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Construction Management and Economics
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rcme20
Augmented Reality to overcome Visual
Management implementation barriers in
construction: a MEP case study
Patrick Dallasega, Felix Schulze & Andrea Revolti
To cite this article: Patrick Dallasega, Felix Schulze & Andrea Revolti (2022): Augmented Reality
to overcome Visual Management implementation barriers in construction: a MEP case study,
Construction Management and Economics, DOI: 10.1080/01446193.2022.2135748
To link to this article: https://doi.org/10.1080/01446193.2022.2135748
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 04 Nov 2022.
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Augmented Reality to overcome Visual Management implementation
barriers in construction: a MEP case study
Patrick Dallasega , Felix Schulze and Andrea Revolti
Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Universit
a 5, 39100, Bolzano, Italy
ABSTRACT
Visual Management (VM) is an important Lean method to enhance information flow and reduce
waste in construction. However, its adoption is hindered by several barriers. Scientific works
mention that emerging technologies can support or replace conventional VM practices, but
empirical evidence is missing. Based on an in-depth literature review, we derived the research
questions (RQs), arguing if Augmented Reality (AR) could mitigate VM implementation barriers.
Thus, a corresponding AR measurement model was developed. Through a case study of mech-
anical, electrical, and plumbing (MEP) installations in a multi-story apartment building, the RQs
were answered using an AR head-mounted display (HMD). To gather the necessary empirical
evidence, the data was collected through direct observations on-site and through semi-struc-
tured interviews. The study findings show that (1) AR provided time savings and generally satis-
factory accuracy levels. (2) AR demonstrably reduced the training effort to better support MEP
marking work. (3) The use of AR reduced the resistance to change to adopt VM practices,
although concerns were raised about poor ergonomics and work safety risks. Future research
activities should consist of investigating the potential of other emerging technologies to over-
come the common Lean implementation barriers in construction.
ARTICLE HISTORY
Received 16 April 2022
Accepted 9 October 2022
KEYWORDS
Augmented reality; lean
construction; visual
management; barriers;
KPIs; MEP
Introduction
Mechanical, Electrical, and Plumbing (MEP) systems are
an essential part of building services. Typical activities
of MEP include the installation of subsystems such as
heating, ventilation, and air conditioning systems
(HVAC), electrical power and lighting systems, firefight-
ing, and fire protection systems, as well as water sup-
ply and drainage systems. A high degree of
interdisciplinary coordination among various skilled
workers is required to manage the complex and time-
consuming nature of MEP installations (Chen et al.
2012), which commonly accounts for 40–60% of the
total cost of construction projects (Khanzode 2010).
Compared to the manufacturing industry, the con-
struction industry is characterised by a relatively lower
productivity (Aslam et al.2020). According to Lean
Institute (Construction Industry Institute 2005), Aziz
and Hafez (2013), value-adding activities in construc-
tion amount for only 10%, while in manufacturing
they account for 62% of all activities. MEP work makes
up a considerable part of construction in terms of the
cost, function, and value increases, therefore playing
an important role in project performance (Bandara
et al.2018). Furthermore, on-site construction and
assembly are characterised by budget and time con-
straints, as well as the complexity of managing mul-
tiple simultaneous processes and actors (Braglia
et al.2020).
Compared to manufacturing where the product
and process flow is visible and traceable from start to
finish due to a fixed location and clear working proce-
dures, construction faces different challenges
(Formoso et al.2002). As the project progresses, the
site layout changes frequently depending on the type
of material and equipment used. Furthermore, the
work processes and therefore the needed material and
equipment are strictly linked to the trades and sub-
contractors working on-site. This means that the direct
observation of the processes involved by the work-
force is often hindered by non-removable visual bar-
riers such as erected walls and slabs during
construction (Formoso et al.2002). This leads to: (1)
CONTACT Patrick Dallasega patrick.dallasega@unibz.it Faculty of Science and Technology, Free University of Bolzano, piazza Universit
a 5 39100
Bolzano, Italy
�2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-
nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed,
or built upon in any way.
CONSTRUCTION MANAGEMENT AND ECONOMICS
https://doi.org/10.1080/01446193.2022.2135748
non-transparent processes, e.g. the main process flow
is not visible and understandable due to the regular
modifications throughout the project which hinders
the information sharing and decision-making among
and of the project stakeholders (Brandalise et al.2018,
Lundberg et al.2021) (2) an impact on worker safety,
e.g. frequent modifications to the site layout and con-
struction itself impedes the effective communication
of safety risks using visuals (Abdelkhalek et al.2019),
and (3) reduced collaboration between the team
members and trades, e.g. the frequent change of the
site environment prevents an effective exchange of
information between the different trades (Valente
et al.2019).
One methodology that helps to minimise these
issues on-site is Visual Management (VM). VM in con-
struction encompasses a set of Lean management
techniques with the aim of passing on specific infor-
mation and instructions to the workforce using visual
tools (Tezel and Aziz 2017b). Four main areas for
implementing VM directly in the workplace can be
identified (Viana et al.2014, Brady et al.2018). First,
there are visual devices that display relevant informa-
tion (“information giving”) such as “One-Point-Lessons
(OPL)” consisting of one-page long operating instruc-
tions to standardise and optimise workflows. Second,
there are visual devices that attract attention via sig-
nals (“signalling”), like “Kanban,” which involves visual
control cards that indicate if materials or parts need to
be replenished. Third, there are visual devices that
control human responses (“limiting”), such as fences or
site layouts which provide a safe enclosure, while also
creating transparency by making the processes observ-
able. Fourth, there are visual devices that intentionally
warn (“guaranteeing”), such as “Poka-Yoke” or “Andon”
devices, by either preventing mistakes or defects from
arising or making any mistakes or defects immedi-
ately apparent.
Recent research has emphasised that emerging
information and communication technologies (ICTs)
can facilitate or even replace the existing VM sys-
tems on construction sites, but these are still in the
early stages of implementation (Dave et al.2013,
(Gurevich and Sacks 2014) , Tezel and Aziz 2017a).
Technologies like Building Information Modelling
(BIM), Mobile Computing, Laser Scanning, and
Augmented Reality (AR) can potentially be used to
improve process transparency (Tezel and Aziz 2017a,
Lundberg et al.2021). However, most AR applica-
tions developed to date have been conceived to
support the work of managers and inspectors rather
than on-site operational workers (Valente et al.
2019). The use of emerging technology to support
and enhance Visual Management (VM) practices on
construction sites would have great promise but cur-
rently it faces a shortage of personnel that are com-
petent in both Lean Construction and new
technologies. This is in addition to a lack of case
studies and best practices, as well as a lack of proof
of return on investment (Tezel and Aziz 2017b).
Thus, conventional VM tools in the construction
industry are frequently paper-based and without
proper digital support. In detail, VM in construction
is often hindered by several specific barriers (Schulze
and Dallasega 2020). These include the lack of a sys-
tematic approach for adopting VM on construction
sites (Tezel et al.2015, Brady et al.2018), a lack of
how to quantify VM benefits (Tezel et al.2017), a
lack of know-how (Abu et al.2019; Basu and Dan
2020), and insufficient training of the workforce
(Dora et al.2016, Lodgaard et al.2016, Cherrafi
et al.2017).
We believe that emerging technologies such as AR
in combination with BIM have the potential to support
current VM practices and overcome some of the limi-
tations by improving how information is visualised.
This is also underpinned by more recent work (Dave
et al.2013, Tezel and Aziz 2017a). AR allows the user
to work in a real-world environment while visually
projecting digital objects generated by BIM. On-site
VM practices in particular would benefit from BIM and
AR integrated visualised information becoming inter-
active and context aware (Wang et al.2013b, Tezel
and Aziz 2017a).
