12% volatile matter [23]. This correlation between bumping and

coal composition is echoed by Osterwald, Dunrud, and Collins who stated

that there was an apparent corre-lation between bumping and the presence of

benzene in the coal matrix [24]. This leads to the deduction that it could be

possible to use coal composition to predict bump susceptibility. Were it

possible to define the applicable components of coal, it would provide a more

accessible and potentially more reliable measure of bump susceptibility than

the commonly accepted mechanical property tests.

The Pennsylvania State University Coal Sample Bank and Database

maintains an archive of bulk coal samples and a database of detailed

characterizations of coal samples acquired from active or previously active

mines across the continental United States.

INOH

WV

N KY

Northern great plains Province

Pacific coast ProvinceRocky mountain Province

Interior Province Eastern Province

Gulf ProvinceCoal basins0

UT 1-56-10

CO 11-1516-2021-5051-105

Fig. 1. Regional clustering of reported bump phenomena by country, compared to coal basins.


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H. Lawson et al. / International Journal of Mining Science and Technology 26 (2016) 38 5

Although reliable, the wealth of information describing the ele-mental,

petrographic, and proximate analytical character of these samples prevents

simple data reduction and visualization using common data analytical

procedures (e.g., scatter plots, pair-wise Pearson correlation coefficients). In

the absence of a priori insight as to which measurements (variables) are

correlated to bumping, an exploratory principal component analysis (PCA)

provides a pru-dent first step.

A PCA transformation provides a convenient means of isolating only

essential information contained across a large number of measurements

(variables) in a manner aiding visualization and suppressing noise [25]. For

example, an individual coal sample might be described by 100 distinct

measurements, some of which are likely correlated, such as percentage of

volatile matter (%) or percentage of hydrogen (%). Using all of the available

coal samples, a PCA performs a series of orthogonal projections that

condense the important between-sample variance contained within the sample

measurements onto a handful of new variables. Effectively, PCA estimates

new axes where the similarity between each individual sample, as well as the

role of each variable, is readily assessed.

In this study, a PCA was performed on 306 coal records from the

Pennsylvania State Coal Sample Database to qualitatively assess a possible

link between sample composition and the propensity for dynamic failure.

Records include petrographic, elemental, and proximate analytical

measurements and were compared to a data-base of dynamic failure events

reported between 1983 and 2009. Associations between bump susceptibility

and sample properties elucidated by PCA will allow for a more targeted use

of engineering controls, foster effective risk prevention research, and

ultimately lead to fewer bump related accidents and fatalities.

2. Method

Five-hundred-twenty-eight records from the Pennsylvania State

University (PSU) Coal Sample Database were used for this study. Records

include elemental, proximate, and petrographic analyses results from channel

samples from coal basins throughout the United States. From material

property data, only a subset of samples and variables were chosen to be used

for a PCA due to the prevalence of missing measurements. Ultimately, 222

samples were removed from the analysis leaving 306 available for PCA.

Variables such as the composition percent of vitrinite, liptinite, inertinite,

carbon, nitrogen, organic sulfur, oxygen, hydrogen, vola-tile matter, as well

as vitrinite reflectance, calorific value measured by Btu/lb, and moisture

content were used. Additional information included geographic location and

seam name. While those data were not used directly in the PCA, they were a

key to correlating the samples with data regarding bump histories.

Using the geographic data, the 306 records were compared to an MSHAdatabase of reported bump incidents in order to infer which samples had a

higher likelihood of being bump-positive or bump-negative. The

database included 369 individual cases reported to the Mining Safety and

Health Administration (MSHA) within the United States between 1983 and

2009. MSHA does not include information regarding the mined seam in these

acci-dent statistics. Consequently, an attempt was made to reconstruct this

data for the 82 mines represented by the database, through publicly available

lease information, MSHA reports of investiga-tion, and state coal

associations. These efforts were successful for 35 of these mines. The coal

seams identified as having been exca-vated by mines with a history of

dynamic failure phenomena were cross referenced with the geographic

information in the list of coal records provided by the PSU Coal Sample

Database. Those records correlating with a mine in which bump events had

been reported

were designated as bump-positive. If no association existed between a given

coal record and one of these 35 mines, it was des-ignated as bump-negative.

There is some inherent error in identi-fying the bump status of records in this

way, due to our inability to reconstruct seam information for each mine

represented within the database of reported bump incidents. Some records

identified as bump-negative, could, in fact, be bump-positive. Geographic

data for both coal records and MSHA accident reports, however, is readily

available. Given our ability to verify that bump-negative records come from

counties in which no bumps were reported ensures that the magnitude of this

error for this study is relatively small. Additionally, while error could exist in

the iden-tification of bump-negative seams, no such error exists in those that

have been designated as bump-positive.

Initially, all available measurements were used in a PCA to determine

their relative importance in defining the principal component axes. An

assessment of variable importance was determined using the principal

component loadings ( Fig. 2) where a variable was removed if (1) it was

mostly uncorrelated (


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(sa = yellow), low-volatile bituminous (lvb = green), medium-volatile (mvb =

green), high-volatile A bituminous (hvAb = teal), high-volatile B bituminous

(hvBb = purple), and sub-anthracite (sa = pink).

Once a variable was removed, the PCA procedure repeated until the

numbers of principal components describing the data were few ( 63). This

procedure continued until a clear relationship between bump history or coal

rank was observed on the principal compo-nent score (PC-score) plots.

