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Digital Twin in construction: An Empirical Analysis
Mahmoud El Jazzar1*, Melanie Piskernik2**, Hala Nassereddine Ph.D.1***
1 Construction Engineering and Project Management, University of Kentucky, USA
2 Institute of Interdisciplinary Construction Process Management, Technical University of Vienna, Austria
* meljazzar@uky.edu
** melanie.piskernik@tuwien.ac.at
*** hala.nassereddine@uky.edu
Abstract. Construction is one of the most information-intensive industries where information needs
to be readily available, accurate, timely, and in a format that is understandable by the recipient.
Information needs to be effectively exchanged throughout the lifecycle – from conceptual planning
to decommissioning. As Industry 4.0 continues to evolve, it is imperative that construction adopts
new technologies. A key element of the Industry 4.0 roadmap is Digital Twin, a digitization
technology that allows the physical and virtual space to communicate. While Digital Twin is rapidly
being adopted in multiple sectors, the technology has the potential to leverage construction data.
This paper synthesizes the current state of practice of Digital Twin in construction by reviewing the
extant literature and proposes a framework that classifies the level of integration in construction into
three subcategories namely: Digital Model, Digital Shadow, and Digital Twin. A conceptual
illustration of Digital Twin in construction is also presented.
Introduction
Construction is one of the most information-intensive industries where information needs to be
readily available, accurate, complete, timely, and in a clear format that is understandable by the
recipient (Xu et al., 2014). Massive data is generated throughout the project lifecycle of a
construction project – from conceptual planning to decommissioning. The success of the
construction project relies heavily on the management of flow of information and the ability to
process the sheer volume of data and extract useful insights (Bilal et al., 2016). Researchers
noted that information management is an integral part of the lifecycle of a construction project
– from when the information is being generated, transmitted, and interpreted to when the
information enables the project to be built, maintained, reused, and eventually recycled
(Onyegiri and Nwachukwu, 2011). While the importance of information throughout the project
lifecycle has been recognized, studies have focused on information management during design
and construction. While these phases are critical, they only account for about 30 to 40 percent
of the total project cost (Jiang, 2013). The operating and usage phase is the phase that accounts
for the majority of the total project lifecycle (60 to 80 percent) (Jiang, 2013; Nicał and
Wodyński, 2016).
Although the construction industry is often labeled as conservative regarding potential
advancements in technology and technological applications, it has made significant strides over
the past few decades to improve information management through the use of Building
Information Modeling (BIM) (Nassereddine et al., 2019). BIM has transformed the traditional
paradigm of construction industry from 2D-based drawing information systems to 3D-object
based information systems (Arayici et al., 2011; BIM Alliance, n.d.). For more than a decade,
BIM has been one of the most important innovation means to approach building design
holistically, enhance communication and collaboration among key stakeholders, increase
productivity, improve the overall quality of the final product, reduce the fragmentation of the
construction industry, and improve its efficiency (Succar, 2009; Schweigkofler et al., 2018).
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One of the greatest benefits of BIM is its ability to represent in an accessible way the
information needed throughout a project lifecycle, rather than being fragmented (Carlsén and
Elfstrand, 2018).
While extensive research studies have been conducted to explore the use-cases of BIM and
assess its benefits throughout the lifecycle of the construction project, BIM does not capture the
data generated during the operating and usage phase (Khajavi et al., 2019). As the fourth wave
of technological advancement (Industry 4.0) continues to evolve, it is imperative that
construction adopts new technologies. A key element of the Industry 4.0 roadmap is Digital
Twin, a digitization technology that is designed to monitor a physical asset and improve its
operational efficiency through the collection of real-time data which enables predictive
maintenance and results in well-informed decision-making (Khajavi et al., 2019).
Fenn and Raskino (2008) explained that there are five major stages in the growth, dissemination
and development of a technology. Collectively, they are referred to as the ‘innovation hype
cycle,’ as depicted in Figure 1. The hype cycle begins with the trigger (step 1) where a
breakthrough event or prototype generates interest in an innovation. Once this trigger occurs,
there is a rapid increase in hype as the cycle reaches the peak of inflated expectations (step 2).