Studies have shown the potential of AR systems to
increase productivity in maintenance (Jetter et al.
2018, Loizeau et al.2019), logistics (Remondino 2020),
and training tasks (Hou et al.2017). In the literature,
AR is used to support assembly tasks by guiding work-
ers step-by-step through complex manual tasks (Wang
et al.2013b, Hou et al.2017) and to support supervis-
ory control and checking for installation or assembly
errors (Kuo et al.2013).
Considering construction, Williams et al. (2015)
pointed out that AR is particularly suitable for support-
ing users in their routine tasks like assembly, marking
and laying tasks. AR is not yet widely applied in con-
struction as there is no hard evidence of its benefits
(Delgado et al.2020a). However, the experiences from
other sectors suggest that benefits can also be
achieved in the construction sector (Delgado et al.
2020b). Recent studies indicate that emerging technol-
ogies like AR could even help to overcome the VM
implementation barriers (Tezel et al.2016a, Tezel and
2 P. DALLASEGA ET AL.
Aziz 2017a,2017b, Valente et al.2019). In this direc-
tion, Tezel and Aziz (2017b) pointed out the lack of a
measurement model to identify the benefits of AR for
construction projects. They suggest that AR could sup-
port the following conventional VM systems and tools
in construction: 5S, One-Point-Lessons, and Last
Planner meeting boards. However, this has only been
examined conceptually without empirical validation. In
this direction, Delgado et al. (2020) highlighted that
practical use cases of AR in construction should be
conducted to justify the investment required to adopt
AR in this sector.
Based on this, our research aims to empirically val-
idate if AR could help to overcome the traditional VM
implementation barriers in construction. Specifically,
the paper empirically validates the following research
questions (RQs). RQ1: Does the application of AR to
support VM practices result in measurable benefits
while performing MEP marking work in construction?
RQ2: Does AR reduce the training effort to support
marking work of MEP construction projects? RQ3:
Does the application of AR to support marking work
of MEP construction projects reduce the resistance to
adopt VM practices?
The article is structured as follows. Section two
presents the literature review and derives the RQs
to be answered. Section three proposes the AR
measurement model and section four describes the
research methodology used. Section five presents
the results of the case study application, which are
discussed in section six, including the implications
for research and practice, limitations, and future
research directions.
Literature review
The following chapter describes the application of VM
in construction (section “Visual management in con-
struction”), the general Key Performance Indicators
(KPIs), and the measurement indicators of AR which
are also used in contexts not directly related to con-
struction (section “ General KPIs of AR”) as well as the
use of AR in construction (section “AR applications in
construction”). In section “Derivation of research ques-
tions”, the RQs to be investigated in our research
are derived.
Visual management in construction
Visual Management (VM) is often mentioned in the lit-
erature as one of the main Lean methods (Erthal and
Marques 2018, Babalola et al.2019) aimed at increasing
process transparency (Tezel et al.2016a, Verbano et al.
2017), measurement and the improvement of organisa-
tions by using devices to project information and
improve communication in the work environment
(Brady et al.2018, Singh and Kumar 2021). This means
creating a visual workplace where the VM tools support
the management in the fields of performance, quality,
human resources, and safety (Tezel et al.2015).
VM is commonly applied in advanced manufactur-
ing (Liker and Morgan 2006, Tezel and Aziz 2015) and
less often in construction due to the frequent changes
in site layout and the simultaneous and overlapping
work of different trades and subcontractors on-site
(Bryde and Schulmeister 2012, Tezel et al.2015,2016a,
Tjell and Bosch-Sijtsema 2015, Brady et al.2018, Singh
and Kumar 2021).
VM tools can be categorised into visual indicators
(a simple display of information, such as safety advis-
ory boards), visual signals (attention-seeking devices,
such as sirens), visual control (tool to impact behav-
iour, such as speed bumps), and visual guarantees
(fool-proof devices to allow only the right thing to
happen) (Galsworth 1997). Typical VM tools and tech-
niques applied to construction include:
i. Heijunka boards to level the demand and control
variability in material and equipment usage (Tezel
and Aziz 2017b, Singh and Kumar 2021).
ii. Visual performance boards and Obeya (big
rooms) where the KPIs are located and dis-
played, and meetings and continuous improve-
ments are facilitated (Tezel et al.2017, Singh
and Kumar 2021).
iii. Andon systems as signalling boards to draw the
supervisors’ attention to sustain Continuous
Improvement Process (CIP) efforts (Tezel et al.
2017, Singh and Kumar 2021).
iv. Project production control systems such as
Location-Based Management Systems (LBMS),
conventional Gantt charts or Last Planner
System (LPS) plans with construction site tasks
(Tezel et al.2017, Singh and Kumar 2021).
v. Last Planner System (LPS) as an important Lean
Construction method for collaborative construc-
tion planning and control (Ballard 2000).
vi. A3 method for visualising the CIP process or
Plan-Do-Check-Act (PDCA) cycle on an A3 sheets
(Tezel and Aziz 2017a).
vii. One-Point Lessons (OPL) to visually train people
on changed procedures, revised standards, etc.
using one-page sheets as effective on-the-job
training tools (Tezel and Aziz 2017a).
CONSTRUCTION MANAGEMENT AND ECONOMICS 3
viii. 5s as a system performance tool to create a dis-
ciplined, clean and orderly work environment
(Singh and Kumar 2021).
ix. Poka-yoke, a quality assurance technique origin-
ally developed by Toyota, to eliminate defects in
a product by preventing mistakes as early as
possible (Tezel and Aziz 2015).
VM faces numerous implementation barriers in con-
struction. It is often applied in isolation and unsystem-
atically to individual processes, resulting in different
levels of VM practices and standards in different sites
run by the same company (Tezel et al.2016a, Tezel
and Aziz 2017a, Brady et al.2018). Furthermore, the
fragmentation of the construction industry, its project-
based character, and the duplication of information
also hampers its implementation (Tezel and Aziz
2017a). Specifically, paper-based documents that
include 2D construction drawings, construction diaries,
and schedules generally account for most of the tech-
nical information needed in day-to-day practice. This
often leads to different levels of knowledge and mis-
understandings between stakeholders, as well as an
incomplete vision of the actual situation which hinders
informed decision-making (Ratajczak et al.2019).
Adopting on-site VM practices during the applica-
tion phase is also hindered by construction workers
who fear making mistakes and who resist changing
from the traditional ways of working (Tezel et al.
2015). This can be attributed to the often very low
level of education and the high turnover of operators
in general (Tezel et al.2015). To increase the success-
ful implementation of VM, the literature emphasises
the importance of adequate operator training, espe-
cially for complex VM practices that are not widely
used on construction sites (Tezel et al.2015, Brady
et al.2018, Brandalise et al.2018). However, the site
managers responsible for Lean and VM initiatives often
report a lack of time and financial resources to appro-
priately train employees (Tezel et al.2017).
Another main barrier when implementing VM is the
missing knowledge on how to objectively measure
and quantify the effectiveness of the different visual
tools and systems (Marodin and Saurin 2014, Tezel
and Aziz 2015,2017a, Tezel et al.2016b, Brady et al.
2018, Johari and Jha 2021).
General KPIs of AR
Several Key Performance Indicators (KPIs) for measuring
the benefits of AR, specifically developed for the manu-
facturing industry, have been identified in the literature
(Jetter et al.2018, Chuah 2019, Frutos-Pascual et al.
2019, Loizeau et al.2019, Ratajczak et al.2019,
Remondino 2020). The application fields include auto-
motive maintenance (Jetter et al.2018), aeronautical
maintenance (Loizeau et al.2019), logistics (Remondino
2020, Wang et al.2020), control cabinet production
(Khokhlovsky et al.2019), and construction (Ratajczak
et al.2019). Table 1 summarises the identified KPIs and
their definitions. The most commonly cited quantitative
KPIs are time, such as a reduction in the time it takes
to complete a certain task, errors, such as a decrease in
the number of errors committed during a specific task
(Loizeau et al.2019, Remondino 2020, Jasche et al.
