A Bibliometric Review of the Evolution of Building Information Modeling (BIM) into Digital Twin (DT) with the Implementation of Internet of Things (IoT).pdf

`A Bibliometric Review of the Evolution of Building
Information Modeling (BIM) into Digital Twin (DT) with the
Implementation of Internet of Things (IoT)
Nourhan Serag1[0009-0002-8385-0491], Alaa Hammad1[0009-0007-2255-7634] and Khaled Gharib1[0000-0002-3294-
9339]
1 Structural Engineering Department, Faculty of Engineering, Cairo University, Egypt
2noourhanm@gmail.com, 2alaa.hammad184@gmail.com, 2kgharib98@gmail.com
Dr. Mohamed Marzouk1[0000-0002-8594-8452]
Abstract. Building Information Modeling (BIM) is a powerful tool that
allows architects, engineers, and construction professionals to
create detailed 3D models of buildings, which contain a wealth
data about the physical characteristics and attributes of the
project, one of the key benefits that it allows stakeholders to
visualize the project in great detail, which can help identify
potential problems or inefficiencies before construction even
begins. However, BIM data is static and does not reflect
changes or updates that occur after construction is complete.
As the construction industry continues to modernize, Building
Information Modeling (BIM) has become a staple in the design
and construction phases of a project. The widespread adoption
of Building Information Modeling (BIM) and the recent
emergence of Internet of Things (IoT) applications offer several
new insights and decision-making capabilities throughout the
life cycle of the built environment. In recent years, the ability of
real-time connectivity to online sensors deployed in an
environment has led to the emergence of the concept of the
Digital Twin of the built environment. This is where the concept
of the digital twin has emerged to revolutionize the way
buildings are managed and operated throughout their entire
lifecycle. A digital twin is a virtual replica of a physical asset or
system that incorporates real-time data from sensors and other
sources, by connecting BIM models to IoT sensors and other
data sources, it is possible to create a dynamic digital twin that
can provide real-time insights into the performance and
condition of a building. Overall, the integration of BIM and IoT
into digital twins offers a powerful tool for designers, engineers,
and building managers to optimize the performance and
efficiency of buildings. This paper conduct a bibliometric review
over the evolution of BIM to DT, examining the benefits of each
technology and how Digital Twin expand on the capabilities of
BIM. The paper also will discuss Digital Twin and BIM in the
industry, discussing real-world applications and the tangible
benefits that organizations have experienced. Ultimately, this
paper highlights the importance of embracing new
technologies like Digital Twins to achieve optimal efficiency and
cost savings in the building industry.
Keywords
Digital Twin (DT)
Building Information Model (BIM)
Internet of Things (IOT)
Construction Industry
Architectural Engineering

1 Introduction
Technological innovations and expansion are key
components of any industry’s success, Either Farm or non-
Farm industries. City information modelling (CIM) or building
information modelling (BIM) have been established in many
countries, such as the UK National Digital Twin which has
published the “Gemini principles” that put an outline that
includes set of properties a digital twin should adhere to. For
instance, artificial intelligence (AI) is predicted to add 10% to the
UK economy by 2030, and improved data sharing can result in
lower consumer bills, reduce the impact on the natural
environment and realize smart asset management (NIC, 2017).
A Digital Twin (DT) refers to a digital replica of physical assets,
processes and systems. DTs integrate artificial intelligence,
machine learning and data analytics to create living digital
simulation models that are able to learn and update from
multiple sources, and to represent and predict the current and
future conditions of physical counterparts [1].
DTs align well with other related emerging paradigms such as
Cyber-Physical Systems and Industry 4.0, and it is predicted
that half of the large industrial companies will use DTs by 2021,
resulting in those organizations gaining a 10% improvement in
effectiveness [2]. The CDBB (Center for digital built Britain)
created principles for the UK National Digital Twin in 2018,
including guidance to policymakers on how to gather
information and support the public for solutions to social
challenges such as climate change, resilience, future mobility
and social inequality [3].
Due to exponential technological advance, highlighted by
storage capacity and processing power, applications have
contributed to the development of the fourth technology
revolution known as Industry 4.0 (a set of technological
principles aimed at taking full advantage of the new
technologies). This allowed for an easy and rapid connection
between people, assembly lines, machines, robots, and
processes, etc. [4]. According [5], the number of publications on
digital twin show a rapid growth from around 2016, then a
doubling of growth every year. However, data needs to be
stored and shared safely and securely, and technologies also
need to be well-designed and ensure security and efficiency
(NIC, 2017). Therefore, the concept of digital twins (DTs) has
evolved as a comprehensive approach to manage, plan, predict
and demonstrate building/infrastructure or city assets.
The digital twin will allow manufacturers to minimize costs,
boost customer service, and find new ways to generate
revenue. Manufacturers can add value for any machinery's full
lifecycle processes, i.e., from design to maintenance. Now
presently in the industry 4.0 context, sensors' connectivity to the
machinery, machine to machine communication, Real-time
monitoring, advanced analytics, Predictive Maintenance, etc.
are being studied.

