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AN INVESTIGATION OF THE COSTS AND BENEFITS OF DIGITAL TWIN IMPLEMENTATION IN CONSTRUCTION By CHRISTIAN TOOCHUKWU ABUGU A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN CONSTRUCTION MANAGEMENT UNIVERSITY OF FLORIDA 2023

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© 2023 Christian Toochukwu Abugu

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To M.E. Rinker, Sr. School of Construction Management, University of Florida

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4 ACKNOWLEDGMENTS My sincere gratitude first goes to my advisor, Dr. Chinemelu J. Anumba for his guidance and support throughout my program. His dedication despite his tight schedule was very helpful in making this project a success. I would also like to thank my committee members: Dr. Aaron Costin, Dr. Eva Agapaki, and Dr. Rui Liu who were instrumental in refining the quality of my thesis. I acknowledge the assistance of esteemed industry professionals l ike John Turner, CEO of Digital Twin Consortium, Igor Starkov, Marc Craddock, and Grayson Savage who provided relevant data for the completion of this project. I would like to thank my parents, Chief/Mrs. S. M. Abugu, and siblings for the unlimited words o f encouragement, advice, and prayers. Special gratitude goes to my sister, Modesta Abugu for cheering me, supporting me, and pushing me to greater heights. I am immensely grateful to Faith Aiya for her constant intellectual and emotional support throughout the challenging times. Numerous persons provided me with academic assistance including Obinna Madubuike, Kofi Asare, and Kwando. I am grateful to you all. Most of all, I thank the Lord Almighty for His unseasoned wisdom, love, grace, and blessings in my l ife.

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5 TABLE OF CONTENTS page LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ .......... 9 LIST OF ABBREVIATIONS ................................ ................................ ........................... 10 ABSTRACT ................................ ................................ ................................ ................... 11 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 13 Background ................................ ................................ ................................ ............. 15 Research Aim and Objectives ................................ ................................ ................. 17 2 LITERATURE REVIEW ................................ ................................ .......................... 22 Overview ................................ ................................ ................................ ................. 22 Digital Twin Definitions ................................ ................................ ..................... 24 Digital Twin Definition for a Construction Project ................................ .............. 27 Digital Twin Key Features ................................ ................................ ....................... 29 Digital Twin Industry Applications ................................ ................................ ........... 32 Application in the Construction Industry ................................ ........................... 32 Design phase ................................ ................................ ............................. 33 C onstruction phase ................................ ................................ .................... 35 Operations and maintenance phase ................................ .......................... 37 Demolition and recovery stage ................................ ................................ ... 38 Application in Other Industry Sectors ................................ ............................... 39 DT applications in smart cities ................................ ................................ ... 39 DT in manufacturing ................................ ................................ ................... 41 DT in healthcare ................................ ................................ ......................... 43 DT in aviation ................................ ................................ ............................. 44 Enabling Technologies of Digital Twin ................................ .............................. 46 The internet of things (IoT) ................................ ................................ ......... 48 Data analytics ................................ ................................ ............................ 49 Extended reality (XR), virtual design and modeling technologies .............. 50 Cloud computing ................................ ................................ ........................ 51 Artificial intelligence ................................ ................................ ................... 51 Benefits of Digit al Twins for the Built Environment ................................ ........... 52 Challenges to Digital Twin Adoption ................................ ................................ . 58 The Cost of Digital Twin Implementation ................................ .......................... 60 DT software ................................ ................................ ................................ 62 DT hardware ................................ ................................ .............................. 62 Education and training ................................ ................................ ............... 63 Digital Twin Costs for a Building Project ................................ ........................... 64

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6 3 METHODOLOGY ................................ ................................ ................................ ... 66 Types of Research Methodologies ................................ ................................ ......... 66 Quantitative Analysis ................................ ................................ ........................ 66 Qualitative Analysis ................................ ................................ .......................... 67 Triangulation/Mixed Method approach ................................ ............................. 67 Costs and Benefits Investigat ion ................................ ................................ ............. 68 Literature Review ................................ ................................ ................................ .... 69 Interviews ................................ ................................ ................................ ................ 70 4 INTERVIEW WITH DIGITAL TWIN IMPLEMENTERS ................................ ............ 73 Overview ................................ ................................ ................................ ................. 73 Int erview Objective ................................ ................................ ................................ . 73 Choice and Selection of Interviewees ................................ ................................ ..... 73 Interview Questionnaire Design ................................ ................................ .............. 75 Data Analysis ................................ ................................ ................................ .......... 76 Conducting the Interviews ................................ ................................ ....................... 76 Findings: Cost of Digital Twin Implementation in Construction ............................... 78 Cost Drivers for Digital Twin Implementation in Construction ................................ . 81 Direct Costs ................................ ................................ ................................ ...... 82 Hardware ................................ ................................ ................................ ... 82 Software ................................ ................................ ................................ ..... 83 Data structure for DT ................................ ................................ ................. 87 Training and expertise ................................ ................................ ............... 89 Mai ntenance and updates ................................ ................................ .......... 90 Indirect Costs ................................ ................................ ................................ ... 91 Opportunity Costs ................................ ................................ ............................. 92 Other Non financial Costs and Inhibiting Factors ................................ .................... 93 Benefits of Digital Twin Implementation in Construction ................................ ......... 95 Project Information Transparency ................................ ................................ .... 95 Real time Tracking and Analysis ................................ ................................ ...... 97 Improved Synergy between Stakeholders ................................ ........................ 99 Safety and Hazard Prevention ................................ ................................ ........ 100 Cost Savings ................................ ................................ ................................ .. 101 Predictive Analysis ................................ ................................ ......................... 103 Material and Resource Tracking ................................ ................................ ..... 104 Key Considerations before Implementing Digital Twin in Construction ................. 105 Choosing the right Project Delivery Method ................................ ................... 106 Digital Twin Inclusion in the Contract ................................ ............................. 107 Using Other Domain Experts ................................ ................................ .......... 107 Data Management/Standardization ................................ ................................ 108 Guidance to Developing and Implementing Digital Twin in Construction .............. 108 Step 1: Define the Digital Twin Scope and Purpose ................................ ....... 109 Step 2: Virtual Representation Development ................................ .................. 110 Step 3: Information Flow and Data Exchange System Development ............. 111 Step 4: Data Analysis System Development ................................ .................. 111