2021), and costs, such as a reduction in training costs
or the costs for personnel and equipment used for a
certain task (Loizeau et al.2019, Ratajczak et al.2019,
Remondino 2020). Furthermore, qualitative KPIs include
cognitive load (Jetter et al.2018, Loizeau et al.2019,
Jasche et al.2021), spatial representation (Jetter et al.
2018), perceived ease of use, perceived usefulness, atti-
tude towards using, and the behavioural intention to
use AR (Jetter et al.2018, Loizeau et al.2019).
Jetter et al. (2018) as well as Loizeau et al. (2019)
stated that KPIs could be substantially improved with
the use of AR compared to traditional information sys-
tems such as paper-based manuals. AR helps to speed
up the training process of assembly routines and
increase the working memory capacity by supporting
information processing and cognition (Jetter et al.
2018, Loizeau et al.2019). In both studies, older partic-
ipants with more work experience reported there to
be benefits in terms of greater decreases in the per-
ceived “Cognitive Workload,” confirming the seemingly
intuitive use of AR technology. Loizeau et al. (2019)
found that AR shortens the understanding phase of a
task (time to understand the maintenance instructions)
and increases the action phase (time to complete the
task), allowing users to more effortlessly focus on the
execution phase. Similar results were obtained by
Remondino (2020) and Wang et al. (2020) regarding
the application of AR technology in the logistics indus-
try to support staff training, storage operations, ware-
house management, and transport optimisation.
According to their studies, AR is especially suitable for
the staff training as logistics companies often employ
temporary resources. AR usage reduces the errors
made by the unqualified workforce and reduces the
training requirement for employing temporary work-
ers. In logistics in particular, AR digitises work proc-
esses such as warehouse operations to switch from
“pick-by-paper” to “pick-by-vision.” In this direction,
Khokhlovsky et al. (2019) found that the use of AR
4 P. DALLASEGA ET AL.
Table 1. Identified KPIs in the literature to measure the benefits of AR systems.
KPI Definition Source
Cognitive workload “The difference between cognitive demands of a
particular job or task and the operator’s attention
resources”; it includes thinking, deciding, calculating,
remembering, looking, searching
Jetter et al. (2018), Loizeau et al.
(2019), Jasche et al. (2021)
Spatial Representa-tion of
Contextual Information
“Skill in representing, transforming, generating, and
recalling symbolic, non-linguistic information”
Jetter et al. (2018)
Perceived Ease of Use “The degree to which an individual believes that using a
particular system would be free of physical and
mental effort”
Jetter et al. (2018), Loizeau
et al. (2019)
Perceived Usefulness “The degree to which an individual believes that using a
particular system would enhance his or her job
performance”
Jetter et al. (2018)
Attitude towards Using Positive or negative attitude towards AR use Jetter et al. (2018), Loizeau
et al. (2019)
Behavioural Intention to Use Likelihood to keep using AR technology once available Jetter et al. (2018)
Under-standing /
Action ratio to Total time
The quantity “U/A ratio” is defined to evaluate the
repartition of time needed to research and understand
the instructions (T
U
) and time needed to execute
actions (T
A
) regarding the total time (T
T
) needed to
complete the task or subtask.
Formula: R
U/A
¼T
A
/T
T
[T
T
¼T
A
þT
U
] [sec/min]
Loizeau et al. (2019)
Total Time Gain calculation The “time gain” (T
G
) quantity evaluates the added value
of AR on the performance of the subtasks. T
AR
is the
time recorded with AR and T
CS
the time recorded
without AR.
Loizeau et al. (2019)
Performance Ability ratio It is the ratio of the defined content of 1 pitch to the
actual measured progress on site. Value >1 indicates
a lack of performance with respect to the expected
performance. Value ¼1 means that the foreseen goal
has been met. Value <1 refers to a more powerful
performance than expected.
Ratajczak et al. (2019)
Quality Gate QG is the number of fulfilled quality checklists out of the
total number of checks assigned to a task
Ratajczak et al. (2019)
Reduction of Time Time of task completion (performance time) Loizeau et al. (2019), Khokhlovsky et al.
(2019), Remondino (2020), Jasche
et al. (2021)
Reduction of Errors Superimposed instructions given to recover from errors.
Number of errors detected during inspections
Loizeau et al. (2019), Ratajczak et al.
(2019), Remondino (2020), Jasche
et al. (2021)
Reduction of costs Lower costs for training and less costs due to human
errors. Lower costs via optimal routeing and stocking
of transport vehicles.
Remondino (2020)
Costs for equipment, staff, third-party specialists, research,
and computer simulation.
Khokhlovsky et al. (2019)
Effort required in days per man-hour cost rate. Ratajczak et al. (2019)
Accuracy Accuracy of the superimposed instructions (for training,
picking, and delivery instructions).
Remondino (2020)
Efficiency Optimized picking (picking-by-vision) route within the
warehouse. Optimal delivery routeing.
Khokhlovsky et al. (2019),
Remondino (2020)
Precision The level of control the user has when interacting with
the virtual objects measured in:
(i) Time to completion:
Time to complete the task
Frutos-Pascual et al. (2019)
(ii) Accuracy: (a) Euclidean distance between the target
and visualised object; (b) rotation-wise as the total
rotation of all angles between target and
visualised object;
Frutos-Pascual et al. (2019)
Interactivity The user’s ability to interact with the virtual objects as
the perceived usability of the AR system is measured
via
(i) NASA TLX, a multi-dimensional scale designed to
obtain cognitive workload estimates from the
test subjects
Frutos-Pascual et al. (2019)
(ii) System Usability Scale (SUS) to evaluate the perceived
usability of a human-machine system
(iii) Post-test questionnaire to measure ease of use, field of
view (FOV), and hand tracking accuracy
CONSTRUCTION MANAGEMENT AND ECONOMICS 5
reduces the number of decisions to be made by the
assembly workers while decreasing the qualification
needs and the risk of errors in the assembly processes.
Further measurement indicators were identified by
Frutos-Pascual et al. (2019) who compared different
freehand AR HMDs in a task-based interaction.
Usability, interactivity, precision, ease of use, the field
of view, and hand tracking accuracy were measured.
According to the authors, more powerful AR tracking
and visualisation devices will lead to improved usabil-
ity, comfort, and accessibility in the future.
Chuah (2019) highlighted that KPIs for Extended
Reality (XR) are not yet properly defined as XR is a
relatively new technology. XR is an umbrella term that
refers to all real and virtual combined environments
generated by computer technology and wearables
that encompass Augmented Reality (AR) and Virtual
Reality (VR). VR refers to technology that places the
user in a complete digital environment while blocking
his view of the real world. AR refers to technology
that enables the user to see the real and digital worlds
simultaneously. According to Chuah (2019), companies
applying XR technology need to formulate KPIs based
on their specific situation. Table 1 summarises the KPIs
identified in the literature used to measure the bene-
fits of AR.