In this stage, advanced companies and consumers seek out the innovation and adopt it early.
However, as time passes but before measurable results are returned, impatience produces the
trough of disillusionment (step 3). The innovation does not simply waste away into nothingness
at this point. Some early adopters and researchers overcome the challenges and begin to reap
benefits, then commit to moving forward. This is the slope of enlightenment (step 4). Finally,
after the aforementioned enlightenment, the applications of the technology to the real world are
defined and the innovation reaches the plateau of productivity (step 5). The growth and lifecycle
of Digital Twin can be plotted on the hype cycle, as shown in Figure 1. Per Gartner, who
developed the hype cycle, Digital Twin is a promising technology that is still in its
developmental phases. As of 2019, Gartner placed it at the peak of inflated expectation
(Campos-Ferreira et al., 2019).
Figure 1: Innovation Hype-Cycle of Digital Twin
[Reproduced from (Campos-Ferreira et al., 2019)]
The main objectives of this research are to gain an understanding of the potential of Digital
Twin in the construction industry and suggest a framework that outlines the applications of
Digital Twin in construction, its perceived benefits, the challenges associated with its
implementation, and the requirements needed to generate a Digital Twin.
The research objectives are achieved through an extensive and comprehensive review of the
extant literature on Digital Twin. Research endeavors that have investigated the implementation
of Digital Twin in non-construction industries were reviewed, examined, and analyzed to gain
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a thorough understanding of the origin, evolution, and capabilities of Digital Twin. Research
efforts that have discussed the use of Digital Twin in the construction industry were then
studied.
Digital Twin Concept
The concept of Digital Twin can be traced back to 2002 when Dr. Michael Grieves from the
University of Michigan gave a presented on what he called Conceptual Ideal for Product
Lifecycle Management (PLM) (Grieves and Vickers, 2016). The PLM concept, which has all
the elements of the Digital Twin, considers that each system consists of two systems: the
physical system or the real space that has always existed and a virtual system that contains all
the information related to the physical system. These two systems are linked and thus,
information flow is enabled between the physical and virtual systems (Grieves and Vickers,
2016). The first Digital Twin implementation was in 2010 when the National Aeronautics and
Space Administration (NASA) on the Apollo program where at least two identical space
vehicles were built to allow mirroring or twinning of the condition of the real space vehicle
throughout the mission (Campos-Ferreira et al., 2019; Schleich et al., 2017). Schleich et al.
(2017) stated the first formal definition of Digital Twin was provided by NASA where Digital
Twin was described as “an integrated multiphysics, multiscale, probabilistic simulation of an
as-built vehicle or systems that uses the best available physical model, sensor updates, fleet
history, etc., to mirror the life of its corresponding flying twin”. Other researches provided a
simplified definition of Digital Twin. For example, (Tao et al., 2018) stated that the idea and
concept of Digital Twin is composed of the physical product, the virtual product, and the
connected data that links the physical and virtual products. The various Digital Twin definitions
focus on three components: the physical space, the virtual space, and the connected data.
Concepts and frameworks that contain these three components therefore correspond to the DT
concept, but the literature also differs in the level of data integration. Some virtual
representations do not allow bidirectional automatic data exchange, whereas this is the case
with fully integrated DTs. In order to resolve the definition of uncertainty, (Kritzinger et al.,
2018) suggests three subcategories in the DT classification. The Digital Model (DM) has the
least data integration, the data flow between the physical object and the digital is done manually.
Changes in the state of the digital or physical object have no direct impact on the state of the
counterpart. When the data transfer between physical and digital objects takes place
automatically, one speaks of the Digital Shadow (DS). With full integration of the data flow in
both directions between the physical and digital object, it is DT in full expression of the concept.
