Application of laser scanning for rock mass characterization and discrete fracture network generation in an underground limestone mine

Abstract

Terrestrial laser scanning (TLS) is a useful technology for rock mass characterization. A laser scanner produces a massive point cloud of a scanned area, such as an exposed rock surface in an underground tunnel, with millimeter precision. The density of the point cloud depends on several parameters from both the TLS operational conditions and the specifications of the project, such as the resolution and the quality of the laser scan, the section of the tunnel, the distance between scanning stations, and the purpose of the scans. One purpose of the scan can be to characterize the rock mass and statistically analyze the discontinuities that compose it for further discontinuous modeling. In these instances, additional data processing and a detailed analysis should be performed on the point cloud to extract the parameters to define a discrete fracture network (DFN) for each discontinuity set. I-site studio is a point cloud processing software that allows users to edit and process laser scans. This software contains a set of geotechnical analysis tools that assist engineers during the structural mapping process, allowing for greater and more representative data regarding the structural information of the rock mass, which may be used for generating DFNs. This paper presents the procedures used during a laser scan for characterizing discontinuities in an underground limestone mine and the results of the scan as applied to the generation of DFNs for further discontinuous modeling.

Full text

Application of laser scanning for rock mass characterization and discrete
fracture network generation in an underground limestone mine
Juan J. Monsalve
⇑
, Jon Baggett, Richard Bishop, Nino Ripepi
Mining and Minerals Department, Virginia Polytechnic Institute & State University, Blacksburg, VA 24060, United States
article info
Article history:
Received 24 June 2018
Received in revised form 29 July 2018
Accepted 25 August 2018
Available online 12 December 2018
Keywords:
Rock mass characterization
Laser scanning
Discrete fracture network
I-site studio
abstract
Terrestrial laser scanning (TLS) is a useful technology for rock mass characterization. A laser scanner pro-
duces a massive point cloud of a scanned area, such as an exposed rock surface in an underground tunnel,
with millimeter precision. The density of the point cloud depends on several parameters from both the
TLS operational conditions and the specifications of the project, such as the resolution and the quality
of the laser scan, the section of the tunnel, the distance between scanning stations, and the purpose of
the scans. One purpose of the scan can be to characterize the rock mass and statistically analyze the
discontinuities that compose it for further discontinuous modeling. In these instances, additional data
processing and a detailed analysis should be performed on the point cloud to extract the parameters
to define a discrete fracture network (DFN) for each discontinuity set. I-site studio is a point cloud pro-
cessing software that allows users to edit and process laser scans. This software contains a set of geotech-
nical analysis tools that assist engineers during the structural mapping process, allowing for greater and
more representative data regarding the structural information of the rock mass, which may be used for
generating DFNs. This paper presents the procedures used during a laser scan for characterizing discon-
tinuities in an underground limestone mine and the results of the scan as applied to the generation of
DFNs for further discontinuous modeling.
Ó2018 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open
access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
According to the Mine Health and Safety Administration
(MSHA), between 2006 and 2016, the underground stone mining
industry had the highest fatality rate in 4 out of 10 years, compared
to any other kind of mining [1,2]. Additionally, during that same
time, 40% of the fatalities were due to ground control issues, such
as roof and rib collapses and pillar bursts. The National Institute for
Occupational Safety and Health (NIOSH) developed guidelines for
designing underground stone mines [3]. However, these guidelines
do not apply to all underground stone mines.
Monsalve, Baggett, Bishop, and Ripepi present a case study of an
underground limestone mine experiencing a structurally con-
trolled mode of failure, and analyze different methods to study that
type of instability [4]. They conclude that the integration of terres-
trial laser scanning (TLS) with discrete element modelling (DEM)
can be used to prevent rock falls in underground excavations to
enhance worker safety. However, an adequate rock mass charac-
terization and structural mapping must be performed in order to
generate reliable models that allow the engineer to have a better
understanding of the rock mass behavior.
The final product of a laser scan is a point cloud that represents
the surfaces of the objects scanned, in this case the rock surface in
an underground environment. The density of the point cloud
depends on a series of parameters from both the laser scanner
operational conditions and the specifications of the project, such
as the resolution and the quality of the laser scan, the section of
the tunnel, the distance between scanning stations, and the pur-
pose of the scans. The lead engineer defines these parameters.