AR applications in construction
Wang et al. (2013b) demonstrated the application of
an AR HMD which allowed real-time information to be
gathered without the need to obtain information from
drawings and photographs, while allowing hands-free
working. Cognitive tasks related to construction work
on site (e.g. reading through drawings and instructions
between work steps) took significantly less time as a
result. In this direction, Hou et al. (2017) proposed a
training framework for VR and AR systems to teach
complex procedures and to increase the skill levels
found in the oil and gas industries. It has been
reported that it takes less time and there are fewer
errors when completing the tasks as well as less learn-
ing time compared to the traditional way of using text
and photographs. The authors suggest using head-
mounted VR or AR systems to further improve these
benefits. Chalhoub and Ayer (2018) compared the per-
formance of two groups of participants for the assem-
bly of prefabricated electrical conduits. The design
information was provided either through the means of
an AR HMD (Microsoft HoloLens) or using traditional
drawings. The main results were faster assembly oper-
ations, less time needed to understand the given
tasks, and fewer errors. Interestingly, participants with
no prior conduit assembly experience accomplished
the best times. Similarly, Kwiatek et al. (2019) analysed
the impact of a hand-held AR application for assem-
bling pipe spools in construction compared to a con-
ventional approach and its impact on human spatial
cognitive abilities. As a result, the AR application
helped significantly to reduce the time it took to com-
plete the pipe assembly tasks. Participants with low
spatial skills benefitted twice as much from the AR
application as their counterparts with high spatial
skills. Therefore, AR does seem to be very suitable for
training purposes such as when onboarding appren-
tice pipefitters. Ratajczak et al. (2019) presented a BIM-
based AR application that displays context-specific
information about the construction project and tasks,
as well as KPIs on the construction progress and per-
formance. The system showed there to be benefits
like increasing the process transparency. However, it
was only tested experimentally.
In the field of facility management, El Ammari and
Hammad (2019) developed a BIM-based Augmented
Reality approach to facilitate remote collaboration and
visual communication between the office and on-site
operators. The field workers used an AR application on
their tablet while in the office, the managers used an
Immersive Augmented Virtuality (IAV) application on
their desktop computer, giving them the same view as
the fieldworkers. The identified benefits included time
saving in terms of the data collection, model naviga-
tion, approvals, as well as the reduction of on-site
errors. Similarly, Chen et al. (2020) created a fire safety
equipment (FSE) inspection and maintenance system
using AR to provide the inspectors and engineers with
the necessary information when performing mainten-
ance and repair work. Significantly higher levels of
performance and fewer errors have been reported
compared to the traditional paper-based method.
Again, the researchers suggest applying an AR headset
with gestures and gaze recognition technology to fur-
ther improve performance.
In summary, AR has been applied primarily to sup-
port training, assembly, and maintenance tasks in the
gas, oil, and construction industries. However, it has
emerged that the knowledge in the field of evaluating
the benefits of AR is still scarce and dispersed. In the
literature, mainly quantitative measurement indicators
such as the reduction of execution times and errors
are considered. This is also underpinned by the work
of Wang et al. (2013a) who emphasised the import-
ance of evaluating the performance of AR systems to
achieve further improvements. According to them,
6 P. DALLASEGA ET AL.
different models like the “Balanced Score Card (BSC), IS
Success Model, Control Objectives for Information and
Related Technology (COBIT), Information Economics
have been applied to suggest measures that can evalu-
ate the performance of information systems by consider-
ing diverse perspectives.” Due to the unique
characteristics of AR systems such as “portability,
mobility, nomadicity, information load, congruence, and
cognitive load,” the existing approaches used to evalu-
ate information systems are not appropriate (Chen
et al.2020). Similarly, Chuah (2019) points out that
established models and technology acceptance theo-
ries such as the Technology Acceptance Model (TAM),
the Unified Theory of Acceptance and Use of
Technology (UTAUT) model, or the Technology-
Organization-Environment (TOE) framework are insuffi-
cient to explain the user reactions to AR technologies
and to measure their benefits. According to Liu et al.
(2018), the TAM model would benefit from including
more measurable indicators for each of the parame-
ters such as perceived usefulness, perceived ease of
use, and external variables, thereby supporting quanti-
tative studies with statistically reliable results.
Derivation of research questions
Tezel and Aziz (2017a) pointed out that there is still
limited knowledge among construction managers on
how to quantify the benefits of VM. Moreover, a sys-
tematic approach to support the successful adoption
of VM in construction is lacking (Tezel and Aziz
2017a). The literature mainly focuses on the indicators
suitable for measuring the benefits of AR in the auto-
motive (Jetter et al.2018), aeronautics (Loizeau et al.
2019), and logistics (Remondino 2020) industries. The
main focus is on the time (Loizeau et al.2019,
Remondino 2020), error (Loizeau et al.2019, Ratajczak
et al.2019, Remondino 2020), and cost aspects
(Loizeau et al.2019, Ratajczak et al.2019, Remondino
2020). In terms of construction, recent works indicate
that AR could support VM leading to an increase in
performance (Tezel and Aziz 2017b, Tezel et al.2020).
However, these works lack adequate empirical valid-
ation and confirmation. This leads to the first RQ to
be answered:
RQ1: Does the application of AR to support VM
practices result in measurable benefits while
performing MEP marking work in construction projects?
As analysed by Tezel et al. (2015,2017b), complex
VM practices require adequate operator training to
achieve the associated benefits. Sustaining advanced
VM tools like the 5s requires a lot of scrutiny and
commitment from the workforce. Despite emphasising
that operator training is essential to VM implementa-
tion, construction managers often mention that they
do not have enough time to adequately train them
(Tezel et al.2015, Tezel and Aziz 2017b). The recent lit-
erature shows the applicability of AR as an effective
training tool for operators in logistics (Remondino
2020), electric cabinet production (Khokhlovsky et al.
2019), maintenance work in automotive assembly
(Jetter et al.2018), aeronautical maintenance (Loizeau
et al.2019), learning complex procedural skills in the
construction, engineering, and medical fields (Hou
et al.2017), and the inspection and maintenance of
fire safety equipment (Chen et al.2020). However, the
work is mainly focussed on the manufacturing envir-
onment and less knowledge is available for the con-
struction industry.
One of the main differences between construction
and manufacturing is its high level of fragmentation
as trades and suppliers are frequently exchanged
between different projects. Due to the high turnover
rate of construction operators, a high training effort is
needed (Hee and Ling 2011). Additionally, construction
often does not allocate a specific budget (and time)
for proper operator training. Therefore, knowledge
about safety, health and standard practices is rarely
renewed (Wolf et al.2019) and not very effective
(Edirisinghe and Lingard 2016). Consequently, con-
struction trades such as scaffolders, roofers, steel
workers, plumbers, etc. have not yet benefitted from
training supported by emerging technologies (Carozza
et al.2013). Additionally, existing training methods
such as self-learning, hands-on-learning and super-
vised-learning for construction operators are ineffect-
ive and outdated (Moore and Gheisari 2019).
Therefore, AR-based training offers the opportunity to
provide safe and more effective instructions to con-
struction operators at a reduced time and cost (Moore
and Gheisari 2019). The second RQ is derived
accordingly:
RQ2: Does AR reduce the training effort to support
marking work of MEP construction projects?
For the successful implementation of VM in con-
struction, the authors also mention the importance of
overcoming conventional working habits (Tezel et al.
2015, Tezel and Aziz 2017b). The workers’ adherence
to traditional working methods is further impeding
the broader adoption of VM in construction (Tezel
et al.2015, Mano et al.2020, Sahu et al.2021).
Resistance to change to new organisational processes
and methods is one of the main barriers to success-
fully putting VM into practice (Tezel et al.2015, Tezel
CONSTRUCTION MANAGEMENT AND ECONOMICS 7
and Aziz 2017a, Sahu et al.2021). This includes the
scepticism of employees towards the management’s
commitment (Dora et al.2016), as well as the missing
support of the middle management in the implemen-
tation processes of VM (Marodin and Saurin 2014). In
addition, the adoption of VM is impaired by the
employee’s fear of the unknown, fear of failure (Tezel
and Aziz 2017b) and complacency, fear of making mis-
takes, as well as the fear of job loss due to Lean
rationalisation (Salonitis and Tsinopoulos 2016,
Cherrafi et al.2017, Abu et al.2019). Based on Tezel
et al. (2015), this can be attributed to the often very
low level of education and the high turnover of opera-
tors in general. On the other hand, according to Jetter
et al. (2018) and Remondino (2020), operators intui-
tively understand the support provided by AR systems.