Digital Twin in other industries
With the rapid advances in emerging technologies, industrial interest in Digital Twin has
increased in the past decade. Campos-Ferreira et al. (2019) reviewed 644 publications related
to Digital Twin between 2014 and 2019 and noted that the number of publications has been
exponentially increasing indicating the increased interest in Digital Twin as a promising
enabling technology. The analysis of these publications showed that most of the Digital Twin
research has been in engineering (35.6 %) and computer science (23.5 %). These two areas
include manufacturing, product development, robotics, simulation, communication, analytics
and architecture validation. The authors also examined the impact Digital Twin can have and
concluded that most of the previous research efforts showed that Digital Twin has the
capabilities to improve the product, the design, and the production and to reduce logistics risks.
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They also noted that Digital Twin is not only used to simulate processes and increase efficiency,
but also to predict process behaviors.
In manufacturing, (Kritzinger et al., 2018) argued that the current focus in Digital Twin research
is on production planning and control as this area is considered the connecting data link in the
production process. Qi and Tao (2018) discussed the application of Digital Twin for product
design. They explained that Digital Twin-based product design that allows designers to view,
optimize and verify functions of their product. Once the design is finalized and coordinated, the
product is sent to the smart factory to be produced. From the input of the raw material to the
output of the desired product, the entire process is managed and optimized through Digital
Twin. The Digital Twin of the product remains in constant contact with the finished product to
monitor it in real time. Subsequently, the Digital Twin can predict the future behavior of the
product, which enables proactive maintenance. Using virtual and augmented reality
technologies, the maintenance, repair and overhaul processes can also be optimized. Finally, in
order to improve the next generation of the product, the data of the product life cycle is collected
and inherited.
Another area where Digital Twin is currently being implemented is logistic (Dohrmann et al.,
2019). The entire industry is moving towards open interfaces and cloud-based IT systems that
enable machine learning and advanced analytics. Examples for the application of Digital Twin
in logistics include the following: predicting the conditions inside a packaging using sensor
data, the design of warehouses can be optimized by simulating the product, personnel and
transport flows in it. Taking these applications a step further, logistics networks could be
globally linked and simulated using Digital Twin and geographic information systems (GIS).
As the concept of DT is evolving other industries are adopting DT for increasing their efficiency
as well as their product quality. Even though construction is not using DT in its full extend yet,
the adoption process already started.
Digital Twin in Construction
Review of Existing Literature
With the increased interest in Digital Twin, the construction industry began to follow suit in
this area. While the definition of Digital Twin might seem similar to BIM, construction
researchers highlighted the differences between these two concepts. Khajavi et al. (2019) stated
that although BIM and Digital Twin have similarities, they differ in multiple ways such as the
purpose, technology, the end-users, and the facility life stage. The applications of BIM have
been extensively investigated in the body of knowledge of construction. While BIM is used by
the architects/engineers during the design phase of the project to perform clash detections and
material take-off and by contractors to conduct production control, constructability analysis,
site and safety management (Volk et al., 2014), it does not work with real-time data (Khajavi
et al., 2019). Digital Twin, however, is implemented to monitor the physical asset and improve
its operational efficiency by analyzing real-time parameters (Khajavi et al., 2019). The Digital
Twin of a building for example can be used for operation and maintenance purposes by allowing
facility managers to perform what-if analysis and ultimately enhance energy utilization and
improve residents’ comfort (Khajavi et al., 2019). The data collected using a Digital Twin
during the operation and maintenance phase of the facility can be saved in a database to be used
by architects on future projects (Qi and Tao, 2018). Most applications of Digital Twin in
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construction are in the operation and maintenance phase of the facility whether the project is
residential or industrial.
Existing literature on Digital Twin in construction is not easily found because the term “Digital
Twin” is not explicitly mentioned in most papers and, is occasionally referred to as BIM or
BIM-FM (facility management).
Shen et al. (2012) presented a framework for an agent-based web service geared towards facility
lifecycle information integration. The objective of this framework was to support facility
management decisions using data collected throughout the project lifecycle – from planning, to
design, to construction. The proposed information integration framework was built using BIM
and real-time asset tracking and real-time asses monitoring technologies such as wireless
sensors and Radio Frequency Identification (RFID). It is worth mentioning that this integrated
approach has been successfully applied to two industrial projects, however, due to the nature of
the industries, the authors were unable to share the results.
