Construction of sprayed concrete lining (SCL) ground support across the world utilizes the construct, verify and rework cycle. This methodology typically requires survey verification of the as-built result against design for each stage of the ground support installation. However, processing and analyzing the measurement data is a time consuming and often intensive manual process. Often once the survey information is available the construction crew will have already left, this will require rework on the next cycle.
Leveraging the latest in high-density LiDAR and
high-speed computing technologies, provides the
ability for construction crews to receive near realtime feedback of their SCL construction against
design. This potentially can significantly improve the
efficiency and quality of SCL reinforcement, while
reducing waste in construction.
In a typical shotcrete application stage, the thickness of shotcrete applied is highly dependent on the
skill and experience of the nozzlemen. Upon completion of the shotcrete placement the compliance
of the sprayed concrete thickness with the design
requirements is not known until after a survey is
completed. The survey results highlight areas of over
spray (excessive thickness) or under spray (deficient
thickness), resulting in shotcrete wastage or costly
For example, during application of shotcrete
nozzlemen often use bolt tips as guidance to allow
them to gauge the approximate depth of their placement. The nozzleman’s experience plays a large role
in ensuring that the correct thickness is achieved.
However, to reduce the amount of under spray sections and prevent rework the nozzleman may choose
to place more shotcrete than required.
In many tunnel and cavern projects, the design
profile of the tunnel or cavern is critical and requires
strict thickness tolerances during SCL construction
to ensure the as-built sections fall within the design
profile specifications. In these types of construction,
both under spray and over spray could result in
non-compliance which then requires very costly and
time-consuming rework. Often depth pins and string
lines are installed in the area to provide guidance to
the nozzlemen, allowing them to visually gauge when
they have achieved profile. The process of installing
depth pins and string lines are time consuming
and labor intensive. This significantly increases the
time and cost of construction. Once installed, the
nozzlemen will have to estimate the placement
thickness between the string lines, which once again
is heavily dependent on the skill and experience of
the nozzlemen. Spring 2020 | Shotcrete 25
Since the introduction of the Building Information
Modeling (BIM) standards, governments around the
world are rapidly adapting BIM for their construction
projects. This is evident in countries including Hong
Kong, Singapore, Norway and Sweden. Figure 1
show the core BIM construction workflow where
design and authoring of the architectural and tunnel
designs are done in 3D CAD software, such as Revit
and Civil3D, where a full 3D model of the completed
section is created. This is followed by the Virtual Design
and Construction (VDC) process where a complete
construction simulation is run using the CAD models.
This helps validate the both the construction process
and the schedule. Clash detection is also accomplished
with the design model to detect any potential design
conflicts before the construction plan is approved.
By using FPGA based System-on-Chip (SoC) technology, similar to the Zynq-mp processor core technology that can deliver teraflops of computing performance,
computationally intensive signal processing algorithms
can be implemented in the hardware using the
Programmable Logic portion of the system (see Figure
2). The Programmable Logic area, in yellow, allows the
computer designer to create custom digital signal processing cores, like GPUs, and execute them in parallel,
allowing high speed processing of large datasets. The
Programmable Logic area also has the benefit of having
dedicated memory banks that support concurrent
access, unlike conventional computer memory access,
via a common bus architecture. The architecture above
makes it possible to process high-density data, like
the 3D point cloud data produced by laser scanners in
Real-time in-situ measurement technology refers to a
portable measurement device equipped with onboard
high-speed computing capabilities to deliver live or near
real-time high-resolution information. One example is the
production of information such as deformation or shotcrete thickness results in 3D. Figure 3 shows the comparison between using a conventional LiDAR against an
in-situ LiDAR technology, in this case the Geotechnical
Monitoring LiDAR (GML).
BIM construction is one of the key reasons for the
use of laser scanner technology. 3D Laser scanners
are used to scan as-built construction elements.