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7 Step 5: Take Ac tions ................................ ................................ ...................... 112 Step 6: Verification and Validation ................................ ................................ .. 112 Step 7: Optimize and Measure: ................................ ................................ ...... 113 5 CONCLUSIONS ................................ ................................ ................................ ... 114 Research Limitations ................................ ................................ ............................ 116 Future Studies ................................ ................................ ................................ ...... 117 APPENDIX : SEMI STRUCTURED INTERVIEW QUESTIONS FOR DIGITAL TWIN IMPLEMENTERS ................................ ................................ ................................ . 118 LIST OF REFERENCES ................................ ................................ ............................. 120 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 131

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8 LIST OF TABLES Table page 2 1 Web of Science categorization for search keyword = "Digital Twin in construction" ................................ ................................ ................................ ....... 23 2 2 ............ 24 2 3 Some Digital Twin definitions ................................ ................................ .............. 25 2 4 Theoretical cost model of Digital Twin for different building types ...................... 65 4 1 Interviewee profiles ................................ ................................ ............................. 74 4 2 Some Digital Twin enabling tools for construction ................................ .............. 83 4 3 Some Digital Twin enabling software ................................ ................................ .. 85

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9 LIST OF FIGURES Figure page 1 1 Digital Twin development milestone ................................ ................................ ... 16 1 2 Research overview ................................ ................................ ............................. 20 2 1 Digital Twin capabilities as opposed to a CAD model ................................ ......... 32 2 2 Digital Twin application categorization in manufacturing ................................ .... 42 2 3 Various Digital Twin application sectors ................................ ............................. 46 2 4 Digital Twin enabling technologies framework ................................ ................... 47 2 5 Realized tangible benefits of Digital Twin to EY company's P&L ........................ 53 2 6 DT Value added services for buildings and asset operations ............................. 54 2 7 Digital Twin implementation challenges in construction ................................ ...... 60 3 1 Types of benefits and costs for projects ................................ ............................. 68

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10 LIST OF ABBREVIATIONS AEC Architectural and Engineering Companies who plan, design, construct, and manage building project AI Artificial Intelligence leverages computers and machines to mimic problem solving and decision making capabilities of the human mind BIM Building Information Modeling is use for digital representation of physical objects CAD Computer Aided Design is the use of computer based so ftware to aid in design processes CBA Cost Benefit Analysis is the evaluation of the cost implications and realizable benefits of engaging in a specific endeavor CPS Cyber Physical Systems are systems where software and hardware components are seamlessly integrated towards performing well defined tasks DT Digital Twins refers to a virtual representation of a physical asset in all instances of real time FM Facility Management refers to a discipline focused on managing a constructed facility for effective performance IoT Internet of Things describes physical object with sensors, processing ability, software and other technologies that connect and exchange data with other devices. NASA National Aeronautics and Space Administration, an independen t body of the U.S. government responsible for the civil space program, aeronautics research, and space research.

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11 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Master of Science in Construction Management AN INVESTIGATION OF THE COSTS AND BENEFITS OF DIGITAL TWIN IMPLEMENTATION IN CONSTRUCTION By Christian Toochukwu Abugu May 2023 Chair: Chimay J. Anumba Major: Construction Management The g lobal transformation of digital technologies has caused the emergence of new concepts instigated by Industry 4.0. One of the most recent concepts is the Digital Twin, which involves the design and development of a virtual copy of a physical asset or system , maintaining a bidirectional connection between the two systems (real and virtual), with the goal of collecting, simulating, and analyzing the data from the virtual environment to enhance the operation of the real system in the real world. Many authors ha ve reviewed the advantages of adopting this concept, and it has continued to attract significant awareness even in the industrial community. It is anticipated that this interest will continue to grow in the forthcoming years. Industries like manufacturing and automotive have made remarkable advancements and transformations in the Digital Twin concept. However, much less adoption has been seen in the construction industry due to the numerous challenges faced by the industry including low productivity, lack o f research and development, the complexity of construction operations, etc. Bearing this in mind, this dissertation discusses the Digital Twin concept, highlighting the key features, some of the enabling technologies, industrial applications, and limitatio ns of its