Dibley et al. (2012) proposed an ontology-based solution for sensor-based building monitoring.
The authors developed an intelligent multi-agent software framework, OntoFM, to support real-
time monitoring. The framework consisted of several ontologies such as building ontology,
sensor ontology, topology ontology, and other supporting ontologies. The developed software
was deployed for testing purposes and the results of the validation showed that the framework
can address the current challenges facing the use of ontology for building monitoring.
Lee et al. (2013) suggested a system to advance urban facility management and perform status
monitoring of urban facilities. This proposed approach can intelligently identify facility risks
and prepare the urban facilities management for real-time emergency response. The authors
developed and tested a prototype of the proposed Intelligent Urban Facilities Management
System (IUFMS). Although the proposed system was performed on simple events of
aboveground and underground urban facilities, the results demonstrated a promising approach
for facility emergency risk management.
Ko et al. (2013) implemented a comprehensive facility management solution using RFID
technology to enhance the efficiency of facility management. A web-based RFID facility
management system was developed and consisted of four modules: a data management module
to collect maintenance records, a statistical module to graphically display the collected data, a
schedule module to ensure the facility functions are normal, and a forecast module to predict
the lifetime of complements and avoid facility malfunction using fuzzy neural networks. The
system was developed to accommodate the flexibility of the team in arranging maintenance
time. The system can be used at different locations at different conditions by different crews on
site. The authors concluded that the system minimizes the total operational time and prevents
duplicate maintenance activities.
Lin et al. (2014) proposed a new mobile automated BIM-based facility management system
(BIMFM) to be used in the operation and maintenance phase by facility management
technicians. The mobile BIMFM system was tested on a commercial building project. The
results demonstrated the effectiveness of the system, paving the way for improving the
efficiency of facility management staff, and facilitating the data updates from facility
management to the BIM environment.
Motamedi et al. (2014) provided a framework for generating BIM-based customized
visualizations known as Facility management Visual Analytics System (FMVAS). The
proposed framework utilizes various sources of building knowledge to assist facility
management technicians in finding the root causes of failures. The system consists of four
layers: the user interface, the engine, the knowledge base, and the database. FMVAS integrates
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inspection and maintenance data of the Computerized Maintenance Management System
(CMMS) with BIM data and provides users with customized color-coded visualizations using
users input, and stored data to show possible root causes of failures in the system.
Kang and Hong (2015) proposed a software architecture to support information interoperability
and the effective integration of heterogeneous data obtained from BIM into a GIS-based
facilities management system.
Shalabi and Turkan (2017) noted that existing facility management information systems
shortcomings lack interoperability and visualization capabilities and highlighted the need for a
new approach to optimize data collection for corrective maintenance. They proposed an
approach to link BIM and present alarms reported by facility management systems. The
automated process shows that data can come from different sources and can be integrated into
BIM using IFC. While bidirectional data exchange between the systems still requires future
research, this study shows potential for minimizing the lead time in corrective maintenance.
Peng et al. (2017) studied an airport terminal and shows how valuable the integration of data
mining, data analysis and BIM can be for building operation and maintenance. The use of BIM
and sensor data in the operation and maintenance phase of facilities generates a sheer volume
of data that facility managers have access to. Often times, facility managers have to deal with
increasingly non-intuitive data sets and also error-prone manual data entries, which results in
various challenges. To address this problem, the authors suggested a BIM-based Data Mining
approach to analyze the accumulated data and extract meaningful laws and patterns and detect
improper records. In this proposed approached, the BIM database is first integrated into a data
warehouse. Three different Data Mining methods are then performed one after the other,
namely cluster analysis to find relationships of similarity among records, outlier detection
technique to clean the database, and improved pattern mining algorithm to find deeper logic
links among records. Facility managers work with a few high-quality data records rather than
going over an unmanageable number of individual entries.