One purpose of the scan can be to characterize the rock mass
and statistically analyze the discontinuities that compose it for fur-
ther discontinuous modeling. In these instances, additional data
processing and a detailed analysis should be performed on the
point cloud to extract the parameters that define a discrete fracture
network (DFN) for each discontinuity set. A DFN is a geometric rep-
resentation in three dimensions of a geological structure defined
by statistical information of the structure characteristics measured
in the field, such as orientation, density, and size [5].
This paper describes the methodology used in a case study mine
to perform a structural mapping on a rock mass in order to obtain
the input data needed to create a DFN for DEM. Different software
packages were used to perform these analyses:
https://doi.org/10.1016/j.ijmst.2018.11.009
2095-2686/Ó2018 Published by Elsevier B.V. on behalf of China University of Mining & Technology.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
⇑
Corresponding author.
E-mail address: jjmv94@vt.edu (J.J. Monsalve).
International Journal of Mining Science and Technology 29 (2019) 131–137
Contents lists available at ScienceDirect
International Journal of Mining Science and Technology
journal homepage: www.elsevier.com/locate/ijmst

(1) SCENE was used to import the laser scans and generate a
project point cloud [6].
(2) I-Site studio was used to perform the structural mapping
and to estimate the trace length and spacing of the different
fractures [7].
(3) Dips was used to characterize and group the discontinuity
sets [8].
(4) 3DEC
TM
was used to generate the DFN [9].
2. Geotechnical conditions
A specific area was defined in the case study mine to focus the
present research. The mine research team and personnel selected
this area because of the presence of significant ground control
issues, mainly defined as structurally controlled instability failure.
This was evidenced by: (1) jointing pattern where at least four
joint sets were well defined; observed wide-joint spacing, gener-
ally ranging from 0.6 to 2 m; amount of fallen blocks observed
on the floor with cubical and tabular shapes; joint surfaces defined
as mostly closed, flat and smooth, with a JRC ranging from 2 to 4,
completely dry and fresh; and other geological structures, such
as faults and contacts, that could generate a rock fall in the absence
of the required support.
This failure mechanism is enhanced by the multiple karst for-
mations present in the mine, which, during excavation, tends to
generate rock blocks up to 4 m
3
that pose a high risk for workers,
equipment, and the overall mining plan [4].
Fig. 1 shows a plan view of the area of interest. This illustrates a
karst structure that crosses both tunnels and has a variable aper-
ture reaching up to 0.5 m. Fig. 1a is a view from the tunnel to
the karst void, while Fig. 1b presents a pile of material that fell
down from the karst structure. Water and mud have also fallen
out of the karst void. For these reasons, laser scans were carried
out close to this area in order to map the structural conditions
and to perform further numerical modeling to understand and pre-
vent rock falls in this section of the mine.
3. Methodology
The following methodology describes in detail the procedures
used by the authors to perform the laser scans in the study area,
to import and process the information obtained from those scans,
and to create the respective DFNs resulting from the virtual struc-
tural mapping. This methodology was designed based on lessons
learned from experiences presented in previous works and prac-
tices using the laser scan and different software packages for these
procedures [10–12]. Additionally, documents, such as the laser
scan user manual and user guides on planning and performing
the laser scans provided by the laser scan provider company, were
used to complement this methodology [13,14].
3.1. Definition of operational conditions
A laser scanner is a surveying apparatus that produces a mas-
sive point cloud (millions of points per scan), indicating the posi-
tions of the scanned objects. This equipment sends an infrared
laser beam into the center of a rotating mirror, which deflects
the laser beam onto a vertical rotation into the environment being
scanned (Fig. 2a). Scattered light from surrounding objects is then
reflected back into the scanner. The equipment is able to identify
two waves: the one sent out by the equipment and the one
reflected by the object. The phase shift between both waves is used
to calculate the distance of the measured object [13]. Then, by
using angle encoders to measure the mirror rotation and horizontal
rotation of the scanner, the x,y,zcoordinates of each point are cal-
culated, resulting in a point cloud that is stored in a removable SD
memory card [13].Fig. 2b presents the vertical and horizontal rota-
tion of the laser scan. The laser scan unit used during this study
was a FARO
Ò
Focus3D, acquired by the Mining and Minerals Engi-
neering Department of Virginia Tech in 2011.
The two main variables to be defined during the scan are the
resolution and the quality. These two variables affect the time of
the scan and the density of the point cloud [14]. The size of the
excavation to be scanned, the distance of the scanner from the tun-
nel face, and the pacing between stations also affect the results.