As described in detail in section 2.3, various drivers
such as perceived ease of use,perceived usefulness, and
cognitive workload influence the acceptance of AR by
construction operators. Users perceive the AR system
to be user-friendly because of its simple, fast, precise,
and reliable application (Jetter et al.2018, Loizeau
et al.2019, Remondino 2020). Thus, the third RQ aims
to practically validate the previous statements.
RQ3: Does the application of AR to support marking
work of MEP construction projects reduce the
resistance to adopt VM practices?
AR measurement model
The literature review identified several KPIs that meas-
ure the performance of AR. They served as a starting
point to develop our measurement model.
First, we selected several KPIs from the literature to
test and answer the research questions derived in sec-
tion “Derivation of research questions”. In addition,
certain KPIs suitable to validate the research questions
not been found in the literature were added by the
research group. The following criteria were used when
selecting the suitable KPIs. (1) Relevance: a KPI must
help the user to validate the research questions. (2)
Reliability: the KPI should be measurable as accurately
as possible. (3) Comparability: the KPIs should enable
the users to identify the similarities and differences
present between the two sets of results. (4)
Understandability: the KPIs should be easily interpret-
able and understandable by a broad range of users.
We selected the KPI Work Safety because potential
work safety risks were observed during the initial on-
site testing of the AR HMD (i.e. tripping hazards from
cables or equipment on-site). The indicator Training
was added because we wanted to measure the effort
required to learn how to use new equipment. From a
research perspective, we wanted to have some
insights into the impact of training on the participants’
performance (Johari and Jha 2021). Furthermore, the
case company complained about the weight of the AR
HMD during the first tests which were still in the office
and not on the construction site. We therefore
decided to introduce an indicator for measur-
ing Ergonomics.
To validate RQ1, the KPIs in terms of Cycle Time
(CT) and Accuracy (ACC), defined as the deviation from
the design, were selected. The KPI CT refers to the
total time from the beginning to the end of the con-
sidered construction operation (e.g. marking opera-
tions on-site). It meets the time that is needed for
value-adding and supporting operations. ACC refers to
the total deviations from the design made during a
given task (e.g. marking operations). It considers abso-
lute deviations from the design in the X and Y axes
(ACC
XY
) as well as in the Z axis (ACC
Z
). The indicator
Perceived Usefulness was selected because it is inter-
preted as a performance-based metric used to evalu-
ate whether AR improves job performance (Davies
1989). For example, an intelligently designed user
interface of a system that only contains the necessary
functions will not disrupt the user experience and
thus support the initial user acceptance and sustained
use of the system (Venkatesh 2000). These KPIs were
selected because they allow for a performance meas-
urement and enable a direct comparison between the
use of AR and the use of conventional methods such
as paper-based manuals and drawings.
To answer RQ2, the following KPIs were considered.
The Training of the AR technology considers factors
such as posture, gestures, and the interactions of the
applicant with the AR HMD. This includes the adjust-
ability and operation controls within the system’s user
interface. Since complex VM procedures require a high
training effort (Tezel and Aziz 2015,2017a), we
decided to select a specific KPI that measures the
training endeavour. Specifically, we considered the R
U/
A
ratio which allows for a comparison between the
tasks carried out with AR support and those carried
out using traditional methods such as drawings, pho-
tographs, hand-written notes, as well as a pen and
tape measure (Loizeau et al.2019).
To answer RQ3, we applied the metrics from Davis
(1985,1989) Technology Acceptance Model (TAM). We
used this TAM model because it is a widely tested
model used to explain the acceptance of information
technology and systems in industry (Praveena and
Thomas, 2014, Liu et al.2018). The indicator Perceived
8 P. DALLASEGA ET AL.
Ease of Use was chosen due to the AR HMD’s intuitive
use through simple gestures and its user friendly inter-
face (Chuah 2019) which aims to reduce the effort
needed when implementing the technology in prac-
tice (Davies 1989, Liu et al.2018). We selected this
indicator because if the applicant believes that the
system is easy to use and requires little effort, the
resulting acceptance level will be increased (Davis
1985). The KPIs, Perceived Ease of Use, a process
expectancy, and Perceived Usefulness, an outcome
expectancy, are the two decisive factors influencing
user intention and usage behaviour, thus supporting
the user acceptance of a technology (Venkatesh 2000).
The KPI Cognitive Workload was used to answer
RQ3 since a user-oriented interface and the intuitive
gestures of the AR HMD are likely to decrease the
user’s mental effort and thus lead to a higher accept-
ance (Voskamp and Urban 2009, Knaepen et al.2015).
Typical cognitive demands like thinking, deciding, cal-
culating, looking, searching, and remembering are pre-
sumably relieved with the AR HMD during the
construction tasks.
Ergonomics describes the influence of the AR HMD
on a user’s gestures and form when performing a par-
ticular task. It considers whether the user’s wellbeing
is affected by the AR HMD or how the gestures affect
the user’s posture and the overall performance of the
system (Aromaa et al.2018). For example, the form
factor of the AR HMD hardware, such its burdensome
weight and low user field of view, causes the users to
become potentially tired or to exert themselves in
unsafe body positions during the task execution.
Therefore, this KPI was selected to answer RQ3.
The indicator Work Safety was used to answer RQ3
since occupational safety plays an important role in
the acceptance of the AR HMD. This is because it
could have a positive or negative influence on work
ability, job performance, and the health of the workers
using the AR HMD. On the one hand, work safety
could be impaired by the narrowed field of view of
the AR HMD (Kim et al.2016, Vorraber et al.2020).
However, AR could help reduce the risk factors and
error rates by superimposing safety warnings onto the
AR HMD (Li et al.2018).
The indicator Attitude Towards Using a system or
technology was used to answer RQ3 because posi-
tively valued outcomes when using the AR HMD
could increase the user’s feelings towards the tech-
nology (Jackson et al.1997) and thus raise the user’s
acceptance of a system and their Behavioural
Intention to Use it (Venkatesh 2000, Praveena and
Thomas, 2014).
The indicator Behavioural Intention to Use was
chosen as a metric because, as stated in the TAM
model, it is able to measure the actual use of the AR
technology in practice and thus helped to determine
its acceptance level (Davis 1985, Davies 1989).
According to Luarn and Lin (2005), Behavioural
Intention to Use is affected by the user’s attitude
regarding the perceived positive or negative perform-
ance effects of the AR technology, as well as the sub-
jective norms regarding the perceived opinion of
others in terms of whether the individual should apply
the AR technology. Behavioural Intention to Use is sig-
nificantly influenced by Perceived Usefulness and
Perceived Ease of Use (Wong 2013).
Research methodology
Analysis and model development
First, a literature review was carried out to determine
the specific KPIs used to measure the benefits of AR
applications in construction and other industries
(Table 1). Moreover, an overview of VM in construction
as well as the specific implementation barriers has
been given. Based on the analysis of the literature
review, we derived the research questions RQ1, RQ2
and RQ3. The AR measurement model (Table 2) was
defined by selecting suitable KPIs from the literature
review as well as defining new metrics with the final
aim of empirically answering the RQs.
Case study selection
The case study methodology, as proposed by Yin
(2009,2011), was adopted because it is suited to
empirically observing and researching occurrences or
contemporary social phenomena in their practical and
cultural context (Eisenhardt, 1989, Yin, 1992, 2011).