Scanning is typically carried out by a survey team
where the laser scanner is deployed in the excavation
heading to collect the as-built scan data. This scan
data is then brought up to the project office, where
a powerful desktop computer analyses the data in a
georeferenced coordinate system. This point cloud data
set often requires some manual processing to correct
for measurement errors and to correlate with the design
data. The entire process is required before producing a
report that can be used for analysis and then feedback
to the construction crew. This process typically takes
between 2 to 4 hours per station. Hence, the use of
such technology in civil constructions are limited.
Since 2016 the rapid adoption of high-speed
embedded computing platforms, like FieldProgrammable Gate Array (FPGA) and Graphics
Processing Unit (GPU) processor cores for embedded
systems, advanced data processing has become
prevalent. These technologies allow battery-powered
devices to achieve computing performance of one trillion
floating-point operations per second (1teraFLOPS). Part
of this rapid adoption is due to the global development
of algorithms and processor cores for machine learning
platforms and real-time autonomous vehicle projects.
Fig. 1: BIM Construction Workflow
Fig. 2: Xilinx FPGA Architecture
26 Shotcrete | Spring 2020
In the above comparison, the ability to analyze and
report the desired construction results automatically and
in minutes has the potential to significantly change SCL
construction processes.
The GML technology was designed and developed by
GroundProbe, a mining technology company that supplies slope stability monitoring radar systems such as
the SSR-XT for the mining industry. Figure 4 shows the
GML system as a complete standalone battery-operated
LiDAR solution, that was designed to be a one person
operation. This technology is equipped with an onboard
high-speed computing device and signal processing
software, that can produce high density point cloud
information in real-time.
The first proof of suitability for the new technology was
to verify the measurement accuracy for shotcrete thickness measurements, against the existing total station
pick up by survey control.
The GML was deployed in various control environments in a tunnel project to verify the thickness measurements. The first method was to compare the results
of existing shotcrete thickness reports produced by
the survey pick-ups against the thickness reported by
the GML scanner. In this process, the GML was setup
next to the total station during conventional pickup to
scan the excavated sections before any shotcrete was
installed. Upon installation of the bolts and shotcrete,
both the GML and the total station were redeployed to
complete the final as-placed scan. These results were
tabulated in typical profile section views as per Figure 5.
Fig. 3: LiDAR vs In-situ LiDAR Workflow Comparison
In Figure 5, the GML results are in blue and the
results of the total station survey are in pink. Results can
be seen in all four scans, the GML results were almost
identical to the total station measurements.
In another verification test, core samples were drilled
to check the thickness against the GML measurements.
In this test, the GML was deployed to scan the section
before any shotcrete was installed. Once the shotcrete
had been placed and cured the drill rig was deployed to
drill four test holes, where the depth of the cores were
measured. These four holes were marked on the tunnel
surface to allow the user to locate the holes in the GML
Figure 6 shows the drill results in the GML SSR-Viewer software. The image was produced by the GML data
and clearly shows the marked holes. For each marked
hole a group of points were selected, creating the annotated figures shown in the figure. An average thickness
measurement was computed for each of the annotated
group of points and displayed in the charts.
Fig. 5: GML & Total Station Comparison Results
Fig. 4: Geotechnical Monitoring LiDAR (GML) Spring 2020 | Shotcrete 27
When operating in Profile Mode, the GML can import
BIM CAD models into the device and automatically
calculate the deviations against the design model. This
allowed the construction crew to have real-time feedback of their work while in the tunnel. Figure 7 shows
an example of the software operating in Construction
Guidance Mode in a tunnel excavation operation.
In Figure 7, the grey point cloud is the scan data and
it overlays the different design profile data, shown in purple and blue. The software automatically calculates the
Fig. 6: GML & Drill Test Comparison Results
Fig. 7: GML Profile Mode
Table 1: GML & Drill Test Comparison Results Table
Table 1 shows the core sample measurements,
against the GML measured results, for the placed
shotcrete thickness. The GML thickness measurements
were accurate to within 1-2 mm (0.04 to 0.08 in.). However, there was a large discrepancy with the results for
Fig 10373RH, as notated in the table below. This was
subsequently investigated, and it was found the core
samples had not been measured correctly.