In short, we can say Digital twin is the combination of the
different techniques which enables users to understand,
predict, and optimize the performance. Predictive maintenance
(PdM) is one of the significant areas that other industries and
researchers are focusing on. It can be applied to many types of
machinery that help reduce unplanned downtimes.
1.1 Digital Twin Definitions
Table 1: Definitions of Digital Twin
Reference Definition Industry
[6] A Digital Twin (DT) refers to a
digital replica of physical
assets, processes and
systems. DTs integrate
artificial intelligence, machine
learning and data analytics to
create living digital simulation
models that are able to learn
and update from multiple
sources, and to represent and
predict the current and future
conditions of physical
counterparts
Construction
[6] The digital twin can realize the
parallel unfolding of physical-
native and digital-native
processes, while acquiring
and storing heterogeneous
information as semantically
structured data
heritage
reconstruction
[7] A digital twin is a virtual model
of an existing product or
system with an information
exchange both from the
existing system to the virtual
model and vice versa
Energy
[8] A complete virtual description
of a physical product that is
accurate to both micro and
macro level.
Manufacturing
[9] High fidelity model or
multidisciplinary simulation,
without considering real-time
connection with the physical
object.
Industry
As mentioned above in Table 1, Digital twin has a various usage
and implementation in different industries, whether all of them
agreed on the same thing that it exchanges information from an
existing model to virtual model and vice versa. The Digital twin
concept appeared for the first time in the aerospace and
aviation industry and then to the other industries, either farm or
non-farm industries. Digital twin researches have boosted in the

last 5 years in the research field as it has a great impact in
minimizing the cost by decreasing the human error.
1.2 Structure of the Paper
Our paper contains 5 main chapters which summarizes our
bibliometric analysis of digital twin/BIM/Internet of things
research. The 5 main chapters are introduction, related
literature review, Aim, Material and methodologies, Research
gap and conclusion. In each chapter we take a step forward in
our research to have a deep insight about the digital twin
methodologies and technologies. First, in the introduction we
will give a brief summary about digital twin, its usage and related
industries, also we summarized some definitions from different
industries related to the DT. And then in the we have the paper
Aim which will have the questions related to our research and
then we’ll see how we use the VOS to make our bibliometric
analysis, and then we have the five used steps in our
methodology containing analysis related to Co-authorship and
authors, Co-authorship and countries, etc. Then last, we have
our conclusion then the research gap identified from the
previous papers.
2 Paper Purpose
The main objective of this study was to perform a
bibliometric analysis of the articles published in the Scopus
database on digital twin and BIM, and then to determine the
extent to which these keywords were in correlation with IOT
(Internet of things). Therefore, the general search focused on
digital twin and BIM, and the specific search focused on digital
twin, BIM and IOT (Internet of things. For this purpose, the
following questions guided this research:
Q1. Which authors have published the highest number of
publications on the subject, and which authors have the highest
number of citations?
Q2. Which institutions, publishers and countries focused most
of their attention on digital twin and BIM?
Q3. What type of documents are most frequently published?
Q4. What citation nodes are the most influential in the network
map using VOS?
In order to answer all the research questions and achieve the
desired objectives, the article is divided into two sections, one
for the general search using digital twin and BIM, and the other
for the specific search which included IOT. The framework is
presented, the method and materials of the study is presented,
and then the results and discussions on the bibliometric
analysis are then presented. Additionally, the research gap that
was found from the analysis and some future lines of research
are presented, with the article ending with conclusions on the
results found.