Arslan et al. (2017) introduced a Highly Archived Distributed Object-Oriented Programming
(Hadoop) architecture, which merges BIM and real-time wireless sensor data to reduce safety
hazards in facility management. The safety hazards considered in the study are ranked
according to their severity for vacant properties fire, vandalism, burglary and undetected water
damage. The authors developed a prototype where wireless sensors (motes) were programmed
and initialized to record temperature, activity and water values. The raw sensor data was then
processed and loaded in the Hadoop storage via Flume, same as the BIM data which was
integrated with Scoop. The framework used Hive – a open-source solution to structure the
already integrated storage data – to link the stored sensor data to the BIM data considering
unique identification of the rooms. The framework can be used to build a proactive safety
facility management system, in which facility managers of vacant properties are notified about
hazards in real-time.
Suprabhas and Dib (2017) suggested integrating wireless sensor data into facility management.
The collected sensor data should be linked and reported via the BIM model, using Construction
Operations Building Information Exchange (COBIE) as the exchange format. COBIE is an IFC-
based data exchange application that is usually integrated into the BIM modelling software via
plugin. The suggested application was designed to assist facility management personnel with
early detection of issues and maintenance checks.
Hu et al. (2018) noted that Mechanical, Electrical and Plumbing (MEP) information is currently
being transferred to the operation and maintenance phase unstructured or in hard copy. The
authors proposed a solution for the intelligent integration of MEP systems into the as-built BIM
model. Therefore, an automatic logic chain between the MEP systems was generated by using
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an algorithm and the equipment is grouped and labeled according to a scheme. To support the
delivery of the as-built model extended by MEP information, an algorithm for the generation
of GIS maps was proposed. A cross-platform was developed using the extended as-built model
and the operation and maintenance management system to perform routine tasks and to respond
efficiently to MEP emergencies. The system presented was applied to a case study. The results
show that the owners were able to reduce the time and costs by 20 % due to the presentation of
the MEP information. Additionally, the maintenance staff was enthusiastic because the solution
gives easy access to relevant information, real time visualization is possible, the response time
to emergencies is optimized and the overall collaboration is promoted.
Chen et al. (2018) presented a framework which supports automatic scheduling of maintenance
work orders to enhance facility maintenance management (FMM) and improve decision-
making. The FMM framework was created by linking data from BIM and facility management
systems (FMS) through proposing an IFC extension for maintenance tasks. Once BIM and FMS
data were integrated, errors in components were visualized and geometrical and semantic
information of the dialed component could be extracted from the BIM models. The maintenance
work order schedule can be automatically generated using a modified Dijkstra algorithm. The
algorithm considers four factors: problem type, emergency level, distance among complements,
and location. The feasibility of the proposed framework was validated in indoor and outdoor
3D environments.
Discussion
After reviewing the literature, the authors noticed that although the concept of Digital Twin was
not clearly mentioned in the reviewed publications – the title and abstracts only referred to BIM
or BIM-FM – the content corresponds to the concept of Digital Twin. Therefore, the authors
are proposing a framework for classifying the existing literature and help the construction
industry better understand the level of maturity of Digital Twin in construction. The framework
is based on the work of (Kritzinger et al., 2018) who outlined a classification of Digital Twin
implementation in manufacturing. Their classification included three subcategories, each
having a specific level of data integration. Digital Model is the digital representation of the
physical space that does not involve any automated data exchange between the physical and
virtual space. Digital Shadow builds on the Digital Model subcategory and enables an
automated one-way data flow between the physical and virtual space. Digital Twin takes Digital
Shadow a step further where the physical and virtual space are fully integrated in both
directions.