According to Fekete, the optimal positioning of a laser scan from
the face is between 0.5 and 1 times the diameter of the excavation
[10]. In this particular case, the diameter of the excavation is 13 m,
and 1 times the diameter was used as the distance between the
scanner and the face.
Once the distance from the face to the scanner was defined, the
adequate resolution and quality were selected. Since these param-
eters vary from site to site, a set of scans were performed in order
to define which combination of resolution and quality values pro-
vided, in a reasonable scan time, a scan with an acceptable point
cloud density to perform an adequate structural mapping of the
rock mass.
Table 1 shows the different operational conditions tested in the
mine, the real time that it took to perform each scan, and the point
cloud size and the point cloud density. Twelve scans were per-
formed, processed, and converted into point clouds, which were
Fig. 1. Geotechnical conditions observed in the study area. Fig. 2. Operating scheme of the laser scanning equipment.
132 J.J. Monsalve et al. / International Journal of Mining Science and Technology 29 (2019) 131–137

imported to I-site studio for analysis. The total point count for each
cloud was measured. To measure the average point cloud density,
three 5 m 5 m mapping windows were generated, one on the
right wall, another one on the left wall, and the last one on the roof.
The amount of points contained within each mapping window was
measured and recorded. The point cloud density was calculated by
dividing the number of points by the area of each mapping window
(25 m
2
). Then, for each scan, the average point cloud density was
calculated by averaging the point cloud density on each mapping
window. The scans performed with the resolutions of 710.7 million
of points and 177.7 million of points were discarded since the
amount of data obtained exceeded the requirements and signifi-
cantly increased the scan time.
For selecting the best operational conditions, the real scan time
and the average point cloud density were taken into account. These
values were normalized and averaged. The best operational condi-
tions for this case were a resolution of 1/4 and a quality of 1x,
which required a scan time of 5 min and 12 s and yielded a point
cloud density of 11 points/cm
2
. The obtained point cloud density
is considered acceptable for structural mapping, bearing in mind
that previous work has performed structural mapping on a LiDAR
extracted point cloud with either a density of 4 points/cm
2
or with
a density of 16 points/cm
2
[15,16].
3.2. Stations location
Laser scanning has been used for multiple applications in both
the tunneling and mining industry, such as support evaluation,
scaling assessments, leakage mapping, analysis of structurally con-
trolled overbreak, structural mapping, roughness evaluation, and
deformation analysis [17]. Fekete determined that the best practice
for the laser scanner distance from the face is from 0.5 to 1 diam-
eter [11]. In addition, they defined the optimal separation between
stations is 1 diameter of the excavation to ensure the maximum
coverage of the area with minimum overlap between scans and
data redundancy [11].
Using these recommendations, the stations were positioned
13 m apart. Fig. 3 shows the estimated locations of the different
stations in which the laser scanner was set. The survey was per-
formed around the pillar between crosscuts 15 and 16.
3.3. Scans referencing
The laser scanner used does not have an integrated global posi-
tioning system. This means the scans are not georeferenced unless
there is a reference point with known coordinates. The laser scan-
ner used does have an internal compass and inclinometer that
allows orientation of each scan with respect to magnetic north
[13].
Without a georeferencing tool, the only way to integrate differ-
ent scans into a single model is to identify common reference
points, or objects, present between scans. FARO suggests the use
of checkboards or spherical references placed in strategic places
that can be detected from the two stations that will be referenced
together. In this particular case, 21.5 cm diameter inflatable balls
were used as spherical targets between scans. Table 2 shows the
recommended distance from the target to the laser as a function
of the target size and laser scan operational conditions. Consider-
ing the recommended target distances, spacing between stations,
and reference target sizes, four references were used in order to
reference the current station with the two other stations (one for-
ward and one behind). As the station moves, the references that
were behind it will be leapfrogged forward as the scans advance
in order to reference all the scans performed in the area together.
This procedure is depicted in Fig. 4.
3.4. Data importing and processing
Once the operational conditions and the scanning procedure
were defined, the laser scans were performed. Nine laser scanning
stations were set around the pillar between crosscuts 15 and 16. In
order to download the laser scans and convert them into point
clouds, the software SCENE was used. Before generating the final
compiled point cloud, the scans were spatially referenced with
each other using the reference points. This process is defined as
‘‘registration.”