3 Methodology
The paper’s methodology is divided into two phases: a
preliminary phase and a descriptive bibliometric phase. The
preliminary phase consists of three stages for the initial
research, keywords identification, study of bibliometric analysis
methods, query string definition, selection of database, and
data cleaning synonyms and non-pertinent word elimination,
this structured approach ensures that the research is conducted
in a systematic and organized manner, allowing for a clear and
concise presentation of the results.
The steps are described as following:
Preliminary phase
Stage 1
preliminary research
Stage 2
database (DB) query
Stage 3
Data analysis
Descriptive bibliometric phase
Stage 4: results’ visualization Stage 5: results’ discussion
Table 2 – Paper Methodology
3.1 Preliminary Phase
The first stage “preliminary research” consist of defining the
research question, topic, or field of interest based on the
individual through search and reading of relevant papers and
identifying keywords that would form the basis of the database
query, after that the most relevant and recurrent words were
selected for the analysis, subsequently different bibliometric
analysis methods were researched to determine the most
appropriate method for the study. To do this, the various
bibliometric analysis tools were compared based on a study
conducted by (23), the study focused on analyzing the features
of the principal tools available for bibliometric analysis. By
undertaking these steps, the researcher ensured the
thoroughness and accuracy of the bibliometric analysis.
Type of Analysis Visualization
Bibliometric
Analysis Tools
Thematic
Authors
Reference
Network
Evolution
Performance
Burst
Detection
Spectrogram
Geospatial
Cluster
Geographical
Overlapping
Density
Overlay
Temporal
Bibexcel * * * * *  *
Biblioshiny * * * * * * * * * * * * *
Bibliomaps * * * * * * *
CiteSpace * * * * * * * * *
CitNetExploerer *
SciMat * * * * * * * * *
Sci2 Tool * * * * * * * * *
VOSviewer * * * * * * * * *
Table 3 - Bibliometric analysis tools features comparison based on [10].

Two software were selected for the bibliometric analysis.
The ﬁrst one is Biblioshiny, which was chosen as the most
comprehensive software based on the comparison. It is a web-
based application developed by Massimo Aria and Corrado
Cuccurullo from the University of Naples and University of
Campania's Luigi Vanvitelli. This tool allows for the analysis of
bibliometric data and their visualization in graphs and maps.
[23]. the second software tool selected was VOSviewer,
developed by the Centre for Science and Technology Studies
(CWTS) at Leiden University in the Netherlands. VOSviewer
permits the creation of bibliometric networks based on co-
citation networks. From the analysis, it was determined that
VOSviewer was complementary to Biblioshiny, as it allowed
for analysis that were not available in the former tool.
Together, these software tools provided a comprehensive
approach to the bibliometric analysis.
The second stage “database query” consist of selection of
a suitable bibliographic database, such as Web of Science,
Scopus, or Google Scholar, and develop a search strategy
based on relevant keywords, authors, institutions, and other
criteria. To do this, a comparison between two main academic
literature collections: web of science and scopus databases
based on a study conducted by (24), Web of Science is a
database created by Clarivate Analytics that covers over
33,000 journals from various fields, including science, social
sciences, and humanities. It includes citation data and allows
for citation analysis, tracking author metrics, and identifying
publication trends. Web of Science is known for its high
citation accuracy and is particularly useful for tracking highly-
cited articles and authors.
Scopus, on the other hand, is a database created by Elsevier
that covers over 76 million records from over 24,000 journals,
including peer-reviewed journals, trade publications, and book
series. It offers comprehensive coverage across a wide range
of subjects and includes tools for citation analysis, author
profiling, and evaluating journal impact.
The comparison of WOS and Scopus discovers that WOS has
strong coverage which goes back to 1990 and most of its
journals written in English. However, Scopus covers a superior
number of journals but with lower impact and limited to recent
articles. Therefore the database chosen for the bibliometric
analysis was Scopus. The selected database was ideal due to
the vast amount of data available on the topics explored in this
paper.
The third stage “data analysis” consists of extraction and
cleaning of the data, after identifying the appropriate database,
the researcher needed to formulate the query string to retrieve
the desired and relevant data from the chosen database, the
researcher utilized specific Boolean operators and
incorporated the previously identified keywords into the query
string. The Scopus database offers an advanced search