Based on the research papers that have been examined and the three classification subcategories
defined by (Kritzinger et al., 2018), it can be noted that the construction industry is moving
beyond the current BIM practices, which mainly focus on the utilization of Digital Models in
design and construction. The majority of the publications reviewed by the authors falls in the
Digital Shadow subcategory, which, in the context of the construction industry, indicates that
while data is being collected and linked to the BIM model, changes made to the digital models
do not lead to changes in the physical space. For example, a MEP-system can be monitored
using a Digital Shadow, in case of an emergency the Digital Model would highlight the problem
but would not take any action. A fully integrated Digital Twin would not only shut the
concerning part of the MEP-system down but would also predict a potential emergency before
occurrence and suggest corrective measures. The concept of Digital Twin in construction is
illustrated in Figure 2. To archive Digital Twin in its full potential, the initiation should be at
an early project phase and throughout the whole lifecycle of a facility. The data collection
should start in the design phase using a BIM model. Data should then be continuously updated
and gathered throughout the construction project lifecycle to obtain a fully functional as-built
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model ready for the commissioning phase. In the operation and maintenance phase, the model
aggregates data from various sensors (i.e. pressure, heat). The data is stored and analyzed using
cloud-computing (i.e. data mining and big data). The virtual representation is then updated in
real-time with the essential data and predictions of the behavior of the physical facility. This
functionality gives the owner, the facility manager, and the operator of the facility the ability to
make informed decisions. The bidirectional communication between the physical and virtual
facility also enables proactive maintenance. Moreover, the long-term benefit of applying this
concept is to improve the next generations of construction projects using the knowledge
captured in the Digital Twin. The analysis of the literature review by subcategory is shown
Figure 3.
Figure 2: Framework for the implementation of Digital Twin in construction
Figure 3: Classification of the existing literature based on Digital Twin integration
While Figure 3 shows that the construction industry is currently in the Digital Model
subcategory, it also suggests that the industry is on its way to implement Digital Twin fueled
by the increased interest in Digital Shadow, an intermediate step between the current state and
the future state (i.e. Digital Twin). Additionally, (Dickopf et al., 2019) outlined the following
three-step holistic approach to support this transition: 1) employ simulation tools to understand
the facility and analyze various what-ifs scenarios, 2) use the simulated models to leverage the
integration of Internet of Things (IoT) to enhance the accuracy and efficiency of predicting
future real-data that is expected to be generated throughout the lifecycle of the facility, and 3)
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collect real-data and leverage it with what was simulated to enhance the performance of the
actual facility and influence the early phases of future projects.
Conclusion
This paper provided an overview of the origins and definition of Digital Twin and briefly
discussed some of the Digital Twin applications in manufacturing and logistics. The current
state of Digital Twin in construction was then investigated via literature review and an
illustration of the concept of Digital Twin in Construction was proposed. The culminating effort
of this study is a framework for understanding the current state of implementation of Digital
Twin in the construction industry. The framework was developed through the synthesis of the
extant literature and divided the digital-twin related research into three subcategories: Digital
Model, Digital Shadow, and Digital Twin. Digital Model has no automated links between
physical object and virtual representation (i.e. BIM). Digital Shadow augmented on the Digital
Model concept and has one directional link. Digital Twin represents the highest integration
level using the bidirectional automated link. The analysis of the framework showed that
although construction has made significant strides by going beyond Digital Model, the
application of Digital Twin is still not fully accomplished in construction industry. However, it
can be concluded that the focus of research is currently being shifted toward Digital Twin. The
first step to achieve this shift is to have sufficient data collection and connection to BIM
including sensing data by leveraging the research performed in the Digital Shadow subcategory.
References
Arayici, Y., Coates, P., Koskela, L., Kagioglou, M., Usher, C., O’Reilly, K., 2011. BIM adoption and
implementation for architectural practices. Struct. Surv. 29, 7–25. https://doi.org/10.1108/02630801111118377
Arslan, M., Riaz, Z., Munawar, S., 2017. Building Information Modeling (BIM) Enabled Facilities Management
Using Hadoop Architecture, in: 2017 Portland International Conference on Management of Engineering and
Technology (PICMET). Presented at the 2017 Portland International Conference on Management of Engineering
and Technology (PICMET), IEEE, Portland, OR, pp. 1–7. https://doi.org/10.23919/PICMET.2017.8125462
Bilal, M., Oyedele, L.O., Qadir, J., Munir, K., Ajayi, S.O., Akinade, O.O., Owolabi, H.A., Alaka, H.A., Pasha, M.,