The case study methodology allows for the gaining of
a novel understanding that would be otherwise diffi-
cult to obtain through purely analytical or statistical
analyses (Yin 2011, Robson and McCartan 2016). We
selected this research methodology because it allowed
us to explain, through the collection of real-world
data, how the application of AR in construction can
help to overcome VM implementation barriers.
We used the purposeful sampling technique
(Palinkas et al.2015) to select the project case and
participants as this non-random technique allows for
the targeting of participants based on the necessary
information that they can provide on the ideas and
topics in question (Tongco 2007, Campbell et al.
2020). Purposeful sampling was applied to better
CONSTRUCTION MANAGEMENT AND ECONOMICS 9
match the objectives of the research to the sample,
thus increasing the rigidity of the study and the reli-
ability of the data and results (Campbell et al.2020).
The case study company was medium-sized and
specialised in supplying MEP systems for residential
construction projects. Since there were several proj-
ects to choose from (e.g. industrial building, hotel
building and others), we used purposeful sampling
to select a multi-story apartment building located in
northern Italy with five apartments on three levels.
Two apartments were on the ground floor, two
apartments on the first floor, and one apartment on
the top floor. The apartment building was chosen
for the following reasons: (i) It was of a manageable
size, so as not to be too complex nor too large for
the practical tests. (ii) The apartments on the
ground and the first floor are very similar and there-
fore suitable for comparing the work processes with
and without AR support. (iii) The case study com-
pany was responsible for the construction and MEP
work consisting of heating, ventilation, sanitary, and
electric installations.
Before the construction began, the suitable work
processes were selected to be supported with AR. In
agreement with the case study company, it was
decided to use AR to support the marking work for
the installation of electrical, heating, ventilation, and
sanitation pipes in the apartments (Figure 1). The
marking work was chosen to be supported by AR as
corrections could easily be made in the case of errors.
Usually, this marking work is done using drawings, a
tape measure and spray cans to mark where the
tubes, pipes, and cables need to be routed and
installed. The focus was on comparing the marking
activities with and without AR support. A Trimble
XR10 safety helmet containing the Microsoft HoloLens
II was selected as the AR HMD device. The BIM soft-
ware Graphisoft Archicad 24 and the AR application
BIM Holoview were used to develop and visualise the
design information (Figure 2). The implementation
period was from June to September 2020.
The following criteria were used to select the case
study company participants: (i) in-depth experience of
carrying out the selected tasks on-site, (ii) involvement
in the project from start to finish, and (iii) a strong
affinity with technology. The BIM coordinator
(Interviewee 1) and four foremen (Interviewee 2 until
Interviewee 5) participated in the case study. Two fore-
men (Interviewee 3 and 4) were responsible for the
heating ventilation and sanitary (HVS) work, another for
the electrical installation (Interviewee 5), and one for
the underfloor heating (UFH) installation (Interviewee
2). The BIM coordinator (Interviewee 1) was a trained
mechanical engineering technician with 11 years of
professional experience. Foremen 2–5 were all certified
installers with 10 years, 8 years, 20 years, and 12 years
of working experience, respectively.
Special attention was also paid to ensure that the
foremen responsible for the HVS, UFH and electrical
work were not exchanged between the marking work
on the ground floor (with the AR HMD) and the mark-
ing work on the first floor (conventionally).
Table 2. AR measurement model.
RQs KPI Description
RQ1 Cycle Time (CT) Timespan from the beginning to the end of the operation (value-added and
supporting tasks).
Formula: CT ¼End of operation – Start of operation [sec/min]
RQ1 Accuracy
X–Y-axis (ACC
XY
)
Deviation of the projected object with the design (e.g. BIM model) considering the
modulus of the Euclidean distance in x and y-direction.
Formula: ACC
XY
¼│ACCX│þ│ACCY│[cm]
Accuracy
Z-axis (ACC
Z
)
Deviation from the projected object with the design (e.g. BIM model) considering the
modulus of the Euclidean distance in the z-direction (height of the object).
Formula: ACCZ ¼│ACCZ│[cm]
RQ1 Perceived usefulness The extent that users believe that applying a certain technology will help them
perform their job better.
RQ2 Training The extent of the instructions required to operate the AR HMD properly for the
given tasks.
RQ2 Understanding/
action ratio to total time
The “U/A ratio” sets the time to understand the work instructions (T
U
) in relation to
the time of execution of the activity (T
A
), taking into account the total time (T
T
)
necessary to complete the given task. (For the specific formula, see Table 1).
RQ3 Perceived ease of use The extent that the applicants believe that using a certain technology will be free of
effort (mentally and/or physically).
RQ3 Cognitive workload The extent of the mental effort (or attention) allocated to performing a particular task.
RQ3 Ergonomics The influence of the AR HMD on the gesture and form of the applicants when
performing a particular task.
RQ3 Work safety The positive or negative impact of AR technology on safety and health at work during
the performing of a particular task.
RQ3 Attitude towards using Positive or negative engagement when using the technology.
RQ3 Behavioural intention to use Probability to keep using the technology in the daily work of the future.
10 P. DALLASEGA ET AL.
The workflow started with the laser scanning of the
shell construction of the apartment building. Using
the collected data, the architectural BIM model was
updated to a BIM model in the “as-built” state of the
existing shell. The created as-built BIM model was
then loaded into the AR HMD and registered. The
required registering was done using two physical QR-
markers per construction location. The positioning of
the markers was done on straight and not two differ-
ent walls to avoid potential deviations coming from
the construction execution.
The wall openings for heating, ventilation, and elec-
trical lines were then marked. The user performed the
marking by looking at the hologram at an angle of
90in order not to lose accuracy during the marking
work. For comparison, the markings for the trenches
on the first floor were created conventionally and on
the ground floor with the AR HMD. Another laser scan
of the installed heating and ventilation ducts and elec-
trical pipes followed. Consequently, the existing as-
built BIM model of the shell construction was updated
to match the as-built BIM model with the completed
MEP work. Again, the as-built BIM model with the fin-
ished MEP work was loaded into the AR HMD. Next
came the floor markings for the underfloor heating
(UFH). The AR HMD displayed the respective construc-
tion sections in which the underfloor heating had to
be installed. For comparison, the markings for the UFH
Figure 1. Marking work supported by the AR HMD of the UFH (left) and an extract of the AR Hologram (right).
Figure 2. Used BIM software and AR application (left) as well as AR HMD (right).
CONSTRUCTION MANAGEMENT AND ECONOMICS 11
were carried out conventionally on the first floor and
with the AR HMD on the ground floor by the same
foreman. It should be noted that the employees con-
sidered the assembly of the laid mesh rather than the
effective geometry of the projected hologram, result-
ing in deviations between the design and execution.
Data collection and analysis
Two types of data were collected within the case
study: (i) performance data by direct observation and
the measurement of KPIs (such as cycle time CT) while
the participants performed their assigned tasks, and
(ii) perceptual data in the form of semi-structured
interviews following DiCicco-Bloom and Crabtree
(2006) recommendations.
Specifically, we used the work sampling (WS)
method to quantify the labour time utilisation through
direct observations of construction labour work (Gong
et al.2011). We selected WS because the case study
project lasted about 6 months, giving us sufficient
time to apply the method. Moreover, the tasks meas-
ured on-site had a relatively long cycle time (in the
range of several minutes). In addition, the analysed
tasks were not highly repetitive, meaning that they
also consist of several subtasks which can be stated as
another characteristic for the suitability of the WS
method (Neve et al.2020). Considering accuracy, only
the ACC
X
and ACC
Y
indicators were measured because
the BIM model showed some errors and inconsisten-
cies considering the height of the objects. This is
because the company focuses on having high accur-
acy levels in the xand yaxis and intends to leave
some freedom for the foremen when installing objects
on the zaxis (the height of objects). The cycle time
(CT) was measured through video recordings of the
chosen tasks to ensure the traceability of the
recorded values.