Figure Name Drilled Results GML Results
Fig 10373 C 110mm 109.3mm
Fig 10372 75mm 75.4mm
Fig 10372 RH 120mm 118.2mm
Fig 10373 RH 100mm 52mm
distance (in millimeters) to and from the selected profile
lines, to produce the deviations in a hot-cold heat map.
The following case study was based on data from an
Australian tunneling project in 2018. The project used
GML for managing the shotcrete thickness for the primary lining ground support in a road header cut tunnel.
The typical construction issue faced in this project
was the amount of shotcrete being ordered for each cut.
Quantities were based on a calculated estimation, often
with large amounts of excess material being ordered
to accommodate rebound and the spraying skill of the
nozzlemen. During the spraying stage, the nozzlemen
have depth pins to gauge the spray thickness, hence
the final sprayed thickness varies widely depending on
the skill of the nozzleman.
In this project, the GML was used to provide in-situ
feedback to the nozzlemen to guide them to spray to
the required design thickness. Since the GML was introduced in a later stage of the project it was challenging
to change the operating procedure. This required
significant planning to be able to train and guide the
nozzlemen to spray to the correct thickness. Using
the GML system helped to reduce the overall shotcrete
usage for the project.
In the early stages of implementation, the GML was
used to characterize the quality of shotcrete application
by the different nozzlemen. Figure 8 shows the typical
spray quality before use of the GML to guide the
nozzlemen. The shotcrete thickness in the images were
represented with red for areas under design thickness,
purple for areas over design thickness and green for
areas with the desired design thickness. As illustrated
in in Figure 8a, the sprayer was able to cover the bolts
correctly but left large areas under sprayed between
the bolts. Figure 8b, shows the opposite. Often the
nozzlemen would overspray the entire area just to
ensure there were no under spray areas resulting in
using significantly more shotcrete than necessary.
28 Shotcrete | Spring 2020
The GML was used in the project for a total of eight
months and after the first month using the GML the project was able to reduce the shotcrete material orders by
30% for the remaining seven months of operation.
The following case study was based on data from another Australian tunneling project between March and May
of 2019. The project utilized GML for controlling the shotcrete spraying process for the tunnel-wide, final lining, to
reduce or eliminate rework due to shotcrete not meeting
the required minimum thickness.
In this final lining shotcrete application, shotcrete was
sprayed continuously between cross passage (CP) to
cross passage, completing a 390 ft (120 m) section at a
time. This required the shotcrete rig and crew to move
13 to 20 ft (4 to 6 m) each time, to complete shotcreting
the entire section. Given the design requirements for
thickness and the tunnel design profile, depth pins and
string lines were installed as guides prior to the spraying
of the final shotcrete lining. The string lines were installed
by a crew of two operators, one surveyor and the use of
Mobile Elevated Work Platform (MEWP). The installation
took the crews approximately two shifts to complete
each section. Figure 11 shows a tunnel section that has
the depth-pins and string-lines installed.
Fig. 8: Detection of overspray and under spray of shotcrete
During the first four weeks of the project, after buy-in
from the site engineers, foremen and nozzlemen, the
project started seeing improvement in the spray quality.
The sprayer was able to use the GML guidance to cover
up the thin spots as seen in Figure 8b. However, there
were still some amount of overspray. Figure 9 shows an
example where the nozzlemen was able to detect thin
areas and rectify them immediately using the GML.
Shortly after the first month, the majority of the nozzlemen were able to use the GML to guide their shotcrete placement to reduce the amount of over spray.
Figure 10 shows the reduction of overspray areas. In
this example, the nozzlemen were able to reduce 33%
of shotcrete usage by using the GML. More importantly,
this was achieved within two weeks.