Table 1
Proposed resolutions and qualities evaluated on the field.
Scan Resolution Quality Real scan time (hh:mm:ss) Point count Average point cloud density (points/cm
2
) Overall rating
Millions of points/scan
S-006 44.4 1/4 1x 0:05:12 38,103,388 11.42 5.04
S-007 44.4 1/4 2x 0:06:06 41,229,797 12.48 4.31
S-008 44.4 1/4 3x 0:07:53 41,684,133 12.64 3.36
S-009 44.4 1/4 4x 0:11:28 41,229,797 12.66 2.34
S-010 28.4 1/5 2x 0:05:27 26,273,301 8.06 4.79
S-011 28.4 1/5 3x 0:06:36 26,573,225 8.13 3.96
S-012 28.4 1/5 4x 0:08:54 26,630,099 7.97 2.95
Fig. 3. Estimated location of the laser scan stations.
Table 2
Recommended target spacing from laser scanner [14].
Sphere size 145 mm Sphere size 230 mm
Resolution
setting
Target distance
(max) (m)
Resolution
setting
Target distance
(max) (m)
1/16 5 1/16 7
1/10 7 1/10 11
1/8 9 1/8 14
1/5 15 1/5 22
1/4 18 1/4 27
1/2 37 1/2 55
1/1 73 1/1 110
J.J. Monsalve et al. / International Journal of Mining Science and Technology 29 (2019) 131–137 133

When a laser scan is performed, the scanner sets the position of
the mirror as the coordinates x=0,y=0 and z= 0. If a reference
point is observed from two different places, a spatial relation
between those stations can be made [6]. SCENE allows the user
to open two stations at the same time and select the common
points between them. Fig. 5 presents the spatial registration pro-
cess at Stations 015 and 016. There are two reference spheres on
each scan.
After referencing all the scans, SCENE evaluates the distance
error between points and the overlap between scan stations. This
software suggests that errors with a value of less than 8 mm and
overlap values greater than 25% provide acceptable results on the
registration process [6]. When the errors are greater than 20 mm
and the overlap between stations is less than 10%, the results from
the laser scan are not acceptable; therefore, the scanning process
must be repeated by using more reference objects or by reducing
the distance between stations. Values between this ranges may
still be accepted, considering a less precision on the laser scan
results.
Table 3 presents the results obtained from the registration of
the nine laser scans, which obtained acceptable values for both
the errors (maximum point error 6.7 mm) and the overlap (mini-
mum overlap 25.2%). According to these results, the recommenda-
tions made by Fekete regarding the laser scan station locations are
acceptable in this particular case. Finally, in order to proceed with
the analysis, the point cloud was saved as a laser scan file [10].
3.5. Structural data processing
I-Site studio is a point cloud processing software that allows
users to edit and process laser scans. This software contains a set
of geotechnical analysis tools that assist engineers during the
structural mapping process. This software allows for greater and
more representative data regarding the structural information of
the rock mass, which can be used to generate DFNs. The point
cloud generated from the laser scans was imported into I-Site stu-
dio to perform a structural mapping along the scanned tunnels. The
generated file contained 331,230,871 points, or 14.7 gigabytes, of
spatial data. Due to computational limitations, the point cloud
was divided into 10 smaller sections, and each section was divided
into right wall, left wall, and roof. This allowed researchers to per-
form the structural mapping with a useful amount of information
that prevented the system from crashing.
Fig. 6 shows the left wall of the third section generated from the
overall point cloud, where it is possible to observe the geological
structures. Some of these structures exhibit similar orientations
and relatively large exposed areas, while others are not easy to
observe due to the sight angle. Additionally, the bottom half of
Fig. 6 shows the mapped discontinuities output from I-Site studio.
For the mapping process, the points that belong to the same dis-
continuity plane have to be manually selected. Using the geotech-
nical tool ‘‘query dip and strike,” an average plane is generated that
accounts for the coordinates of each selected point. This process
was repeated along the whole model section by section. Fig. 7
Fig. 4. Reference objects placement along different scan stations.
Fig. 5. Registration and verification of stations 015 and 016.
Table 3
Point cloud registrations statistics [6].
Scan point statistics Obtained
value
Acceptable
value
Unacceptable
value
Maximum point error 6.7 mm <8 mm >20 mm
Mean point error 4.3 mm
Minimum overlap 25.2% >25.0% <10.0% Fig. 6. Structural mapping in I-Site.