Variables were scaled to unit variance prior to PCA to suppress the influence

of measurement unit. PCA and data visualization were performed using the

statistics package in Matlab 2013.

3. Results and discussion

Figs. 2 and 3 indicate an unambiguous correlation between pos-itive

bump history (D) and low organic sulfur content when organic sulfur (%) and

volatile matter (%) are used as variables in a PCA. In other words, we can

find that in Fig. 2 samples with a pos-itive bump history cluster near the

bottom of the base of the large triangular scatter of samples. Additionally, we

see from Fig. 3that the base of the triangular point-scatter is defined by a low

loading of organic sulfur, i.e., samples containing a large amount of organicsulfur content reside near the precipice of the triangular scatter whereas those

with positive bump history cluster near the base of the triangular scatter. In

fact, the uppermost limit of organic sulfur content within the bump-positive

samples was 2.07%. However, the average sulfur content for the bump-

positive subset was much lower, at 0.71%. Below a threshold of

approximately 2%, the number of records with a history of dynamic failure

increases with decreasing sulfur content ( Fig. 4).

A principle component loadings plot indicates that high organic sulfur

content and low volatile matter (%), approximating coal rank, are negatively

correlated to bump history ( Fig. 3).As noted in Fig. 4, the apparent normal distribution of bump positive

samples within a range of sulfur and volatile matter com-positions suggestthat there may be a range of values for both of these values most commonly

associated with dynamic failure incidents.

Volatile matter describes the lighter hydrocarbons liberated from the coal

during the combustion process. Therefore, it is important to recognize that the

fraction of elemental components (hydrogen, oxygen, and carbon) roughly

approximate volatile mat-ter composition. Organic sulfur may be defined as

sulfur in the coal matrix that is not a sulfate and is not pyritic in nature [26].

More importantly, this relationship holds regardless of location; it is true of

both Eastern and Western coal mining operations. It is impor-tant to

emphasize that stress-related variables pertinent to dynamic failure, such as

overburden depth, mining methods, local stratigraphy, and the presence of

multiple seam mining, have not been taken into account in this study. In spite

of this, sulfur content appears to provide a reasonable measure of the coals

inherent

18Volatile matter (%)

16Organic sulfur (10) (%)

samples

141210

of

8

Number

642

0 10 20 30 40 50 60Compositional percentage

Fig. 4.Number of bump-positive samples versus compositional percentages ofvolatile matterand organic sulfur.

capability for dynamic failure, independent of other mechanical or

stratigraphic factors. To elaborate, a coal with high inherent bump-

susceptibility could, in fact, never do so, if not sufficiently stressed. Coals

with positive bump histories have clearly been exposed to the necessary

stresses required to produce bumping; whereas, this may not be the case in the

non-bumping sample subset.

PCA also revealed a linear distribution of samples on the PC-scores plot

( Fig. 2) according to coal rank. Fig. 3 indicates that this distribution was

dictated entirely by the percentage of volatile matter present in each sample.

This behavior is unsurprising in absolute terms, considering that coal rank is

defined by calorimet-ric methods and fixed carbon percentage that indirectly

reflect the content of volatile matter present in a given sample [27]. More

importantly, coals with ranks in the low to high volatile bitumi-nous range

show a greater fraction of total samples with a positive bump history. It is

important to note that, as previously discussed; volatile matter is a convenient

way to describe a combination of other elemental and molecular variables that

are liberated during the combustion process. It could be that it is one of these

variables specifically, rather than the overall compositional percentage of

volatile matter which is significant in these results. Further analy-sis is

required to verify the role of volatile matter versus an isolated parameter or

set of parameters within this overarching category before more confident

assertions may be made as to its true role. Additionally, coal rank is a

function of coal maturity, which could subsequently be associated with

geologic and stratigraphic factors not accounted for in this study. It could be

these factors that are contributing to the occurrence of dynamic failure

phenomena, and they are being accounted for by proxy through volatile

matter percentage. While the correlation between organic sulfur content and

bumping is clear, it is premature at this stage of the analysis to assert an

obvious connection between rank and volatile matter in understanding bump

history. In that, it is entirely possible that only organic sulfur content,

appropriate geological and stress conditions actually mediate the occurrence

of dynamic failure.

Fig. 4 illustratesthe number of bump positive samples within the sampleset with respect to their compositional percentages of sulfur and volatile

matter. This suggests that there could be a range of values for both organic

sulfur and volatile matter content within which dynamic failure events are

most likely to occur. For organic sulfur composition, this range appears to be

between roughly 0.40% and 0.70%. For volatile matter, this range is between

roughly 38% and 41%. The ratio of volatile matter to organic sulfur (VM/S)

is a convenient way to simultaneously describe the range within which a

given sample will fall. Thus, it can be stated that within this sample set, a

VM/S ratio between approximately 59 and 95 is associated with a higher

number of bump-positive samples. It is important to note, however, that the

upper limit in this range is a soft limit and could be reflective of the relative

scarcity of sam-ples with VM/S values greater than 95. In fact, there were

only 9 of these, 8 of which were categorized as bump-positive. The relation-

ship between VM/S and bump-proneness ( Fig. 5), indeed, suggests thatvolatile-material-rich coals are more susceptible to bumping, as are sulfur-

lean coals. When using the VM/S ratio, some of the outlying data points for

sulfur content or volatile matter content alone are accounted for. For instance,

the average sulfur content of coal in Columbia County, Pennsylvania, is

extremely low at 0.56%. This is lower than the average sulfur content for

bump-prone coals. However, this appears to be a non-bumping seam. This

could be explained by the average volatile matter content from coal in this

county, which is also extremely low at 4.51%. This and similar cases suggest

that it is the combination of these which could, in fact, correlate most closely

with bump positive history.