Fig. 9: Nozzlemen guided to fix up thin spots in shotcrete application
Fig. 10: Reduction of shotcrete usage Spring 2020 | Shotcrete 29
Fig. 11: Depth-pin and string line installation in tunnel section
Fig. 12: GML scan showing under spray sections in Red
Fig. 13: GML Scan Color threshold for spray thickness
Prior to the use of GML, the nozzlemen were using
the string lines as a guide to spray the desired thickness
and profile. Once the spray was completed, the section
was surveyed to verify the shotcrete placement against
the design profile and required thickness. The project
ran for months and found there were too many thin
spots that required rework, despite the installation of
depth pins and string lines. The GML was then deployed
simply as a verification tool to capture the construction
baseline. Figure 12 shows a typical rework issue on the
Figure 12 shows thin spots represented in the hot
color palette (red) and over sprayed sections in the cold
color palette (purple). The thin spots were spread across
the entire sections requiring significant amounts of
rework and verification by dedicated rework crews.
The first key question regarding deployment of the
technology was whether it can operate in cycle, particularly during the SCL stage of the tunnel construction
process, to monitor spray conformance.
During spray conformance monitoring, the GML is
typically deployed next to the front stabilization jack
of the shotcrete rig and remains in place during the
entire spray sequence. This position allows a wide scan
area to be captured with minimal obstruction from the
shotcrete rig. The GML completes a baseline scan
under two minutes before the shotcrete operator starts
to spray. Once the shotcrete operator is satisfied with
the first pass, the boom is lowered, and a second scan
is captured. As shown in Figure 13, the results are then
presented to the shotcrete operator on a tablet to indicate any areas that have not reached the required thickness. The scanner operator then uses a laser pointer
or cap lamp to guide the shotcrete operator to respray
the thin areas. Once both operators are satisfied, a final
scan is taken to confirm the results.
The other key issue was the nature of the final lining
spraying process, were the shotcrete rig and crew
needed to advance at every 13 to 20 ft section. This
required the GML to be relocated with the rig to operate
30 Shotcrete | Spring 2020
in cycle. The GroundProbe team worked closely with
the shotcrete crew along with engineers to develop a
shotcreting sequence that allows the GML to operate in
cycle. Leveraging the rapid processing capabilities of the
GML, the system was able to operate without delay for
the majority of the construction.
Given the support of the engineering and final lining
team, and lots of teamwork, the project was able to
begin to achieve the desired results within the second
week of deployment. Figure 14 shows the desired spray
result. In Figure 14, it can clearly be seen that there were
no thin spots and the reduction of over sprayed sections
was significant.
This was a considerable improvement on the project
and the technology was introduced to cover more areas
of final lining construction within the project. Inside the
initial two months of deployment, the final lining teams
were able to complete 2.6 miles (4.2km) of final lining
construction without rework, significantly reducing the
amount of shotcrete material used for the project.
The rapid progression of powerful embedded computing
and LiDAR technology enables the development of near
real-time in-situ scanning solutions. These advances
allowed application of the new technology into SCL
construction operations and enabled modifying current
practices to achieve improved safety, quality and cost
The rapid advancements in computing technology is
certainly a key enabler, allowing technology designers to
develop tools that could significantly evolve the mining
and construction industry. However, the success of such
technology relies heavily on the people operating in the
industry. Our experience shows that the success of the
case studies referenced in this paper, depended heavily
on the engagement of the engineering and construction
crews, especially the nozzlemen. One of the biggest
challenges faced was the management of change in the
construction processes. Effective communication and a
collaborative approach, to reach a desired solution, was
critical to the success of introducing the changes. Leveraging the experience of the foreman and nozzlemen also
was a key element in the development of the technology
and the construction process.
Fig. 14: GML guided spray with zero non-compliant thin spots
Finally, project management teams also play an
important role in fostering the development of innovative technologies by adopting them at an early stage of
a project. This is critical for any emerging technology
to achieve the desired potential, and to incrementally
change the current state of the art.
We would like to thank the excavation and final lining
teams mentioned in this article, especially the nozzlemen
and site engineers for their contribution to the technology.