2016. Big Data in the construction industry: A review of present status, opportunities, and future trends. Adv. Eng.
Inform. 30, 500–521. https://doi.org/10.1016/j.aei.2016.07.001
BIM Alliance, n.d. BIM Alliance on BIM. BIM Alliance. URL https://www.bimalliance.se/vad-aer-bim/bim-
alliance-om-bim/ (accessed 12.19.18).
Campos-Ferreira, A.E., Lozoya-Santos, J. de J., Vargas-Martınez, A., Mendoza, R.R., Morales-Menendez, R.,
2019. Digital Twin Applications: A Review 6.
Carlsén, A., Elfstrand, O., 2018. Augmented Construction: Developing a framework for implementing Building
Information Modeling through Augmented Reality at construction sites. Luleå University of Technology.
Chen, W., Chen, K., Cheng, J.C.P., Wang, Q., Gan, V.J.L., 2018. BIM-based framework for automatic scheduling
of facility maintenance work orders. Autom. Constr. 91, 15–30. https://doi.org/10.1016/j.autcon.2018.03.007
Dibley, M., Li, H., Rezgui, Y., Miles, J., 2012. An ontology framework for intelligent sensor-based building
monitoring. Autom. Constr. 28, 1–14. https://doi.org/10.1016/j.autcon.2012.05.018
Dickopf, T., Apostolov, H., Müller, P., Göbel, J.C., Forte, S., 2019. A Holistic System Lifecycle Engineering
Approach – Closing the Loop between System Architecture and Digital Twins. Procedia CIRP 84, 538–544.
https://doi.org/10.1016/j.procir.2019.04.257
Dohrmann, K., Gesing, B., Ward, J., 2019. Digital Twins in Logistics - A DHL perspective on the impact of digital
twins on the logistics industry.
Fenn, J., Raskino, M., 2008. Mastering the hype cycle: how to choose the right innovation at the right time, Gartner,
Inc./Harvard Business School Press series. Harvard Business Press, Boston, Mass.
Grieves, M., Vickers, J., 2016. Origins of the Digital Twin Concept. https://doi.org/10.13140/RG.2.2.26367.61609
10
Hu, Z.-Z., Tian, P.-L., Li, S.-W., Zhang, J.-P., 2018. BIM-based integrated delivery technologies for intelligent
MEP management in the operation and maintenance phase. Adv. Eng. Softw. 115, 1–16.
https://doi.org/10.1016/j.advengsoft.2017.08.007
Jiang, H., 2013. A System Dynamics Model for Manpower and Technology Implementation Trade-off And Cost
Estimation. Electronic Theses and Dissertations 202.
Kang, T.W., Hong, C.H., 2015. A study on software architecture for effective BIM/GIS-based facility management
data integration. Autom. Constr. 54, 25–38. https://doi.org/10.1016/j.autcon.2015.03.019
Khajavi, S.H., Motlagh, N.H., Jaribion, A., Werner, L.C., Holmstrom, J., 2019. Digital Twin: Vision, Benefits,
Boundaries, and Creation for Buildings. IEEE Access 7, 147406–147419.
https://doi.org/10.1109/ACCESS.2019.2946515
Ko, C.-H., Pan, N.-F., Chiou, C.-C., 2013. Web-based radio frequency identification facility management systems.