feature, which allowed the inclusion of synonyms of a word to
expand the search results. The query string that was used in
this study incorporated the identified keywords along with the
Boolean operators and synonyms was:
 (DT OR “Digital Twin”) AND (BIM OR “Building Information
Modeling” OR “Building Information Modelling”) OR (IoT
OR “Internet of Things”) AND (Construction Industry OR
“Architectural Engineering”) AND (LIMIT-TO
(LANGUAGE, “English”)).
The research was conducted in May 2023 and was limited to
articles, review, and conference papers written in English, The
search strings resulted with 469 Documents. The data were
downloaded in CSV format and then processed using the
software and tools previously introduced in the paragraph.
Subsequently data cleaning was conducted in which consisted
of the elimination of synonyms and non-pertinent words present
in the articles, before running the analysis as shown
Topic Synonyms Term
Digital Twin dt, digital twins, digital twin dt
Building
Information
Modelling
bim, building information modeling
(bim), building
information modelling (bim),
building information modelling,
building information modeling, BIM
bim
Internet of Things iot, internet of things (IOT), internet
of things, internet of thing
iot
Construction
Industry
construction sector, construction,
construction industry
construction
industry
Architectural
Engineering
Architectural design, architecture,
architectural engineering
architectural
engineering
Table 4 – List of synonyms
Dataset Information
Total number of documents 469
Timespan 2015:2023
Sources 245
Average citations per document 14
Document Type
Article 266
Review 104
Conference paper 99
Authors
Number of authors 1669
Authors of single-authored docs 13
Authors’ collaboration
Single-authored documents 13
Co-authors per document 4.47
Document contents
Keywords Plus 3173
Author’s Keywords 1449
Table 5 – Dataset Information

3.2 Descriptive Bibliometric Phase
The second phase consists of conducting appropriate
bibliometric techniques and indicators, such as citation
analysis, co-authorship analysis, journal analysis, or keyword
analysis, to identify patterns and trends in the literature, and use
visualization tools, such as tables, figures, maps, or network
graphs, to present the results in a clear and understandable
way, finally interpret the results and discuss their implications
for the research question or field of interest, including strengths,
weaknesses, and limitations of the study. By following a
rigorous methodology, bibliometric research can provide
valuable insights into the structure, evolution, and impact of
scientific knowledge.
3.2.1 Frequency Analysis
A. Yearly Distribution and Growth Tends
Publications Per Year
It is a bibliometric analysis method that involves examining
the number of publications in a particular field or topic area
over time. This analysis helps to identify the growth trajectory
of the field, as well as any significant trends or changes in
publication output. To conduct the analysis in biblioshiny, the
researcher would first need to retrieve the publication data for
the topic of interest using a query string. Once the data is
obtained, the researcher can conduct analysis and apply
filters to group the data by year, and then calculate the total
number of publications for each year. The yearly distribution
of publications can be visualized using charts or graphs,
allowing the researcher to identify any growth trends or
patterns over time. Additionally, the researcher can calculate
the growth rate of the field by comparing the number of
publications in successive time periods. This type of analysis
is useful for understanding the evolution of a particular field or
topic, identifying areas of emerging research, and tracking
changes in the focus or direction of research. It can be used
to inform research funding decisions, identify potential
collaborators, and inform strategic planning for research
institutions.
Figure 1 – Annual Scientific Production with biblioshiny

Year Number of Publications
2023 115
2022 197
2021 102
2020 35
2019 13
2018 2
2017 1
2016 1
2015 3
Table 6 – Number of Publications per year
B. Documents per year by source
This method involves examining the number of publications for a
specific topic or field, published in a particular source over time. To
conduct this analysis, the researcher would first need to retrieve
the publication data for the topic of interest using a query string.
Once the data is obtained, the researcher can apply filters to group
the data by year and source, and then calculate the total number of
publications for each year and source. The analysis allows the
researcher to identify the most productive sources of publications
for the topic of interest, as well as any changes in productivity over
time. It helps to identify the sources that publish the most relevant
research output in a particular field or topic and can assist
researchers in determining the best source for their own
publication. This analysis is particularly useful for identifying
publications in high-impact sources, such as highly reputable
journals, which can be important for researchers in determining
where to submit their own work for publication. It can also assist in
identifying the most influential sources in a particular field, which
can be useful for assessing the impact of research output or
tracking research trends over time.
Figure 2 – Sources’ Production over time with biblioshiny