The graph illustrates an overall decrease in the number of bump-negativerecords as VM/S increases. Likewise, it illustrates


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H. Lawson et al. / International Journal of Mining Science and Technology 26 (2016) 38 7

120Bump positive

100 100

withpositive

history(%) 100 99

89Bump negative

9283 83

80 71 7160

Perce

ntagerecords

ornegativebump

545050

4640

29 29

20 1117

817

0 1 00

0-10

10-20

20-30

30-40

40-50

50-60

60-70

70-80

80-90

90-100

+100

VM/ S value

Fig. 5. Percentage of bump-positive and bump-negative samples versus their VM/Svalues.

60Bump negative

50 Bump positiveVM/S=20

(%)

40

matter

30

Volatile

2010

0 2 4 6 8 10Sulfur (%)

Fig. 6. Percentage of volatile matter versus organic sulfur (528 sample records).

an increase in the number of bump-positive records as VM/S increases ( Fig.

5).No bumps were reported in seams with a VM/S ratio of less than 24.3,

based upon data from the 306 coal records. To explore whether or not this

could represent a lower limit for this variable below which bumps do not tend

to manifest at the stresses gener-ated by current mining practices, the 222 coal

records eliminated from the original sample set were re-introduced to thedatabase and identified as bump-positive or bump-negative, by means of the

same protocol utilized in the original 306 records. These records were then

plotted by compositional percentage of volatile matter (y-axis) versus

organics sulfur (x-axis). To assess the signif-icance of the VM/S ratio, a line

with a slope of VM/S = 20 was fitted to the plotted records ( Fig. 6). Of the

plotted records, 77 were cat-egorized as bump-positive. Of these, 97.4% were

well above the line VM/S = 20. This discriminator was less successful at

account-ing for the remaining 449 bump-negative cases; only 67% of these

fell below the VM/S = 20 line. This could be partially accounted for by the

possibility of false-negatives in the VM/S > 20 range and the lack of other

factors contributing to dynamic failure relevant to these cases (e.g., sufficient

overburdens and stiff stratigraphy). In other words, VM/S values of greater

than 20 do not guarantee bumping-quite the opposite, in fact; dynamic failure

events are relatively rare. However, given the presence of other factorsassociated with dynamic failure events, mines operating within these seams

could be at significantly higher risk than mines oper-ating in seams with

lower VM/S values. Consequently, a high VM/S ratio may be considered a

necessary but insufficient criterion to facilitate dynamic failure events.

These results represent a qualitative, empirical link between the organic

sulfur and volatile matter content of coals and their innate susceptibility

towards dynamic failure. These results beg the issues of overburden depth,

mining method, the possibility of multi-seam mining interactions, etc. It is

imperative to incorporate the influ-ences of these and other variables, and also

to further explore the role of volatile matter, if a holistic picture of bumpingbehavior

is to be constructed. Current NIOSH research seeks to create a quantitative

model for prediction of dynamic failure behavior incorporating these data.

PCA-generated relationships between bump history and com-positional

percentages of vitrinite, liptinite, inertinite, carbon, nitrogen, and moisture

have proven to be ambiguous at this time. PCA analysis revealed secondary

correlations (albeit weaker) between positive bump history and higher than

average nitrogen, oxygen, and hydrogen. Positive bump history was also

correlated with higher than average liptinite content and lower than average

vitrinite content. Some of these, such as petrographic attributes, in particular,

could be correlated with other geologic influences not considered in this

relatively simple study. The nature of these sec-ondary relationships is the

subject of continued investigation in order to explore their potential utility in

predicting coal behavior. These relationships, however, appear to exist

independently of the link between VM/S to positive bump history and, at this

time, seem to be a less accurate indicator of bump susceptibility.

After re-introducing 222 previously eliminated coal records, 97.4% of

bumping-positive records fall above the line VM/S = 20. This delineation

successfully accounts for bump-negative cases with 67% accuracy.

4. Conclusions

PCA analysis using coal data from the Pennsylvania Stata Coal Bank has

revealed a very strong correlation between low organic sulfur content, high

volatile matter, and positive bump history. The number of bump-positive

samples was shown to increase with decreasing sulfur and increasing volatile

matter. By taking the ratio of volatile matter to sulfur, VM/S, a minimum

threshold for this value of 20 was effectively established, below which bumps

are not generally induced by the stresses experienced within the sam-ple set.

This limit successfully accounts for 97.4% of bump-positive records. Samples

with negative bump histories are less successfully accounted for at 67%; this

highlights the fact that both inherent susceptibility and appropriate stress

conditions are necessary to facilitate a dynamic failure. These results establish

that one coal could, in fact, be more inherently prone to bumping than another

and this susceptibility is directly correlated to its composition. These

observations further establish the necessity of addressing coals on a seam-by-

seam basis in coal bump research, rather than as a homogenous material. For

coal mines operated in seams with high VM/S values, the operators need to be

aware of their status as potentially high-risk for bumping, and mine

accordingly. This risk is inherent to the coal seam itself, independent of other

variables. Understanding this facet of dynamic failure phenomena is a new

piece to the puzzle, and could help to shed new light on developing a more

robust model for predicting coal bumps in the future.

