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Digital Twin in construction: An Empirical Analysis


Abstract and Figures

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.
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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
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.
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).
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
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
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.
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
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
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
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.
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
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
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)
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.
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.
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... The process of extracting the CAD model and building a complex physical entity, usually a 3D object [42] 3D printers and 3D scanners ...
... VR is a step ahead of the virtuality aspect. It allows users to completely dive into 3D experiences [42] Augmented Reality (AR) and Virtual Reality (VR) platforms ...
... The BIM Model and big data act as input data to the digital twin, where the digital twin reflects the information to update the BIM. The interaction of those technologies in the environment is presented in Figure 3, as recommended by [42,[47][48][49]. ...
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The construction sector has undergone several transformations to address adverse environmental , economic, and social impacts. The concept of the circular economy (CE) has transcended into this domain to solve the needs of construction amid resource constraints. Furthermore, advanced digital tools are being implemented across industries owing to the boost given by the fourth industrial revolution. This paper aims to develop a framework that investigates the effect of digital tools on CE implementation in the construction sector. The study is based on a three-step approach, where first, an initial framework design based on a systematic literature review was conducted. This is followed by framework optimization using semistructured interviews with experts and validation through a case study. This study resulted in the development of a new framework, which aims to investigate how advanced digital tools can be used in the construction sector to enhance CE implementation. The contribution of the present study is twofold: (1) the integration (addressing existing research gap) of CE and digitalization concepts in the construction sector; (2) an investigation into the critical barriers, offering insights for construction practitioners.
... DT characteristics can be captured from existing definitions especially from other mature domains as application areas and specific use cases often characterize definitions of DTs. Currently, DT technology is most maturely applied in the fields of manufacturing (including smart design [83][84][85][86], management [87,88] and factory [89,90]), smart architecture [91][92][93][94] and cities [82,95]. Critical features can therefore be summarized by comparing and mapping keywords from articles on DT definitions and concepts, as shown in Table 4. ...
BIM has been playing a pivotal role during the last decade in bringing in revolutionary and systematic changes, especially for the design and construction stages in bridge engineering; while the emerging Digital Twin (DT) technology, mainly applied in the operation and maintenance phases, has great potential to shape a DT-enhanced BIM framework to fully enable whole life cycle digital construction. However, the current adoption of DT in bridge engineering causes conceptual and technical confusion, which hinders the technology fusion to achieve its full potential. This paper aims at filling the gap by conceptualizing a DT-enhanced BIM framework from the perspective of bridge engineering. In total, 125 116 documents on BIM and DT were reviewed, compared, and analyzed; a crucial metrics based performance hierarchy for bridge digital twin was concluded and a DT-enhanced BIM framework was proposed to promote full lifecycle digital bridge engineering implementation. Furthermore, the analysis and conceptual development align well with the existing mature BIM framework and are expected to contribute actively to the future development of BIM and DT and their integrated advanced technologies.
... The first three issues are being addressed by the use of new technologies like BIM [6,1], digital twins [4,7], and especially by the rising use of Offsite Construction [4,6], which brings new technologies to the construction site by manufacturing standardised parts in an assembly line and shipping them only to be assembled on site. This technique is gaining popularity due to its more predictable nature, their use of standardised processes and their repeatability. ...
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We introduce the first automated models for classifying natural language descriptions provided in cost documents called "Bills of Quantities" (BoQs) popular in the infrastructure construction industry, into the International Construction Measurement Standard (ICMS). The models we deployed and systematically evaluated for multi-class text classification are learnt from a dataset of more than 50 thousand descriptions of items retrieved from 24 large infrastructure construction projects across the United Kingdom. We describe our approach to language representation and subsequent modelling to examine the strength of contextual semantics and temporal dependency of language used in construction project documentation. To do that we evaluate two experimental pipelines to inferring ICMS codes from text, on the basis of two different language representation models and a range of state-of-the-art sequence-based classification methods, including recurrent and convolutional neural network architectures. The findings indicate a highly effective and accurate ICMS automation model is within reach, with reported accuracy results above 90% F1 score on average, on 32 ICMS categories. Furthermore, due to the specific nature of language use in the BoQs text; short, largely descriptive and technical, we find that simpler models compare favourably to achieving higher accuracy results. Our analysis suggest that information is more likely embedded in local key features in the descriptive text, which explains why a simpler generic temporal convolutional network (TCN) exhibits comparable memory to recurrent architectures with the same capacity, and subsequently outperforms these at this task.