134 J.J. Monsalve et al. / International Journal of Mining Science and Technology 29 (2019) 131–137

shows a plan view of the final point cloud with the mapped discon-
tinuities. In total, 874 structures were mapped in the area.
3.6. DFN generation
Once all the relevant structural features observed in the 3D
model were mapped, the structural data was exported into an Excel
file. Structural features extracted from a point cloud into I-Site stu-
dio contain x,y, andz coordinates, orientation information (dip, dip
direction, and strike, and size information (trace length and area).
This information was imported into the software dips, which was
used to analyze and classify the structures into joint sets. The
defined joint sets can be filtered and reimported into I-Site studio
as individual joint sets, where geotechnical tools can calculate the
spacing between discontinuities from the same family.
3DEC is a DEM software that can use DFNs to generate geolog-
ical structures in a rock mass. These DFNs are a set of discrete, pla-
nar, disk-shaped, and finite size fractures, which are used to cut
through the blocks that constitute the model. These sets of disks
are defined based on statistical information on the characteristics
of the measured fractures in the field, such as orientation, size,
and density [5]. All of these characteristics can be obtained from
the information mapped from the laser scans.
The orientation is defined by the dip (steepest declination) and
dip direction (measured clockwise from true north) of a plane. This
defines the spatial position with respect to the true north and a
horizontal plane [18]. Orientation parameters are defined as circu-
lar data. Due to this, the most adequate distribution to represent
this data is the Fisher distribution, which is used to represent 3D
orientation vectors [8]. This distribution depends on the factor k
that can be estimated form datasets greater than 30 poles. The soft-
ware dips is able to calculate each kcoefficient, mean dip, and dip
direction for each joint set.
While the actual size of a structure is difficult to measure, it is
regularly estimated as the trace length of the discontinuity, which
is the length of the exposed structure. In this case, the trace length
was considered the maximum length of the mapped plane. This
information provided a statistical distribution for the trace length
of each joint set.
Fracture density is a measure of the frequency of discontinu-
ities belonging to the same family. This parameter could be mea-
sured as the number of fractures per unit length (P
10
), the length
of fractures per unit area (P
21
), or the area of fractures per unit
volume (P
32
)[9]. When a DFN is generated in 3DEC, this param-
eter serves as a stopping condition [19]. For instance, when P
10
is
used as the density parameter, a sampling line is defined, and
3DEC will randomly create discs based on the predefined size
and orientation distributions until the number of fractures inter-
secting that scan line meet the value set as P
10
. For this work, the
P
10
estimate was based on the spacing of the discontinuities cal-
culated using I-Site studio, considering the definition of disconti-
nuity frequency on a scan line as the reciprocal of the mean
spacing [20]. In this case, spacing values less than 50 cm were
not considered when calculating the fracture density because
their respective fracture densities were not comparable with
those observed in the field.
Fig. 7. Plan view of the scanned area with structural features mapped.
Fig. 8. Stereo net analysis and trace length and fracture density summary statistics.
J.J. Monsalve et al. / International Journal of Mining Science and Technology 29 (2019) 131–137 135

4. Results and discussion
The methodology described in this work allowed the authors to
obtain a 3D virtual structural mapping of the area of interest. This
information is important to help visualize and understand the struc-
tural setting for an area of interest and to identify possible blocks
that may form and generate rock falls. Fig. 8 summarizes the virtual
structural mapping and the statistical analysis for the trace length
and the lineal fracture density of each identified structural set. Four
discontinuity sets were defined from the mapped discontinuities:
(1) Set 1 is almost perpendicular to the tunnel orientation and
presents a sub-vertical dip
(2) Sets 2 and 3 are oblique joints with a steep dip
(3) Set 4 corresponds to the bedding planes and contacts
between rock units, which are almost parallel to the tunnel
orientation and its mean dip of 29°.
Table 4 shows the statistical summary for the orientation, size,
and density of each individual discontinuity set. This information
was used to generate a DFN in 3DEC for each discontinuity set.
Fig. 9 shows each individual DFN and the intersection of all four
DFNs. It is also possible to observe a stereographic analysis of the
generated disks, which can be compared with the stereographic
analysis obtained from the virtual mapping. The major challenge
identified during DFN generation was density definition. If the lin-
eal density is considered, the value is only considered along a sin-
gle sample line. Therefore, if the control volume in which the
fracture network is being generated is significantly larger than
the sizes of the disks, a large number of disks will be generated
before the fracture density condition is met.