Acknowledgments

Thanks to Gareth Mitchell for providing the coal records used in this

study. Special thanks also to Ted Klemetti and Deno Pappas for providing the

database of reported dynamic failure incidents used for correlation with coal

records.

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Geotechnical risk management to prevent coal outburst in room-

and-pillar mining

Zhang Peter

, Peterson Scott, Neilans Dan, Wade Scott, McGrady Ryan, Pugh JoeAlpha Natural Resources, Inc., Waynesburg 15370, USA

a r t i c l e i n f o

Article history:Received 29 July 2015Received in revised form 5 October 2015

Accepted 25 October 2015Available online 19 December 2015

Keywords:Coal outburstPillar retreatingPillar stabilityRisk management

a b s t r a c t

A coal outburst is a severe safety hazard in room-and-pillar mining under deep cover. It is more likely to occur during pillar

retreating. Multi-seam mining dramatically increases the risk of coal outburst within the influence zones created by remnant

pillars and gob-solid boundaries. Though coal outburst is gener-ally associated with heavy loading of coal pillars, its

occurrence is difficult to predict. Risk management provides a proactive tool to minimize coal outburst in room-and-pillar

mining under deep cover. Risk assessment is the first step in identifying and quantifying outburst risk factors. The primary risk

factors for coal outburst are overburden depth, roof and floor strength, geological anomalies, mining type, multi-seam mining,

and panel width. A risk assessment chart can be used to proactively screen out min-ing sections with high risk of coal outburst

for further analysis. Gob-solid boundaries and remnant pillars are critical factors in evaluation of the coal outburst risk of

multi-seam mining. Risk identification, risk assessment, geologic influence mapping, geotechnical evaluation, risk analysis,

risk mitigation, and mon-itoring are essential elements of coal outburst risk management process. Training is an integral part

of risk management for risk identification and communication between all the stakeholders including man-agement, technical

and safety personnel, and miners.

2015 Published by Elsevier B.V. on behalf of China University of Mining & Technology.

1. Introduction

A coal outburst is a sudden violent burst of coal from a pillar with broken

coal or blocks of coal forcibly ejected into open entries. A coal outburst is a

severe safety hazard as the mining crew is highly exposed at the site when the

event occurs. Though deep cover and strong roof and floor are underlying

geologic con-ditions of a potential burst incident, its real occurrence is also

the result of additional mining factors. In room-and-pillar mining, a coal

outburst could occur during both development and pillar retreating, but the

latter greatly increases the risk of outburst. The other risk factors of coal

outburst also include mining layout, multi-seam mining, and presence of

adjacent gob, cutting sequence, and local abnormal geologic conditions. Over

the years, the cases of coal outbursts have been studied by many researchers

and mining practitioners [16].

It is commonly believed that the coal outburst is the result of a sudden

release of elastic strain energy stored in coal pillars and is highly associated

with cutting into heavily loaded coal pillars, but its occurrence is a rare event

and is difficult to predict. A number of

e Corresponding author. Tel.: +1 724 6272267. E-mail

address: [email protected](P. Zhang).

engineering controls have been recommended to mitigate outburst potential.

For room-and-pillar mining, sufficient pillar sizes have been the primary

control for coal outburst prevention. The pillar design tools, such as ARMPS

and AMSS developed by NIOSH, have played an important role in the design

of stable pillars to prevent pillar collapse and squeezing as well as coal

outbursts. In fact, with the implementation of pillar design using proper

stability factors, pillar collapse and squeezing have been almost eliminated,

and the number of coal outbursts has been greatly reduced in the US over the

past decade. However, after a few outbursts occurred dur-ing pillar retreating

in the US over the past a few years, it has been realized that sufficient pillar

size is still not enough to prevent coal outbursts.

The investigations of the incidents showed that other factors such as

multi-seam mining, panel layout, cutting sequence, and local geologic factors

also seemed critical in causing the events. Therefore, it has become

imperative that additional proactive mea-sures beyond proper pillar design be

implemented to prevent coal outbursts.

Room-and-pillar mining is the main mining method used by Alpha

Natural Resources, and there are considerable sections that are practicing

pillar retreating under deep cover and multi-seam mining situations. To

reduce the probability of coal outbursts, the

http://dx.doi.org/10.1016/j.ijmst.2015.11.003
http://dx.doi.org/10.1016/j.ijmst.2015.11.003http://dx.doi.org/10.1016/j.ijmst.2015.11.003http://dx.doi.org/10.1016/j.ijmst.2015.11.003http://www.sciencedirect.com/science/journal/20952686http://www.sciencedirect.com/science/journal/20952686http://www.elsevier.com/locate/ijmstmailto:[email protected]:[email protected]:[email protected]:[email protected]://www.elsevier.com/locate/ijmsthttp://www.sciencedirect.com/science/journal/20952686http://dx.doi.org/10.1016/j.ijmst.2015.11.003


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2095-2686/ 2015 Published by Elsevier B.V. on behalf of China University of Mining & Technology.


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10 P. Zhang et al. / International Journal of Mining Science and Technology 26 (2016) 918

company has developed and implemented a risk management pro-cess to

identify, analyze, and mitigate and control coal outbursts through geologic

influence mapping, engineering evaluation, mon-itoring, and training.