The interviews were conducted face-to-face in a
semi-structured form and took between 45 and
60 min. After the interviews were completed, the
records were transcribed. Verbatim comments have
been included in this paper to enrich the description
with the real-life experiences of the participants.
Compared to structured interviews which use pre-
determined questions with fixed wording to ask each
respondent the same thing, semi-structured interviews
allow for more flexibility in terms of structure, ques-
tion design, order and wording (Brinkmann 2013).
They also allow the interviewer to ask for more details,
as well as clarification and follow-up questions (Rubin
and Rubin 2011). Semi-structured interviews rely on an
interview guide which includes open-ended questions,
cues, and notes for the interviewer to explore the
topics and research questions prepared by the
researcher (Zainal 2007, Brinkmann 2013). We applied
the semi-structured interview method because it is dif-
ficult to formulate the appropriate question-answers
precisely in advance and it leaves more space to add
new questions during the dialogue based on the flow
and information given in the interviews (Robson
and McCartan 2016). Table 3 shows the interview
protocol that was used as the basis for the semi-
structured interviews.
The validation of the case findings and conclusions
were supported by follow-up presentations with
company representatives in face-to-face meetings and
discussions with three peer academics.
Case study application
In the following section, the application of the AR
measurement model presented in section 3 (Table 2)
to answer the RQs in the case study is presented.
RQ1: cycle time (CT)
Figure 3 shows the relative time expenditures when
marking the heating, ventilation, sanitary (HVS), under-
floor heating (UFH) and electrical work using the AR
HMD compared to conventional means.
The marking work for the trenches and slots in the
eat-in-kitchens took around 37 s with the AR HMD and
30 s with the conventional marking method. On the
other hand, the marking of the trenches for the bath-
rooms in apartment 1 (A1) and apartment 2 (A2) with
the AR HMD took 46 and 53 s, respectively, compared
to the conventional approach which took 30 and 35 s
in A3 and A4, respectively. The marking work using
the AR HMD support for the UFH in the eat-in-kitchens
A1 and A2 took 48 and 25 s, respectively. The conven-
tional marking work for the UFH in the eat-in-kitchens
A3 and A4 took 57 and 67 s, respectively. The marking
work with the AR HMD support for the UFH in bath-
rooms A1 and A2 took 9 and 10 s, respectively. The
Table 3. Interview protocol.
Purpose Semi-structured questionnaire questions
RQ1 1. How did you perceive the usefulness of the AR HMD?
RQ2 2. Was a high training effort necessary?
RQ2 3. How did you perceive the ease of use of the AR HMD?
RQ2 4. Was a high cognitive workload required to use the AR HMD?
RQ3 5. Had the usage of the AR HMD had an impact on ergonomics?
RQ3 6. Was work safety impacted by using the technology?
RQ3 7. How is your attitude when using the technology in practice?
RQ3 8. How is your behavioural intention to use the AR HMD?
12 P. DALLASEGA ET AL.
conventional marking work for the UFH in the bath-
rooms A3 and A4 took 36 and 46 s, respectively.
The marking work for trenches in the toilet A2 took
83 s with the AR. In A4, it took 175 s using the conven-
tional marking approach. Comparing the results
between the conventional marking work on the first
floor and the AR HMD on the ground floor gave mixed
results. Marking slots with the AR HMD in the eat-in-
kitchens took 23% longer (Figure 3). In contrast, mark-
ing the slots in the bathrooms took between 29% and
59% less time compared to the conventional approach
(Figure 3). Depending on the area, a time saving of
between 15.8% and 78.3% was achieved when mark-
ing the MEP work using the AR HMD. In particular, the
electrical work took an average of just 7 min com-
pared to 45 min using traditional methods, a time sav-
ing of 84%. In total, a time saving of 18.8% was
achieved for the marking of HVS in the eat-in-kitchens,
bathrooms, and toilets, and a time saving of 57.9% for
the marking of the UFH in the eat-in-kitchens
and bathrooms.
RQ1: accuracy X-Y-axis
Figure 4 displays the average accuracy of the execu-
tion versus the design of the HVS marking work. Here,
the indicator ACC
XY
was measured. Depending on the
number of measurements, the accuracy of the marking
work for MEP in the eat-in-kitchens ranged from
1.80 cm and 9.05 cm (Figure 4). Likewise, the marking
work for MEP in the bathrooms and toilets showed
deviations between 3.00 cm and 10.25 cm. Two meas-
urements showed a deviation of 10.25 cm, and six and
four measurements showed deviations of between
1.80 cm and 4.5 cm, respectively (Figure 4). In sum-
mary, it appears that the accuracy increases with the
number of measurements for the markings carried
out. AR provides the highest accuracy in complex
areas where the operator needs to look for up-to-date
plans and take many measurements. The accuracy
results have an average deviation of 3.47 cm when
marking HVS installations and 1.80 cm when marking
electrical work.
Figure 5 displays the average accuracy compared to
the design of the marking work carried out for the
UFH installation using the AR HMD. Again, only
the ACC
XY
indicator was measured. The accuracy of
the marking work for the UFH in the eat-in-kitchens
resulted in 2.46 cm in A1 and 6.66 cm in A2. The accur-
acy in the bathrooms was 3.5 cm in A1 cm and 2.1 cm
in A2 (Figure 5). In summary, the average accuracy of
marking the UFH with the AR HMD was 3.76 cm. From
Figure 3. Relative time effort compared to the traditional method of MEP and UFH work (negative values represent time saved
and positive values additional time expenditure).
CONSTRUCTION MANAGEMENT AND ECONOMICS 13
this, it can be concluded that the AR HMD showed an
accuracy in the X and Y axes between 2 cm and 4 cm.
According to the semi-structured interviews,
Interviewee 4 reported that the accuracy level of the
AR HMD is insufficient for the bathrooms where a
0.5 cm precision is required. Interviewee 4 observed
that “the price-performance ratio is still unclear and
questionable.”
RQ1: Usefulness
All interviewees noted that the AR HMD was perceived
“to be very useful.” Specifically, the participants stated
that “the technology enables my work to be executed
with high quality” (Interviewee 2, 4 and 5), that “the
technology increases the effectiveness of my work”
(Interviewee 3 and 5), and that “the technology
increases the efficiency of my work” (Interviewee 2
and 4). In line with the measurement of the C/T indi-
cator, all participants responsible for the construction
execution stated and confirmed that “the technology
helped me reduce the time it took me to complete
my work” (Interviewee 2, 3, 4 and 5).
RQ2: Training
Before starting the marking work, the research team
gave the case study participants a brief introduction
on how to use the AR HMD in practice. The training
had a duration of 15 min. It consisted of a brief
explanation of how to wear and position the AR HMD
and how to interact with the Microsoft HoloLens.
Interviewees 4 and 5 both stated that “there was no
extensive training required to use the AR HMD” when
Figure 4. Average accuracy of the marking HVS compared to the design (bars in dark grey show average accuracy X–Y and bars
with scattered grey visualise the number of measurements).
Figure 5. Average accuracy of the marking UFH compared to the design (bars in dark grey show the average accuracy X–Y and
bars with scattered grey visualise the number of measurements).
14 P. DALLASEGA ET AL.
supporting the marking work on-site. Thanks to the
very intuitive interaction option with the AR HMD (by
using gestures), the training content was able to be
applied very easily into practice (Interviewees 1, 2
and 3).
RQ2: understanding/action ratio to total time “U/
a ratio”
Figure 6 shows the calculated U/A ratio for each fore-
man (Interviewee 2 until Interviewee 4) who used the
AR HMD to support the marking work on-site. The
time measurements were extracted from the regis-
tered videos of the marking work carried out.