... The mapping of Industry 4.0 onto the Architecture, Engineering, and Construction (AEC) industry has been coined as Construction 4.0 -a radical transformation that is digitizing and industrializing the AEC industry using technology [3] [4]. Major technologies such as augmented reality, robotics, big data, drones, and digital twins are being heavily investigated to increase their use across the construction project lifecycle [5][6] [7][8] [9]. ...
Conference Paper
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The Architecture, Engineering, and Construction(AEC) industry has undergone a significant and radical transformation in its design and documentation process as it evolved from the days of the drafting board to today’s Building Information Modeling (BIM) process. As BIM remains the center of this transformation, it is important to keep both practitioners and academicians updated on the current state-of-adoption of BIM in construction projects. Thus, this paper presents the results of a BIM survey conducted on 125 respondents representing 83 companies located in the United States of America, United Kingdom, Netherlands, and Canada. The types of the targeted companies varied between Owner, Owner’s Representative(OR), Architect/Engineer (A/E), GeneralContractor/Construction Management (GC/CM), Mechanical Contractor, Electrical Contractor, SheetMetal Contractor, Plumbing Contractor, FireProtection Contractor, Structural Steel Contractor, and Facility Manager. Findings of the paper elaborate on why companies are using and requiringBIM, why companies are not using and requiringBIM, and how BIM is being used by the different company types across the project lifecycle.
... Beyond BIM, the construction industry is currently witnessing the development of digital twins as a new form of managing the design, planning and production operations of construction projects. A construction digital twin aims to leverage data streams from a variety of sources, including site monitoring technologies and AI functions, to enhance reality capture and to enable proactive process management (Sacks, et al., 2020;El Jazzar et al., 2020). The research on digital twin is still in the early stages, and several academic and industrial efforts are starting to invest more in this new framework. ...
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Digital twinning is a new approach to enhance the management of design, planning and construction operations. A construction digital twin aims to enhance the reality capture of ongoing operations using sensing technologies and AI functions to enable proactive process management. While a digital twin is clearly defined in the context of construction operations, where a digital replica is generated out of a physical site; a design digital twin lacks a clear framing as both twins are digital. This paper explores an approach to creating a design digital twin using agent-based simulation to mimic real BIM-based design projects. Accordingly, a digital replica is generated as an agent-based model. In addition, several KPIs are introduced to capture data related to BIM model dynamics. The results show that the suggested KPIs can increase the transparency of the design process, capture development dynamics at the level of BIM model elements, increase situational awareness among designers related to model development status, and identify higher clashing risk zones.
... A change in the status of the physical asset updates the virtual asset, and a revision on the virtual asset leads to a change in the physical asset and processes [38] (Table 4). Most BIM practices currently applied in the construction industry fall within the digital model category of the DT concept [39]. ...
Conference Paper
Robust project controls are a must-have mechanism for successful project management. Timely and reliable project controls data is crucial to diagnose the project's current status, predict the project's future performance, and manage risks. However, traditional project controls practices, tools, and techniques are not standardized, sophisticated, consistent, objective, synthesized, or connected enough to support project management in its entirety. The project team's lack of commitment and trust in project controls are not uncommon due to past experiences with impractical and unreliable project controls mechanisms. With increasing complexity and resource constraints, projects need to have comprehensive and reliable project controls. The negative impact of project complexity and risks on schedule, cost, quality, and safety can be effectively and efficiently managed only by implementing multi-dimensional, design-centric, and data-driven project controls approaches such as digital twin (DT). DT is a promising project controls approach where a real-time virtual replica of physical structure functions as a single-source-of-truth. It helps establish a plan, monitor and control progress, measure the project's current state, forecast its trend, manage risks, and communicate project status with stakeholders. However, incorporating DT into existing project controls practices does not guarantee successful implementation unless the practices meet critical success criteria. This paper reviews current practices, barriers, enablers, opportunities, and future trends for project controls with digital twin and advanced technologies.
... For the application and utilization of these technologies, information in the real world must be accurately implemented in the virtual world. Various attempts are being made to close the gap between the real and virtual world across all industries [3]. This trend is equally applicable in the construction sector. ...