2. Primary risk factors for outburst occurrence

2.1. Understanding coal outburst risk Numberofcoaloutbursts

4035

A30 A indicates the coal25 seams alpha is mining201510

A A A5 A

A A A A0Coal outburst is a type of pillar failure that can occur under excessive

loading. It often concerns the local stability of an individ-ual pillar or a small

group of pillars under high stress. Most of the outburst incidents have

happened during cutting into heavily-loaded pillars. Fig. 1 illustrates the

conditions of a coal outburst occurrence. High vertical stress exerted on a

pillar makes it store a great amount of energy. Strong roof and floor provide

firm lateral confinement to the pillar so that the stored energy cannot be dis-

sipated easily by rib deformation. An outburst could occur when the pillar or

a portion of the pillar is loaded to a critical state at which no more elastic

energy can be stored by additional loading. The driving force for an outburst

event is the existing high stress level in the pillar and a release of the

confinement that holds in the high stress.

It has been known that coal outbursts take place in Appalachia when

mining with strong roof and floor under deep cover, but it is difficult to

predict whether and where exactly they would occur. Because of the

uncertainty of their occurrence, risk always exists when mining under burst-

favorable conditions.Coal outbursts are rare events, but their occurrence is detrimen-tal to

safety with a high possibility of injuries and fatalities. The risk of the outburst

can be defined as the likelihood or probability of an outburst event under a

given geologic and mining condition. It is a one hit event, which is in contrast

with the general definition of risk by the number of events over population. It

is so difficult to describe by a quantitative probability that a qualitative

description such as low risk, moderate risk, and high risk can be practically

used for the purpose of risk management. Although an outburst event is most

likely to involve injuries or fatalities, some small scale bursts or precursorevents, because they have no significant consequences, could be very likely

neglected. To prevent outburst reoccurrence, it is very important to evaluate

any small or precur-sor outburst incidents and to mitigate the risk of a

subsequent large incident occurrence.

The other aspects of outburst risk deal with exposure and con-sequence.

The exposure refers to the frequency, duration, and the number of people

exposed at the risk site. As outbursts often occur when mining activity is

going on, the exposure is always high. The outburst risk in pillar retreating

can be reduced by safe positioning at the face as well as reducing the number

of people working in by the pillaring line. The risk can also be reduced by

administrative controls like setting up posts or shields to protect people who

are frequently exposed to the risk. All of these are important to workplacesafety, but this paper mainly focuses on the risk man-agement process of how

to reduce the probability of coal outbursts.

Stress

Coal outburst

Pillar Strong roof/floorsqueeze

Weak roof/floor

Strain

Fig. 1. Conditions of a coal outburst occurrence.

.4 .32gasNo No TillerEagle

groveDarby Chilton Creech.Harlan banner groveBeckley No Elswick Kelliokakellioka

PocahontasPocahontasUpper cedar Cedar above Powellton

Upper DCoal seams with reported o utburst

Fig. 2. Occurrence of coal outbursts in coal seams in central Appalachia.

2.2. Primary risk factors

Coal outbursts are more likely to occur in certain geologic and mining

conditions. The primary risk factors for outbursts can be divided into geologic

factors and mining factors. The geologic risk factors include: overburden

depth greater than 200250 m; strong roof which could overhang a certain

distance over the gob; strong floor which does not heave readily; presence ofgeological anoma-lies such as faults, floor rolls, and sandstone channels; and

abrupt change of coal seam thickness.

Coal outbursts are known to occur in both the eastern and west-ern coal

fields in the US. Coals susceptibility to outburst seems to have little to do

with its chemical compositions and mechanical properties, for outburst

history has shown that any type of coal could burst under favorable

conditions. Fig. 2shows the occur-rence of coal outbursts in the coal seams

in Central Appalachia. The number of outbursts in a particular seam is not an

indication that the seam is more prone to outburst. History has shown that

outbursts occurred in almost all of the coal seams if the outburst conditions

were met. A coal outburst is more likely to occur in a seam or in a mine with

outburst history, but that does not preclude the possibility of an outburst event

in a seam or in a mine with no outburst history.

Risk factors related to mining are associated with increase of pillar

loading as a result of current and previous mining activities, and these factors

include development, pillar retreating, multi-seam mining, panel layout, and

cutting sequence. Development mining and pillar retreating are primary

driving factors that increase vertical stress in pillars. Pillars at the pillaring

line could be heavily loaded if there are strong roof overhangs over a large

area into the gob. The increase of pillar loading could also come from the

adjacent pillared gob separated by barrier pillars and from multi-seam mining

with the existence of gob-solid bound-aries and remnant pillars. Panel width

is also an important factor for pillar loading during development due to the

arching effect, and during retreating due to smaller abutment load with

subcriti-cal gob width. The stress in the pillars in the retreat face changes

dynamically as mining take places from cut to cut. Local high stress in a

particular pillar or a group of pillars in a retreat face could be created by a

certain mining sequence, delayed roof caving, or unsystematically-left stumps

or blocks.

3. Coal outburst risk assessment

3.1. Quantification of primary risk factors

In order to proactively manage the outburst risk, it is important to

quantitatively describe the primary risk factors. The effect of the primary riskfactors on the probability of an outburst occurrence


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P. Zhang et al. / International Journal of Mining Science and Technology 26 (2016) 918 11

depends on the amount of pillar loading to be increased by a par-ticular

factor.Strong roof and floor are necessary conditions for coal out-bursts.