Year
Sustainability
(Switzerland)
Buildings
Applied Sciences
(Switzerland)
Automation In
Construction
Sensors
2015 0 0 0 0 0
2016 0 0 0 0 0
2017 0 0 0 0 0
2018 0 0 0 1 0
2019 0 0 0 1 0
2020 1 0 1 3 0
2021 5 2 7 5 4
2022 12 13 12 12 10
2023 20 19 18 16 13
Table 7 – Number of Publications per Sources per year
C. Documents by author
This method involves examining the publication output of
individual authors in a particular field or topic area. To conduct
this analysis, the researcher would first need to retrieve the
publication data for the topic of interest using a query string.
Once the data is obtained, the researcher can apply filters to
group the data by author, and then calculate the total number
of publications for each author. This analysis allows the
researcher to identify the most productive authors in a
particular field or topic area, as well as any changes in
productivity over time. It can help to identify potential
collaborators or experts in a particular field, and can provide
insight into the research interests and areas of expertise of
individual authors. Overall, "Documents by author" analysis in
Scopus is a valuable tool for identifying key researchers and
experts in a particular field, as well as assessing the quality
and impact of an author's research output.
Figure 3 – Authors’ Production over time with Scopus Analysis

Figure 4 – Authors’ Production over time with biblioshiny
Author Name
Number of
Publications
Liu, Z. 8
Parlikad, A.K. 7
Anumba, C.J. 6
Xie, X. 6
Yitmen, I. 6
Lu, Q. 5
Zhong, R.Y. 5
Agrawal, A. 4
Aliu, J. 4
Fischer, M. 4
Heaton, J. 4
Joshi, S. 4
Lv, Z. 4
Madubuike, O.C. 4
Oke, A.E. 4
Sharma, M. 4
Shi, G. 4
Singh, V. 4
Zheng, P. 4
Table 8 – Number of Publications per Authors
D. Documents by affiliation
Documents by affiliation" analysis in Scopus is a bibliometric
analysis method that involves examining the publication output of a
particular organization, institution, or university in a particular field
or topic area. To conduct this analysis, the researcher would first
need to retrieve the publication data for the specific topic of interest
using a query string. Once the data is obtained, the researcher can
apply filters to group the data by affiliation, and then calculate the
total number of publications for each organization or institution. This
analysis allows the researcher to identify the most productive
organizations or institutions in a particular field or topic area and
track their publication output over time. It can help to identify
potential research partners or competitors, and can provide insight
into the research areas of focus for each organization or institution.

The analysis can also be used to calculate affiliation metrics such
as the h-index or citation count, which can be used to measure the
impact of an organization's research output. This information can
be used in research evaluations, institutional rankings, or in
identifying potential funding opportunities.Overall, "Documents by
affiliation" analysis in Scopus is a valuable tool for identifying the
most productive organizations or institutions in a particular field,
tracking their publication output over time, and assessing the
impact of their research output.
Figure 5 – Documents by Affiliations with Scopus Analysis
Affiliation Number of
Publications
Ministry of Education China 14
Hong Kong Polytechnic University 11
University of Cambridge 11
CNRS Centre National de la Recherche Scientifique 10
Beijing University of Technology 10
University College London 9
University of Florida 8
Högskolan i Jönköping 8
University of Technology Sydney 8
UNSW Sydney 8
Table 9 – Number of Publications per Affiliations
E. Documents by country or territory
Documents by country or territory" analysis in Scopus is a
bibliometric analysis method that involves examining the
publication output of different countries or territories in a particular
field or topic area. To conduct this analysis, the researcher would
first need to retrieve the publication data for the specific topic of
interest using a query string. Once the data is obtained, the
researcher can apply filters to group the data by country or territory,
and then calculate the total number of publications for each country
or territory. This analysis allows the researcher to identify the most
productive countries or territories in a particular field or topic area
and track their publication output over time. It can help to identify
potential research partners or competitors, and can provide insight
into the research areas of focus for each country or territory. The

analysis can also be used to calculate country or territory metrics
such as the h-index or citation count, which can be used to measure
the impact of a country or territory's research output. This
information can be used in evaluating the research strength of
different nations or regions, or in identifying potential funding
opportunities. Overall, "Documents by country or territory" analysis
in Scopus is a valuable tool for understanding the research activity
and productivity of different countries or territories in a particular
field or topic area, and for identifying potential research partners or
competitors.
Figure 6 – Documents by Countries with Scopus Analysis
COUNTRY/TERRITORY
China 134
United Kingdom 60
United States 59
Australia 47
India 35
Italy 32
Germany 25
Sweden 18
Saudi Arabia 17
France 15
Spain 15
Hong Kong 14
Canada 13
Malaysia 13
Table 10 – Number of Publications per Countries