Comparing the works with and without AR, the U/A
ratio showed a gain of 19.3%, 24.6%, 14.3% and 16.6%
for the foremen involved, respectively. Considering the
average, a value of 95.4% (with AR) and 76.7% (with-
out AR) of the U/A ratio was calculated, resulting in an
average gain of 18.7% for all users. Similarly, Loizeau
et al. (2019) achieved an average 14% increase in the
U/A ratio, thus supporting AR aeronautical assembly
tasks. Reducing the understanding time and increasing
the action time confirms that AR adds value by mak-
ing it easier for the operators to process the necessary
instructions.
RQ3: perceived ease of use
All interview participants confirmed that “the technol-
ogy was very easy to use” (Interviewees 1, 2, 3, 4 and
5). Most of the participants in the case study liked
interacting with gestures using the Microsoft HoloLens
2. Interviewees 1, 2 and 3 reported that “the use of
the technology was intuitive.” During the case study,
we used an AR-application in the HMD that uses QR-
markers to register the digital models. Interviewee 4
tested the registering, and it took 13 s with two QR-
markers to register the digital model. Furthermore, he
tested two AR applications, one that used QR-markers
and one that did not use markers for the registering.
Interviewee 4 stated that the AR applications with
markers were “appropriate for my work environment
as they do not require much time and specific training
to be used on-site.” If the registering is done with a
marker-free AR application, it is “too difficult and time
consuming to learn for a conventional foreman on-
site” (Interviewee 4).
RQ3: cognitive workload
All survey participants confirmed that using the AR
HMD requires a high level of concentration or in other
words, more concentration to perform the marking
Figure 6. Comparison of U/A ratio with AR (left) and without AR (right).
CONSTRUCTION MANAGEMENT AND ECONOMICS 15
work on-site than the traditional working method.
However, Interviewees 1, 4 and 5 reported that “using
the technology does not lead to mental fatigue” espe-
cially “when the AR HMD is not used continuously but
only for some specific tasks (like marking work on-
site).” Similarly, the respondents stated that using the
technology “requires more attention” (Interviewees 3,4
and 5) but it “does not interfere with daily work”
(Interviewee 2).
RQ3: ergonomics
To assess the ergonomics, the BIM manager decided to
also use the AR HMD to support the installation activities
which generally take longer than marking work on-site.
Here, Interviewee 2 used the AR HMD to install the
underfloor heating system in sleeping room 1 of apart-
ment 2. He explained that “a different posture is
required to use the technology”and that wearing the
AR HMD for a long period of time leads to “increased
strain to the neck, which causes pain.”This is mainly
caused by the increased weight of the AR HMD. On the
other hand, the participants noted that “the helmet was
ergonomically designed”and that “the helmet is com-
fortable to wear”(Interviewees 1, 3, 4 and 5). From this,
it can be deduced that the ergonomics should be signifi-
cantly improved in order to use the AR HMD over a lon-
ger time span (Kerr et al.2011).
RQ3: work safety
The survey participants noted that the use of the tech-
nology “has an impact on work safety” (Interviewees
1, 2, 3, 4 and 5). Specifically, there is a possible nega-
tive influence “due to an impaired field of vision
(Interviewee 2)” and “due to increased attention”
(Interviewee 5) was also mentioned. Specifically,
Interviewee 5 mentioned potential trip hazards (e.g.
through cables or pipes) and a risk of the occupants
falling (e.g. through unsecured danger areas). These
occupational safety risks cannot be recognised by the
operator due to the overlaying of digital objects with
the physical environment. Interviewee 2 stated that “if
the construction operator pays more attention to the
hologram, potential danger spots on-site could
be overlooked.”
RQ3: attitude towards using
Considering the level of acceptance or in other words
the resistance to change, the statements “the technology
was very useful”(Interviewees 4 and 5) and “it increases
the effectiveness and efficiency of my work”
(Interviewees 2 and 3) were recorded. Moreover, it had
also been noted that “the technology makes my work
easier”(Interviewee 5) and that “the technology helped
me reduce the time it takes to get my job done”
(Interviewees 2 and 4). However, given the level of qual-
ity, not all participants agreed that the AR HMD would
bring in benefits. Specifically, Interviewee 2 stated that
“the registering is too imprecise”and Interviewee 3
pointed out that “the AR HMD has not helped to reduce
errors in surveying and marking work.”
RQ3: behavioural intention to use
Here, Interviewees 4 and 5 noted that “the technology is
suitable for my work environment”and Interviewee 3
stated “I like the use of technology in my job.”Along
these lines, Interviewee 4 emphasised that “the AR HMD
is generally a help for my job”and Interviewee 1
pointed out that “it is generally a helpful solution.”
Moreover, the statements “I would use the technology
after the test phase”(Interviewees 3, 4 and 5) and “I
would recommend the technology to my colleagues”
(Interviewees 1 and 2) were also recorded.
Discussion
AR systems have been used and tested mainly in
industries like logistics, aeronautics, and the automo-
tive industry (Jetter et al.2018, Khokhlovsky et al.
2019, Loizeau et al.2019, Remondino 2020). We
believe that AR helps to overcome the traditional
implementation barriers of VM practices in construc-
tion, resulting in performance improvements.
Considering RQ1, the literature reports difficulties
when measuring the benefits of VM implementations
in construction due to a lack of KPIs and appropriate
measurement metrics (Tezel et al.2016a,2017, Tezel
and Aziz 2017b). Remondino (2020) suggests using
managerial KPIs such as time reduction and accuracy
improvements to measure the benefits of AR in logis-
tic processes. Jetter et al. (2018) suggest that KPIs
should allow for a comparison with conventional
working methods and that each AR application should
be analysed in detail to identify the most appropriate
performance indicators.
In our research, we have empirically validated that
AR-supported VM practices result in significant time
savings for marking works of MEP construction proj-
ects compared to the conventional work approach. On
average, about 18% less time was spent on HVS mark-
ing work, around 58% less time on UFH marking work,
16 P. DALLASEGA ET AL.
and about 84% less time on electrical marking work.
According to the surveyed foremen, AR has the great-
est benefit in areas with numerous markings as it
saves time when reviewing the plans and undergoing
collision control. This has also been reported in the lit-
erature by Wang et al. (2020). However, considering
the levels of accuracy, the application of AR in our
case study did not fully meet all customer require-
ments. The average deviations of the marking work
range between 3.47 cm for HVS, 3.76 cm for the UFH,
and 1.80 cm for electrical work. Areas with many
objects (such as bathrooms with washbasins, a toilet,
bidet, bathtub, shower cabin, etc.) require an accuracy
level with a maximum deviation of 0.5 cm.
Furthermore, the accuracy of the UFH marking was
partially compromised by following the underlying
metal grid rather than the AR HMD’s projected holo-
gram (Figure 1). Future research activities should con-
sider a specific distinction between the accuracy levels
of the technology as the surveying technology in use
(e.g. laser scanner) in terms of the AR application
including its registering functionality (e.g. 2, 3, or 4
markers) and the errors induced directly by the human
operator.
The benefits of the AR HMD are also supported by
the participants’ subjective perception of “Perceived
Usefulness.” The foremen reported a substantial
increase in work efficiency and perceived the AR HMD
to be easy to use in their work. This is also confirmed
by the work of Jetter et al. (2018) who identified a
strong correlation between the reduction in time
required for assembly tasks, the reduction in errors,
and the KPI “Perceived Usefulness.” Based on the case
study results, the AR HMD led to measurable benefits
in terms of time saving and perceived usefulness.
Furthermore, the use of the AR HMD for MEP layout
and marking tasks resulted in generally acceptable
accuracy values except for certain