Overburden depth is a quantitative geologic factor to describe the probability

of the outburst occurrence. The overbur-den depth is the primary source of

pillar loading, and obviously the probability of an outburst increases with

overburden depth. Fig. 3 shows the distribution of coal outbursts with

overburdendepth in the ARMPS database, cited in Research Report on theCoal Pillar Recovery under Deep Cover, Office of Mine Safety and Health

Research, National Institute for Occupational Safety and Health, 2010.

Considering that less coal is mined out from deeper cover, the probability

of outbursts increases greatly with overburden depth.Each of the mining factors would contribute to a stress change in the

pillars under its influence, but the amount of change and dif-ficulty of

determination vary by each factor. Fig. 4shows the esti-mated average stress

increase in the pillars under influence in terms of its ratio to overburden

stress, and its relative certainty. As outburst risk concerns the local stability of

pillars, the average stress increase refers to the pillars in the influence zone

with the highest stress increase. Development stress is determined by

extraction ratio and is relatively certain. The side abutment pressure from the

adjacent gob and multi-seam stress around gob-solid boundaries are not

significantly high, but moderately uncertain. Pillar retreating and multi-seam

mining can cause the greatest amount of stress increase with remnant pillars

left. Gob caving, shape of the remnant pillars, and interburden and overbur-

den geology determine how much pressure could be transferred to the pillars

at the influence zone. The estimation of this stress increase is more difficult

and also includes a greater range of uncertainty. The effect of panel width on

pillar loading cannot be neglected. A narrow panel can reduce the stress in

pillars both for development mining and pillar retreating. The combined effect

of all the mining factors would represent the stress level increase in the pillars

at the influence zone, and thus the risk level of out-burst caused by mining

factors.

3.2. Conceptual outburst risk assessment model

To better assess the outburst risk by both geologic and mining factors, a

conceptual outburst risk assessment model is given as shown in Fig. 5. The

risk rating along the horizontal axis is based on the combined effect of the

primary mining factors. The total outburst risk level is a combination of both

geologic and mining factors, and is determined by the area defined by the

overburden depth and the risk rating by the mining factors. A third axis

toward the upper right corner represents the total risk level and probabil-ity of

the outburst occurrence. The risk level can be classified as three zones: high

risk zone, moderate risk zone and low risk zone. This risk assessment model

is used to develop a risk assessment chart to screen the outburst risk for all the

mining sections of the company.

100Burst

ofcases 75 Squeeze/Collapse

Success

50

Number

25

60 120 180 240 300 360 420 480 540 600670Depth of cover (m)

Fig. 3. Distribution of coal outbursts with overburden depth in ARMPS database.

Relatively Moderately Highly

Averagepillarstressincrease

certain uncertain uncertain

tooverburdenstressratio

3

2

1

Develop- Side Pillar Multi-seam Multi-seamment abutment retreating gob-solid remnant

from oundary pillaradjacent gob

Primary mining factors

Fig. 4. Average stress increase in pillars by mining factors.

and coal

Risk levelofoccurrence

probabilitydept

h outburstHigh risk zone

Overburden

Moderate riskzone

Area=total risklevel

Low risk zoneRisk rating by mining factors

Fig. 5. Conceptual coal outburst risk assessment model.

3.3. Development of a risk assessment chart

Based on the proposed risk assessment model, overburden depth and

primary mining factors are used to develop a risk assess-ment chart. Though

the effect of primary mining factors on out-burst risk is to cause stress

increase in the pillars at the face, their risk is expressed by risk rating just for

an initial risk screen-ing. Table 1shows the risk ranking scheme. The totalrisk score is on a 100 scale, representing the risk rating for all the primary

mining factors. The higher the total risk score, the higher the risk level caused

by mining. The weight assigned to each risk factor is based on how much it

contributes to the risk level as well as its certainty. Mining over old workings

is assigned more weight because of its combination effect of both abutment

pressure and subsidence. The risk rankings for mining over old workings is

based on interburden thickness in comparison with multipliers of mining

height. The risk ranking for mining under old workings is based only on

intersburden thickness. The risk score assigned to each sub-factor is based on

the degree to which the factor would contribute to stress increase in the pillars

of interest.

To assess the outburst risks for the company mines, risk rating iscalculated for each of the active CM sections based on the data saved in the

quarterly-updated geotechnical database. Risk ranking is based on the current

mine maps and geologic data. All the min-ing sections are plotted in a risk

assessment chart as shown in Fig. 6. The outburst risk is classified using

three levels. The risk levels are defined by the mining situations with

commonly-believed risk level as shown in Table 2. The three outburst acci-

dents that occurred in Appalachia over the past ten years are also plotted in

the chart. It is obvious that they fall into the high risk zone.

The risk assessment chart is used to screen the sections with potential

outburst risk and to assign priority for further risk anal-ysis. The risk chart is

updated every quarter to reflect the current mining activities. Risk assessment

is the first step of the risk man-agement process.


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Table 1Coal outburst risk ranking.