F. Documents by type
"Documents by type" analysis in Scopus is a bibliometric analysis
method that involves examining the publication output of different
document types in a particular field or topic area. To conduct this
analysis, the researcher would first need to retrieve the publication
data for the specific topic of interest using a query string. Once the
data is obtained, the researcher can apply filters to group the data
by document type, such as journal articles, conference papers,
book chapters, or reviews, and then calculate the total number of
publications for each document type. This analysis allows the
researcher to identify the most common document types used in a
particular field or topic area and track their publication output over
time. It can help to identify the most relevant and influential
document types in a particular field or topic, and can provide insight
into the research areas of focus for each document type. The
analysis can also be used to evaluate the impact of different
document types. For example, journal articles may have a higher
citation rate than conference papers or book chapters, and this
information can be useful in determining the most effective way to
disseminate research results. Overall, "Documents by type"
analysis in Scopus is a valuable tool for understanding the
publication practices in a particular field or topic area and for
identifying the most common and influential document types used
in that area.
Figure 7 – Documents by Type with Scopus Analysis
3.2.2 Word Frequency Analysis
Word frequency analysis is a statistical technique used to
identify the most used words in a given text or corpus. It involves
counting the frequency of each word in the text and then ranking
them in order of frequency, with the most frequent words
appearing at the top of the list. This technique can be used to
identify patterns in the text, such as common themes or topics,
and to gain insight into the vocabulary and writing style of the
author. Word frequency analysis is commonly used in text

mining, natural language processing, and computational
linguistics. It can be performed manually, by counting the words
in the text, or automatically, using software tools that can
process large volumes of text data. By using biblioshiny
analysis tool, the findings from the word cloud, word frequency
analysis, and analysis of words over time provide insights into
the prominence of certain terminologies in the field of Digital
Twin. The results indicate that the term "Digital Twin" has the
highest frequency followed by "life cycle," "architecture design,"
"internet of things," and "decision making." However, when
examining the frequency of these terms over time, "decision
making" exhibited a notable increase from 2019 to 2023, while
the remaining terms maintained a consistent frequency rate
throughout.
Figure 8. Words Cloud (created with biblioshiny)
Figure 9. Most Frequent Words (created with biblioshiny)

Figure 10. Words' Frequency over Time (created with biblioshiny)
3.2.3 Words Network Map
VOSviewer is an analysis tool utilized for mapping and
identifying word associations based on co-occurrence
frequencies. It constructs a comprehensive word network map
by scrutinizing a corpus of text and flagging instances where
multiple words appear jointly. The representation of individual
words as nodes and their interconnectedness delineates the
strength and frequency of their co-occurrence. Our analysis
revealed cluster 1 (yellow) to be linked with digital twin,
simulation, manufacture, and digital storage, whereas cluster 2
(red) exhibited correlations with decision-making, information
management, architecture design, and building information
modeling. Furthermore, cluster 3 (orange) elucidated the
relationship between digital twin and artificial intelligence, while
cluster 4 (purple) delineated the linkages between internet of
things and embedded systems.
A. Co-occurrence Network of All Keywords
Figure 11. Word network map (one or more clusters, link frequency threshold =
10, binary counting, resolution = 1.00) (created with VOSviewer).

B. Co-occurrence Network with authors Keywords
Figure 12. Word network map (one or more clusters, link frequency threshold =
10, binary counting, resolution = 1.00) (created with VOSviewer).
3.2.4 Co-Authorship
A. With Authors
As illustrated in Figure 13, a “co-authorship” analysis of the selected
literature was performed, considering “authors” as the unit of
analysis. In this analysis, the minimum number of documents for
each author is 4, and the number of selected authors is 38,
accordingly. The size of the nodes in this ﬁgure depends on the
number of the authors’ literature while the connecting lines between
them indicate the collaboration between different authors.
Figure 13. Co-Authorship with Authors (created with VOSviewer).