We would also like to thank the monitoring team from
21MT, led by Christian Reich, for their highly skilled operators that helped to train and integrate the GML technologies into the construction projects.
1. Chen, B.; Mares, D.; Carter, N.; Ayres, P.; Voysey, G., 2020,
“Leveraging Super-Computing and High-Density Lidar Technologies
for Real-Time Verification and Rectification of Tunnel Construction
Against Design,” ITA–AITES World Tunnel Congress AND 46th
General Assembly.
2. Bergmann, N. W., 2005, FPGA-Based reconfigurable system-onchip. Very Large-Scale Integration System-on-Chip (VLSI-SoC2005),
Perth, Australia, 17-19 October, 2005. Perth, Australia: International
Federation of Information.
3. D. Lv, X. Ying, Y. Cui, J. Song, K. Qian and M. Li, “Research on
the technology of LIDAR data processing,” 2017 First International
Conference on Electronics Instrumentation & Information Systems
(EIIS), Harbin, 2017, pp. 1-5. doi: 10.1109/EIIS.2017.8298694.
4. Processing. Altameemi, A. A., & Bergmann, N. W. (2016).
Enhancing FPGA softcore processors for digital signal processing
applications. 2016 Sixth International Symposium on Embedded
Computing and System Design (ISED), 294–298.
5. Zynq-mp-core-dual.png (800×900). (n.d.). Retrieved 18 September 2019, from
block-diagrams/zynq-mp-core-dual.png. Spring 2020 | Shotcrete 31
Did not see in the folder.
For the last 11 years, Ben Chen has played a lead technical and commercial role in delivering the
technology roadmap and his team was able to deliver an array of technologies to significantly diversify and grow the company’s brand, market share and revenue. More recently, his team has delivered
the Geotechnical Monitoring Lidar (GML) technology that won the Financial Review 2018 Most Innovative Products and Most Innovative Company awards. In the same year they also delivered the Geotechical Monitoring Station (GMS) technology that won the 2018 Good Product Design Awards.
Peter Ayres is the Lead – Tunnelling Solutions for GroundProbe and a former Technical Services Manager for
Orica. Currently he is working with the GroundProbe’s Product Development team in the development and
implementation of the GML system globally to both Mining and Civil industry. Over the past 12 years, he has
worked as a Tunnel Designer with Arup in New York, USA, followed by 6 years with Leighton Contractors (Asia)
Ltd. in Hong Kong as a Tunnelling Engineer and Blasting Engineer. Projects have included the 7 Line Extension, NY; West Kowloon Terminus & XRL822, HK; Harbour Area Treatment Scheme, HK; and the Tseung Kwan
O – Lam Tin Tunnel, HK. Peter has an M.Eng. in Mining Engineer from Camborne School of Mines – University
of Exeter and is currently studying for an LLM in Construction Law and Arbitration at Robert Gordon University.
Christian Reich is the Founder and Managing Director of 21MT, an Australian company committed
to implementing innovative technologies and solutions in the mining and tunneling industries. Since
2018, Christian has led 21MT in developing extensive experience using Groundprobe’s real-time GML
scan technology in Australian tunneling projects and has established a proven track record of efficiency improvements by integrating real-time scanning into the excavation cycle. Previously Christian
worked at Atlas Copco in Germany as the Product Manager for underground rock excavation equipment.
He graduated from Technische Universität Clausthal in mining engineering and business studies.
Nick Carter is the Lead – Technical Solutions at GroundProbe and has been involved in the innovation and
development of GroundProbe’s emerging technologies since 2011. He has travelled to the farthest and deepest
expanses of the earth to provide mining and civil markets with these technologies and globalized understanding
of the application requirements. He currently works on the Geotechnical Monitoring Lidar (GML) technology
with a small, core team of talented people who regularly solve challenging problems with creative solutions.
The GML has received innovation awards from the Australian Financial Review and most recently won the
Technology Transfer Award at the 2019 Institution of Engineering Technology (IET) Innovation Awards in London.