Mining method (weight = 20%) Mining over old workings Mining under old workings Panel width (weigh = 20%)(weight = 35%) (weight = 25%)

Parameter Risk score Parameter Risk score Parameter (m) Risk score Parameter (m) Risk scoreD 5 D 5 D 5 P < 130 5R 20 I > 50H 10 I > 30 10 P = 130150 10

I = 2550H

20

I = 1530

20

P > 150

20

I = 1025H 30 I < 15 30I < 10H 35

Notes: D = development; R = retreat; I = interburden thickness; H = mining height; and P = panel width.

2000Moderate High risk zone

1800 risk zone Huff1600

(m)

C-2 minecreek

1400Low risk

depth

1200zoneMine

Overburden 1000 Brody section

800 mine Outburst600 fatality400 Risk level200 definition

point0 10 20 30 40 50 60 70 80Risk rating

Fig. 6. Alpha coal outburst risk assessment chart.

Table 2Mining situations for coal outburst risk classification.

Risk Overburden Mining Multi-seam Panel Risklevel depth (m) method mining width (m) ratingLow 150 50High >425 Retreat None >150 40High >600 Retreat None


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Old gob Old gobOld gob

Retreat direction RetreatdirectionRetreat direction

(a) Retreat direction is (b) Retreat direction is (c) Retreat direction isperpend icular to th e angled to the gob-solid paralle l to the gob- solid

gob-solid box oundary boundary

Potentially heavy-loadedpillars under one sideabutment pressure

Potentially heavy-loaded

pillars under abutmentpressure from two sides

Fig. 8. Gob-solid boundaries of different orientation to retreat direction and their influence on pillar loading in multi-seam influence zone.

could be formed from mining in different coal seams. Remnant pil-lars take

abutment pressure from at least two sides and are highly stressed. The amount

of stress transferred to the pillars at the cur-rent seam depends on interburden

thickness and geology. Promon-tory and isolated remnant pillars take

abutment pressure from at least three sides and could be very highly-stressed

if not yielded. The pillars under or above remnant pillars could be under high

risk of outburst due to multi-seam stress. The multi-seam stress from the

remnant pillars could be determined by numerical modeling, but the results

should be verified by underground mapping and observation.

Pillar sizing is also part of risk analysis. Both the pillars in the panels and

barrier pillars should be designed with proper stability factors. Analytical

methods, numerical modeling, and underground mapping are used to estimate

the stress level in the pillars in retreat sections for risk mitigation decisions.

Because the source of outburst risk comes from the high stress in pillars,

stress analy-sis is an important part of risk analysis.

5.2. Risk mitigation and decision making

Outburst risk is treated by risk level determined from risk anal-ysis. Risk

mitigation measures are based on estimated stress level in the individual

pillars during development. Table 3 shows the risk treatment criteria and

actions for pillar retreating. The philos-ophy of risk mitigation is to modify

the risk by avoiding, reducing, and optimizing the risk. The avoiding means to

avoid cutting into highly stressed pillars, while the reducing means to reduce

the stress level in the pillars through optimal layout and operational controls.

A pillar under average stress of 28 MPa is considered as highly stressed, and

would be under high risk of outburst during retreating. The pillars with high

risk are generally found within

multi-seam influence zones under or above remnant pillars. The easiest option

to mitigate the high risk is to avoid it by leaving the blocks in place. A pillar

under average stress of 20 MPa is con-sidered as low risk for retreating.

Engineering controls are applied for the pillars with moderate risk by

reducing the risk through optimization. The controls include optimal layout,

optimal mining direction, optimal cut sequence, proper stump size, and

sufficient roof support by leaving blocks.

As the real occurrence of outbursts is always uncertain, the decision for

risk mitigation also contains risk. Fig. 10 illustrates the risk of decision

making on risk treatment. Two types of mis-takes could be made by risk

analysis. Type one error is when the outburst would not occur, but the

evaluation shows that it would occur. In this case, a conservative decision

could be made to apply controls that are not needed. Type two errors are

when the out-burst would occur, but the evaluation shows that it would not

occur. In this case, a risky decision could be made by doing nothing or by

applying insufficient controls. The risky decision is caused by false risk

analysis.

To minimize type two errors, it is important to collect as much geologicinformation as possible, to verify the risk by underground mapping and

monitoring, and to calibrate numerical models with reliable geotechnical data

and information.

5.3. A case of risk analysis

This example concerns a pillaring panel in the Eagle Seam under up to

300 depth of cover. Fig. 11shows the geologic column in the mining area.

The roof is sandstone, and the floor is strong shale. Fig. 12 shows the area

where the Eagle Seam was developed withan 8-entry system and 21 m 21 m

center-to-center pillars, and the Powellton Seam, which was mined and

retreated 52 m above

Old gob Old gob Old gob

Remnant pillar Remnant pillarRemnant pillar

Potentially heavy-loadedpillars under abutmentpressure from two sides

Heavy-loaded pillarsunder abutment pressurefrom three or four sides

(a) A long narrow (b) A promontory (c) An isolatedremnant pillar remnant pillar remnant pillar

Fig. 9. Remnant pillars of different shape and their influence on pillar loading in multi-seam influence zone.


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Table 3Actions at different risk levels.

Average pillar stress level Risk level Control Mapping/monitoringduring development (MPa)

>20 High2028 Moderate


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Fig. 14. Rib condition in the area without multi-seam mining.

Fig. 15. Rib condition in the area under the remnant pillars.

tors of roof and floor strength, geologic anomalies, and overburden depths,

over/under mining, and corresponding interburden thick-ness. Important

geologic and geotechnical factors of this process include:

Creating an overburden thickness or depth of cover is poached based on

the coal seam structure and best available topography.