Struct. Infrastruct. Eng. 9, 465–480. https://doi.org/10.1080/15732479.2010.546804
Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., 2018. Digital Twin in manufacturing: A categorical
literature review and classification. IFAC-Pap. 51, 1016–1022. https://doi.org/10.1016/j.ifacol.2018.08.474
Lee, Jaewook, Jeong, Y., Oh, Y.-S., Lee, J.-C., Ahn, N., Lee, Jaehong, Yoon, S.-H., 2013. An integrated approach
to intelligent urban facilities management for real-time emergency response. Autom. Constr. 30, 256–264.
https://doi.org/10.1016/j.autcon.2012.11.008
Lin, Y.-C., Su, Y.-C., Chen, Y.-P., 2014. Developing Mobile BIM/2D Barcode-Based Automated Facility
Management System. Sci. World J. 2014, 1–16. https://doi.org/10.1155/2014/374735
Motamedi, A., Hammad, A., Asen, Y., 2014. Knowledge-assisted BIM-based visual analytics for failure root cause
detection in facilities management. Autom. Constr. 43, 73–83. https://doi.org/10.1016/j.autcon.2014.03.012
Nassereddine, H., Veeramani, D., Hanna, A., 2019. Augmented Reality-Enabled Production Strategy Process.
Presented at the 36th International Symposium on Automation and Robotics in Construction, Banff, AB, Canada.
https://doi.org/10.22260/ISARC2019/0040
Nicał, A.K., Wodyński, W., 2016. Enhancing Facility Management through BIM 6D. Procedia Eng. 164, 299–
306. https://doi.org/10.1016/j.proeng.2016.11.623
Peng, Y., Lin, J.-R., Zhang, J.-P., Hu, Z.-Z., 2017. A hybrid data mining approach on BIM-based building
operation and maintenance. Build. Environ. 126, 483–495. https://doi.org/10.1016/j.buildenv.2017.09.030
Qi, Q., Tao, F., 2018. Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree
Comparison. IEEE Access 6, 3585–3593. https://doi.org/10.1109/ACCESS.2018.2793265
Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., 2017. Shaping the digital twin for design and production
engineering. CIRP Ann. 66, 141–144. https://doi.org/10.1016/j.cirp.2017.04.040
Schweigkofler, A., Monizza, G.P., Domi, E., Popescu, A., Ratajczak, J., Marcher, C., Riedl, M., Matt, D., 2018.
Development of a Digital Platform Based on the Integration of Augmented Reality and BIM for the Management
of Information in Construction Processes, in: Chiabert, P., Bouras, A., Noël, F., Ríos, J. (Eds.), Product Lifecycle
Management to Support Industry 4.0. Springer International Publishing, Cham, pp. 46–55.
https://doi.org/10.1007/978-3-030-01614-2_5
Shalabi, F., Turkan, Y., 2017. IFC BIM-Based Facility Management Approach to Optimize Data Collection for
Corrective Maintenance. J. Perform. Constr. Facil. 31, 04016081. https://doi.org/10.1061/(ASCE)CF.1943-
5509.0000941
Shen, W., Hao, Q., Xue, Y., 2012. A loosely coupled system integration approach for decision support in facility
management and maintenance. Autom. Constr. 25, 41–48. https://doi.org/10.1016/j.autcon.2012.04.003
Succar, B., 2009. Building information modelling framework: A research and delivery foundation for industry
stakeholders. Autom. Constr. 18, 357–375.
Suprabhas, K., Dib, H.N., 2017. Integration of BIM and Utility Sensor Data for Facilities Management, in:
Computing in Civil Engineering 2017. Presented at the ASCE International Workshop on Computing in Civil
Engineering 2017, American Society of Civil Engineers, Seattle, Washington, pp. 26–33.
https://doi.org/10.1061/9780784480823.004
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., 2018. Digital twin-driven product design, manufacturing
and service with big data. International Journal of Advanced Manufacturing Technology 94, 3563–3576.
https://doi.org/10.1007/s00170-017-0233-1
Volk, R., Stengel, J., Schultmann, F., 2014. Building Information Modeling (BIM) for existing buildings -
Literature review and future needs. Autom. Constr. 38, 109–127. https://doi.org/10.1016/j.autcon.2013.10.023
Xu, X., Ma, L., Ding, L., 2014. A Framework for BIM-Enabled Life-Cycle Information Management of
Construction Project. Int. J. Adv. Robot. Syst. 11, 126. https://doi.org/10.5772/58445