It is important to note that the DFNs obtained in this work that
resulted from a virtual mapping on a laser-scanned area do not
represent the final structural setting of the simulated rock mass.
3DEC uses these discs to cut through the blocks that conform the
model, ultimately obtaining a set of blocks with fractures that were
generated based on the DFNs. Because of this, it is important to
compare the final structural setting of the simulated rock mass
with conditions observed in the field. If these differ, the model
should be calibrated to obtain the best representation of the actual
rock mass. Additionally, it is worth mentioning that the DFN
models result from statistical distributions obtained from mea-
sured values; therefore, each time the model is run, it generates
a different DFN producing different outputs. In order to obtain a
significant result for the model, a stochastic analysis must be
performed to address significant conclusions from the simulations.
Table 4
Statistical summary of the joint properties for each joint set.
Parameter SET
S1
N= 157
S2
N= 127
S3
N=97
S4 (Bedding)
N=45
Orientation Dip (°) 8868 7529
Dip direction (°) 255 348 21 144
K (Fisher) 103.9 102.4 69.5 197.3
Size Distribution Log-normal Log-normal Log-normal Log-normal
Mean 0.353 0.318 0.018 0.778
Standard deviation 0.659 0.772 0.749 0.934
Density Number of fractures per unit length
of scan line (P10)
Normal
m= 1.011
r
= 0.495
Min = 0.18
Q2 = 0.576
Median = 1.180
Q3 = 1.577
Max = 1.883
Normal
m= 0.928
r
= 0.492
Normal
m= 0.941
r
= 0.477
Fig. 9. DFN generated based on the scanned data.
136 J.J. Monsalve et al. / International Journal of Mining Science and Technology 29 (2019) 131–137

5. Further work
Since the methodology presented in the present study was
developed to obtain information relevant for generating DFNs only,
no mechanical properties of discontinuities on the rock mass have
been measured yet. Thus, performing rock mechanics laboratory
tests to measure the joint strengths is necessary. In addition, field-
work aimed at measuring the wall strength and the roughness of
each joint set will be performed. In addition, intact rock samples
will also be collected to evaluate the mechanical properties of
the footwall, orebody, and hanging wall. These parameters will
be used as inputs for the numerical models and compared with
observed conditions in the mine.
6. Conclusions
This paper describes the methodology used to perform virtual
structural mapping in an underground mine from information
obtained from a set of laser scans in an area that presents a struc-
turally controlled failure mechanism. The following conclusions
are derived from this work:
(1) Planning the scanning project is fundamental in order to
save time during the scanning process, reduce errors on
the resulting point cloud, and obtain the necessary informa-
tion required for the specific need.
(2) While there is no existing guideline for performing an
underground laser-scanning project, the experiences from
other authors are valuable in defining the conditions of the
scanning. For this study, following the recommendations of
Fekete and Diedrichs allowed us to obtain a maximum point
error of 6.7 mm and a minimum overlap between scans of
25.2%, both acceptable values according to studies by
researchers [6,17].
(3) It is important that geologists or engineers performing the
structural mapping from the project point cloud have spent
adequate time in the field in order to avoid any misleading
interpretation and to ensure the results concur with those
observed in the field.
(4) Information extracted from the laser scans provides suffi-
cient information to perform a statistical analysis of the vari-
ables required to generate a DFN for each identified
structural set. However, further numerical models are
required to define the effectiveness of this methodology in
accurately representing the rock mass structure.
(5) In order to perform this kind of analysis, the integration of
four different software packages is required. Therefore, it is
important to define an adequate workflow, which identifies
the inputs required for each program and the outputs to be
obtained. It is also important for the engineer to understand
the limitations of each software and how these limitations
can affect the results of the models.
(6) Finally, the criteria of the engineer, based on their experi-
ence in the field and observed ground conditions, are funda-
mental for deriving a conclusion from the results of the
analyses.
Acknowledgements
This work is funded by the NIOSH Mining Program under Con-
tract No. 200-2016-91300. The authors would like to thank ITASCA
for their support and guidance during this project. Maptek is
acknowledged for providing a license of the software I-Site Studio.
Views expressed here are those of the authors and do not necessar-
ily represent those of any funding source.
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