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This paper introduces a modified A* pathfinding algorithm that can be used in building Mechanical Electronic Plumbing (MEP) path design by revising nodes selection process and post-processing. The pathfinding algorithm is used when a computer calculates the optimal path in a given space by algorithmizing how humans intuitively calculate the optimal path. As construction technology is gradually advancing, buildings with large and complex internal structures are increasing, so there is a need to automatically optimize existing design methods that rely on human intuition for a more efficient design. In the case of building MEP design, it is time and money consuming to design paths since they are complexly arranged throughout the building, and designs are frequently changed in response to the nature of the construction industry, where construction errors are frequent. Therefore, an MEP path design optimization module, MEPAutoroute, was developed by implementing a modified A* pathfinding algorithm to solve these problems. Algorithm was applied to seven different exemplary structures with MEP equipment, and the results are analyzed to determine its efficiency.
Purpose Construction cost management is one of the important processes that should be achieved effectively and accurately for successful project delivery. Modern-day construction cost management demands a high level of spatial skills. Augmented reality (AR) can potentially increase the stakeholders’ spatial skills as a supportive technology to traditional cost management tools and techniques. AR is a breakthrough technology that could considerably ease execution in various industries, but AR applicability in cost management has not been studied extensively. Thus, this study aims to explore the use of AR in construction cost management tools and techniques. Design/methodology/approach Data were collected using a qualitative approach consisting of two rounds of the Delphi technique. A total of 22 experts in the construction and information technology fields were interviewed using a purposive sampling technique. The manual content analysis helped analyse data. Findings The study identified AR features with the potential to increase the usage of cost management tools and techniques. AR can enable spatial skills (abilities, thinking and tasks) in most cost management tools and techniques. However, technical, cultural and technical and cultural barriers obstruct the use of AR in the construction industry. Originality/value The usage of AR in construction cost management tools and techniques has not been examined in detail until now. Thus, the study was developed to meet the industry needs and fill the literature gap to investigate the potential use of AR in construction cost management tools and techniques.
With the advancement of Industry 4.0 and the development of science and technology, industries have seen the advantages of digital twin (DT) implementations and concepts. Although the construction industry is still in the early stage of DT development and implementation, it is urgent for the industry to have a DT agenda. This paper identifies 1158 related bibliographic records from the Web of Science and 745 records from the Derwent Patent Database and quantitatively analyzed them to describe patterns of publications. Two knowledge mapping tools were selected to visualize and analyze the literature in the relevant scientific domain. Then, we integrated clustering, knowledge mapping, and network analysis methods to show the current foci and future directions of DT-MT derived from the data of time distribution, journal areas, and subject distribution. The results indicate that DT is a cross-disciplinary information technology with great development potential, and its development in the construction industry is scattered with the leading advancement in the manufacturing of construction materials and components. The derived knowledge map shows three stages of construction DT, including (1) data collection and creation of DT framework and database; (2) implementation of planning and design modules in detail and control of construction sites; and (3) carrying out asset management and fault prediction in the operation and maintenance phase of projects. The novelty of this paper is the specific analysis of DT in the construction industry. This study adds value to the architecture, engineering, and construction industries by shedding light on the focus of DT practical applications and the formation of a development path of construction DT.
Conference Paper
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VR and AR technologies are among the digital tools and techniques of Construction 4.0 which is a new term reflecting the effect of Industry 4.0 digitalization transformation in the construction industry. VR is identified as a digital-driven experience on which the user is transferred into a simulated virtual environment while AR is an interactive experience of a real-world environment whereas AR involves superimposing computer-generated images and information on real-world environment. By increasing complexity and high requirements of buildings, the construction management actions of pre-construction phases play a crucial role on the efficiency and the effectiveness of the building projects. Since the pre-construction phase is mainly associated with its uncertainty; the decisions to be made at this stage brings high risks. This study aims to evaluate the potentials of VR-AR technologies in order to show how VR-AR can be used in various management areas of pre-construction such as feasibility, programming, cost-budget, scheduling, constructability review, communication and coordination. By employing a through literature review, it is also targeted to provide the applicability areas of VR-AR technologies in pre-construction phases. 1
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The concept of a digital twin has been used in some industries where an accurate digital model of the equipment can be used for predictive maintenance. The use of a digital twin for performance is critical, and for capital-intensive equipment such as jet engines it proved to be successful in terms of cost savings and reliability improvements. In this paper, we aim to study the expansion of the digital twin in including building life cycle management and explore the benefits and shortcomings of such implementation. In four rounds of experimentation, more than 25,000 sensor reading instances were collected, analyzed, and utilized to create and test a limited digital twin of an office room facade element. This is performed to point out the method of implementation, highlight the benefits gained from digital twin, and to uncover some of the technical shortcomings of the current Internet of Things systems for this purpose. INDEX TERMS Building Information Modeling, Digital Twin, Life Cycle Management, Internet of Things, Wireless Sensor Network.