Author Documents Citations Total Link Strength
parlikad a.k. 7 350 7
xie x. 6 289 4
zheng p. 4 260 1
lu q. 5 256 3
heaton j. 4 208 4
liu z. 13 161 6
li x. 4 160 1
zhong r.y. 5 97 4
liu j. 5 94 3
zhang x. 4 91 1
wang x. 4 89 1
guo y. 4 77 1
zhang y. 7 56 3
liu b. 4 53 3
Table 11 – Number of Publications per Authors and Citation
B. With Countries
The results of the country-based co-authorship analysis of the
investigates documents as shown in figure 14, where the size of the
nodes implies the number of published papers and the linking lines
between them show the international collaboration in research. In this
analysis, the minimum number of documents for each country is
considered to be 4, and the number of the considered countries is 41
accordingly. As clearly appears from graphs, China, is the country with
the highest number of publications with 134 published documents, Then
United Kingdom, United States and Australia with number of
publications, 60,59, and 47 respectively.
Figure 14. Co-Authorship with Countries (created with VOSviewer).

Country Documents Citations Total Link
Strength
China 134 1554 54
United
Kingdom
60 1059 41
United States 59 893 39
Australia 47 854 28
France 15 733 9
Singapore 11 604 9
Hong Kong 14 572 12
India 35 486 19
Italy 32 426 15
South Korea 12 397 5
Germany 25 353 12
Canada 13 331 8
Table 12 – Number of Publications per Countries and Citation
3.2.5 Authors Citation
The authors' citation refers to the number of times a particular author's
work has been cited by other researchers in their own work. It is a
measure of the impact and influence of an author's research output in a
particular field of study. The citation count is usually reported as a
numeric value and can be used to compare the performance and
productivity of different authors in a scholarly community.
Figure 15. Authors’ Citation (created with biblioshiny)
Figure 16. Authors’ Citation (created with VOSviewer)

3.2.6 Countries Citation
Figure 17. Countries’ Citation (created with biblioshiny)
Figure 18. Countries’ Citation (created with VOSviewer)
Country Average Article Citations
CHINA 12.10
UNITED KINGDOM 26.10
AUSTRALIA 22.40
FRANCE 37.60
HONG KONG 38.40
SINGAPORE 70.20
GERMANY 18.50
USA 9.90
CANADA 28.30
IRELAND 35.20
ITALY 9.70
ESTONIA 33.50
INDIA 6.00
QATAR 25.20
KOREA 11.80
SWEDEN 4.90
MALAYSIA 10.80
Table 13 – Average Article Citations per Countries

3.2.7 Documents Citation
Figure 19. Documents Citation (created with biblioshiny)
Figure 20. Documents Citation (created with VOSviewer)
Paper Total Citations
POIZOT P, 2020, CHEM REV 346
ZHENG Y, 2019, J AMBIENT INTELL HUMANIZED
COMPUT
253
CHAKRABORTY G, 2021, CHEM REV 252
LIM KYH, 2020, J INTELL MANUF 234
ERRANDONEA I, 2020, COMPUT IND 170
MINERVA R, 2020, PROC IEEE 168
SEMERARO C, 2021, COMPUT IND 157
LENG J, 2021, J MANUF SYST 152
SINGH M, 2021, APPL SYST INNOV 150
LU Q, 2020, J MANAGE ENG 146
WONG JKW, 2018, AUTOM CONSTR 129
WEKING J, 2020, INT J PROD ECON 118
LU Q, 2020, AUTOM CONSTR 109
OPOKU DGJ, 2021, J BUILD ENG 105

WANASINGHE TR, 2020, IEEE ACCESS 94
SEPASGOZAR SME, 2021, BUILDINGS 81
ASADI K, 2020, AUTOM CONSTR 76
RATHORE MM, 2021, IEEE ACCESS 75
JIANG F, 2021, AUTOM CONSTR 73
KRAUS S, 2022, INT J INF MANAGE 71
NASIR V, 2021, INT J ADV MANUF TECHNOL 70
ZHUANG C, 2021, ROB COMPUT INTEGR
MANUF
70
HOU L, 2021, APPL SCI 68
LO CK, 2021, ADV ENG INF 65
OLU-AJAYI R, 2022, J BUILD ENG 63
Table 14 – Total Citations per Paper
3.2.8 Sources Citation
Figure 21. Sources Citation (created with biblioshiny)
Figure 22. Documents Citation (created with VOSviewer)