Generating a geologic model based on drill hole information, regional

trends, adjacent mine areas, and outcrops. Here a depo-sitional model is

developed and will include the roof lithology zones, transitional rock

types, and regional structure with faults. Additionally, rider and leader

coals are mapped.

The multi-seam interaction is mapped by identifying and prop-erly

locating previous mining above and below the coal seam to be mined.

Areas of mine extraction are mapped with gob-solid boundaries and

remnant pillars clearly identified for all previ-ous mining. Fig. 16

illustrates mapping previous mining and areas of high potential for multi-

seam interactions.

Interburden thickness and composition between the mined coal seams is

modeled and incorporated in the geotechnical analysis.

The second phase of geologic influence mapping involves in-minemapping with test hole optical scoping during development mining. Here, the

initial geologic model is refined with the actual conditions and rooffloor

lithology identified underground. In-mine mapping is the verification of risk

factors used in the risk analysis. Additionally, geologic features such as weak

roof or adverse conditions predicted during the initial mapping are assessed

for additional support needs and mine design adjust-ments. Important

geologic and geotechnical factors of this process to map include:

Geologic features and rooffloor lithology as well as the inter-pretation of

projected adverse conditions. Geologic anomalies such as weak rock

transitions, seam rolls, and structural fea-tures such as faults, slicken side

surfaces, and folds are mapped and later monitored.

Roof scoping in test holes is important to identify upper roof lithology for

roof stability and bolt anchor horizons as well as roof separations.

Section conditions are mapped for roof stability including falls, draw rock,

and sagging. Stress features such as rib sloughage, floor heave, and

unusual weighting of the roof. Mine heights and non-typical mine working

are identified.

Fig. 17 isan example of the completed geologic influence map.The third or

final phase of geologic influence mapping is pre-retreat mining panel

assessment. Mapping is generally a second and thorough review of the panel

with focus on changes in mine conditions primarily due to stress,

weathering, and time-dependent deterioration of entries. Mapping identifies

stress influ-ences such as rib sloughage and unusual weighting. Roof, rib, and

floor conditions and changes or deterioration of conditions are mapped. Areas

are identified for operations and management that require additional roof

support. Where a risk of coal outburst is identified from hard roof or other

geologic features coupled with high stress zones, pillars are identified to be

skipped or left in place. The retreat-mining plan maybe revised with the

additional information from the mapping. This is final pre-retreat mining pro-

duct and referred to in Appendix H of the MSHA Roof Control Plan

Handbook as the Hazard Map.

6.2. Monitoring

After the assessments show the panels are suitable for mining, the active

areas and retreat sections are routinely monitored and mapped. Here the focus

is on changing conditions from dynamic stress changes; roof, pillar, and floor

stability; and the mining pro-cess. Gob caving, cut sequence, stumps, and

blocks left are moni-tored for effects of outby pillar loading and stability.

Geologic features such as seam rolls and faults are monitored for stability and

stress changes with the advancing abutment pressure.

Pillar and development mining is a dynamic process and with monitoring

risk can be further assessed and quantified, and the extraction process can be

adjusted to the actual present conditions.If a small scale outburst occurs, the section will stop mining immediately,

and an evaluation will be performed for any neces-sary changes to the retreat

plan to minimize the risk of potential outbursts in subsequent mining.

7. Training for risk communication

7.1. Training development

A vital part of the geotechnical review process is making the general mine

population aware of the risks and hazards associated with mining in

geotechnical adverse conditions. There is a distinct advantage to havingeveryone at the mine site looking for potential signs of ground control failure,

as opposed to only having technical staff looking in specific locations on

occasional visits. In the fall of 2014, MSHA requested amendments to roof

control plans to address mining in deep cover conditions, defined as 366 m of

cover on advance and longwall or 300 m of cover on retreat, and coal out-

burst potential. Training of the personnel working in deep cover was included.

To address this request, Alphas Engineering Meth-ods and Standards

Division developed a comprehensive training course that not only covered

deep cover and coal outburst aware-ness, but also addressed general

geotechnical hazards, defined ter-minology, and taught proper reporting

procedures in the event that an adverse condition is observed.

Before any specific training was developed it was first impor-tant to asktwo basic questions: who is our target audience, and


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pillarPowellton

remnant

gobsolid2Gas

boundarygob

boundaryOverlappingsolid

gob boundaryPowellton

solid solidboundary

go 2Gas

gobboundary

gob Powelltonsolid

boundaryOverlapping

solid

Observedinteraction

gobPowelltonboundarysolid

1240

7070

1240

1240

Fig. 16. Over and under mining multi-seam influence and the predicted interaction areas.

Fig. 17. Completed geologic influence map for all mine areas.

what do we want them to do differently? The end goal was to make the entire

mine population, which includes a wide spectrum of individuals with varying

levels of experience, aware of geotechni-cal risks, how to identify them, and

prepared to notify manage-ment when the mine is showing indications of a

potential problem. To accomplish this goal, the training was divided into three

modules: namely Inform, Identify and Prevention.

Each module is designed to be as interactive as possible, requir-ing

feedback from the trainees to keep them engaged and to better assess their

level of understanding of the material.

7.2. Inform

This module educates participants about geotechnical hazards and their

causes. Coal outbursts in p

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