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This contribution introduces an approach for the optimization of smart products and systems in the early development phases through a Closed-Loop Systems Engineering approach. This approach – holistic in terms of considering the overall system and its usage context, interdisciplinary, overreaching multiple lifecycle phases, and supported methodologically as well as tool-wise – combines aspects from both Model-Based Systems Engineering and Product resp. System Lifecycle Management to optimize the system by using advanced verification and validation methods and techniques, in particular Model-, Twin- and System-in-the-Loop, and seamless feedback of product usage data to the early development phase. This approach has been prototypically applied at the example of a test bed of an autonomous construction area system of systems.
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With the advances in new generation information technologies (New IT), especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance, etc. On this basis, what similarities and differences there are between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twinning can be complementary, so how they can be integrated to promote smart manufacturing are discussed.
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Huge amounts of data are generated daily during the operation and maintenance (O&M) phase of buildings. These accumulated data have the potential to provide deep information that can help improve facility management. Building Information Model/Modeling (BIM) technology has proven potential in O&M management in some studies, making it possible to store massive data. However, the complex and non-intuitive data records, as well as inaccurate manual inputs, raise difficulties in making full use of information in current O&M activities. This paper aims to address these problems by proposing a BIM-based Data Mining (DM) approach for extracting meaningful laws and patterns, as well as detecting improper records. In this approach, the BIM database is first transformed into a data warehouse. After that, three DM methods are combined to find useful information from the BIM. Specifically, the cluster analysis can find relationships of similarity among records, the outlier detection detects manually input improper data and keeps the database fresh, and the improved pattern mining algorithm finds deeper logic links among records. Particular emphasis is put on introducing the algorithms and how they should be used by building managers. Hence, the value of BIM is increased based on rules, extracted from data of O&M phase that appear irregular and disordered. Validated by an integrated on-site practice in an airport terminal, the proposed DM methods are helpful in prediction, early warning, and decision making, leading to the improvements of resource usage and maintenance efficiency during the O&M phase.
The construction industry is a project-based industry characterized by heterogeneity, extreme complexity and fragmented supply chain. Its complexity is increased by mutual relationships between different stakeholders involved in the creation, management and efficient exploitation of engineering data. Over the years, productivity and reliability in CI has been struggled by a difficulty in sharing information between construction project participants and in providing accurate information on site, which is a primary cause of poor performances.
The Digital Twin (DT) is commonly known as a key enabler for the digital transformation, however, in literature is no common understanding concerning this term. It is used slightly different over the disparate disciplines. The aim of this paper is to provide a categorical literature review of the DT in manufacturing and to classify existing publication according to their level of integration of the DT. Therefore, it is distinct between Digital Model (DM), Digital Shadow (DS) and Digital Twin. The results are showing, that literature concerning the highest development stage, the DT, is scarce, whilst there is more literature about DM and DS.
Incomplete building information in delivery and the lack of compatible tools for Operation and Maintenance (O. &M) have hindered the development of the intelligent management of Mechanical, Electrical and Plumbing (MEP) systems. In fact, the information related to the O. &M management of the MEP system conventionally comes from the completion documents in the forms of hard copies or unstructured digital files, making it hard to search for useful information in the "sea" of documents and drawings. Therefore, digitalization of information is an urgent task to facilitate the intelligent management of the MEP system. As a project deliverable, the as-built information model shall not only contain geometrical information and necessary construction-related data, but also built-in information useful for the intelligent O. &M management. In the present study, based on the Building Information Modeling/Model (BIM) technology, a set of solutions including the automatic establishment of the logic chain for MEP systems, an equipment grouping and labeling scheme and an algorithm to transform BIM information to GIS map model, is proposed to digitalize and integrate the MEP-related information into the as-built model. Subsequently, a cross-platform O. &M management system is developed using the MEP-related information in the as-built model to run routine O. &M tasks and to effectively response to MEP-related emergencies. The developed system is applied to aid the O. &M management of MEP engineering in a real project, showing that the developed system facilitates the intelligent O. &M management and guarantees the security of the MEP system and its subsystems.