Sources Articles
AUTOM CONSTR 869
IEEE ACCESS 825
PROCEDIA CIRP 390
SUSTAINABILITY 331
AUTOMATION IN CONSTRUCTION 290
SENSORS 282
CONSTRUCTION AND BUILDING MATERIALS 263
J MANUF SYST 250
IFAC-PAPERSONLINE 225
INORG CHEM 181
JOURNAL OF CLEANER PRODUCTION 181
INT J PROD RES 178
J CLEAN PROD 176
COMPUT IND 162
CRYSTENGCOMM 155
DALTON TRANS 151
APPL SCI 148
PROCEDIA MANUF 143
RENEW SUSTAIN ENERGY REV 135
J AM CHEM SOC 132
ACS APPL MATER INTERFACES 131
CRYST GROWTH DES 129
INT J ADV MANUF TECHNOL 122
ANGEW CHEM 117
ENERGY BUILD 113
Table 15 – Total Articles per Source
4 Conclusion
Digital twin research has earned a worldwide attention from
all tech-related industry’s professionals which will push into
further efforts, innovation and research will continue to improve
global construction practice in service delivery. This study has
reviewed the bibliometric data of relevant published journals
from Scopus database on digital twin generally and DT/BIM/IOT
specifically.
The bibliometric analysis enables researches to have a wide
look and in deep insights into the topic’s potential and identify
the gaps for future research. This helped the authors identify
the various factors that could be considered during research in
Digital Twin. The digital twin has a lot of scope in many fields
such as health care, aviation, precision agriculture, education,
energy sector, etc. This paper mainly focuses on implementing
the digital twin in construction industry using BIM/IOT due to its
broad scope in the AEC field.

5 Research Gap
Based on our bibliometric analysis of the SCOPUS
database papers related to Digital twin and BIM, we found that
most of the papers focus on the implementation of digital twin
within the manufacturing and construction industry. Less than 5
papers only focus on the integration between digital and facility
management, which have a great impact on the integration
between different stakeholders. Less attention also has been
paid to the operation & maintenance (O&M) phase.
6 Future Recommendations
The next research should focus on the operation and
maintenance (O&M) phase which is the longest time span in the
asset life cycle. If this research is done correctly, it will bridge
the gap between human relationships with buildings/cities.

References
[1] Q. P. A. W. P. (. H. J. S. J. Lu, "Developing a Digital Twin at Building and City
Levels: Case Study of West Cambridge Campus," Journal of Management in
Engineering, 2020.
[2] Q. P. A. W. P. (. H. J. S. J. Lu, "Developing a Digital Twin at Building and City
Levels: Case Study of West Cambridge Campus," Journal of Management in
Engineering, 2020.
[3] E.-Y. K. S.-Y. Ahn, "Digital Twin Application and Bibliometric Analysis for
Digitization and Intelligence Studies in Geology and Deep Underground
Research Areas," Data, pp. 8(4),73, 2023.
[4] L. Ante, " Digital twin technology for smart manufacturing and industry 4.0: A
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Manufacturing Letters, pp. 27: 96-102, 2021.
[5] F. Tao, 2021. [Online].
[6] A. G. A. D. L. L. .. L. Z. T. Gros, "Faceting the post-disaster built heritage
reconstruction process within the digital twin framework for Notre-Dame de
Paris," Scientific Reports, 2023.
[7] D. P. M. Bayer, "A digital twin of a local energy system based on real smart
meter data," Energy Informatics, 2023.
[8] C. S. A. N. J. Y. B. H. D Jones, "Characterising the Digital Twin: A systematic
literature review," CIRP Journal of Manufacturing Science and Technology, 2020.
[9] S. F. H. D. C. X. M Liu, "Review of digital twin about concepts, technologies,
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[10] J. A. Moral-Muñoz, E. Herrera-Viedma and A. Santisteban-Espejo, "Software
tools for conducting bibliometric analysis in sciene: An yp-to-date review," El
profesional de la información, 2020.
[11] H. S. M. M. Y. H. F. M. F. M. F. &. N. A. E. Arezoo Aghaei Chadegani, "A
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