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12 adoption. Moreover, this study stands out from other works of literature because it in vestigates the cost implications and the benefits of Digital Twin implementation in construction through semi structured interviews conducted with industry prof essionals and current Digital Twin implementers. Interviews with these practitioners also revealed major cost drivers, numerous benefits of the technology adoption for construction projects, as well as key considerations , and a 7 step guidance potential us ers could fo llow for effective implementation to maximize realizable benefits and minimize implementation costs of Digital Twin in construction.

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13 CHAPTER 1 INTRODUCTION development process. Other than developing the infrastructure required for socioeconomic improvement, it is a major contributor to overall economic growth (Abdullah, 2004) . In d ifferent countries, the industry contributes about 8 10% to the economies on average, helping to promote growth and boost the employment rate, (Dixit et al., 2017) . In 2017, construction was responsible for 8.1% of Australia's GDP, 6.5% of the UK's (Sue scún et al., 2016) (Statista, 2015) . As of the first quarter of 2022, the industry contributes 4.10% of GDP in the US accounting for about 650 billion USD (Trading Economics, 2022) . Notwithstanding, the construction sector ha s been faced with lots of development challenges attributed mainly to poor productivity. It is perceived as one of the least digitalized industries and for decades now has been slow in digital technology adoption (Leviäkangas et al., 2017) . A significant effort must therefore be made to ensure the construction industry is meeting the pace of other industry sectors like manufacturing and automotive. Over the years, numerous digital technologies have emerged triggered by industry 4.0, most of which are util ized in different industry sectors but not so much in the construction industry. These emerging technologies such as robotics, Artificial intelligence (AI), the Internet of Things (IoT), advanced computerized designs, predictive models, etc. have helped to create digitized global industries that are now more productive, cost effective, and sustainable. The construction industry has mainly employed traditional construction tools and methods which seemed to have worked over the past decades. However, due to i ts limitations, it is evident that these traditional

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14 techniques cannot ensure consistent quality and meet the growing trend of technological advancements in comparison with other industries. If the industry really needs to transform, it is crucial that the y keep up the pace in adopting recent technological advancements, one of which is the Digital Twin The construction sector is slower in new technology adoption than other industries since doing so increases project risk. The industry also faces additional challenges when integrating digital twins due to variables including high implementation costs and a lack of understanding of their advantages (Barima, 2016) . A significant challenge in the modernization of the construction industry lies in the dynamic nature of the projects and work processes, and the lack of motivation to adopt emerging technologies in comparison to other industries such as manufacturing and automotive. The main contribution of this research would be to understand the value and cost im plications of digital twin implementation in the construction industry. Chapter 2 contributes to the body of knowledge through literature reviews by enunciating the current state of the digital twin with emphasis on the built environment. Other industry ap plications, some enabling technologies, benefits, challenges, and the sparse information found on the cost of building the D igital T win technology were also discussed. The methodology and data collection for this study was described in chapter 3 while Chap ter 4 explains the findings from the interviews with D igital T win implementers. Furthermore, interview conducted with industry professionals revealed some Digital Twin cost drivers as well as factors potential Digital Twin implementers could consider befor e implementation in construction projects . Chapter 5 concludes the

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15 study with recommendations for future research while highlighting limitations of the study. Background Digital Twin emerged from Cyber Physical System (CPS) , a broad concept in construction which descr ibes a bi directional coordination to tightly maintain integrat ion between virtual models and the physical construction in a manner that changes made in one environment are automatically updated in the other environment (Akanmu e t al., 2021a) . Improvements in construction process control, as built documentation, and sustainable building techniques are made possible by the CPS approach. CPS and DTs are widely garnering interest from researchers and practitioners in various indust ries as the urge to integrate cutting edge technologies into enterprises grows. The ideas behind C yber Physical System and D igital Twin revolve around the same fundamental ideas: a close virtual to physical connection, live communication, organizational synergies, and extensive collaboration., however, as indicated by (Tao, Qi, et al., 2019) , Cyber Physical Systems and Digital Twins are different from one another in a number of ways, including their history, development, engineering methods, cyber physical mapping, and core elements . T he Digital Twin is not just a technology, it is essentially an amalgam of different existing technologie s, such as 3D modeling, digital prototyping, machine learning, and artificial Intelligence, IoT, etc. The rising popularity of Digital Twin is a reflection of the unavoidable trend of the physical and virtual worlds becoming more interconnected and interwo ven. As more scholars committed themselves to the study of Digital Twin, the number of pertinent articles started to rise dramatically. Digital twins have come a long way, since Grieves first proposed the idea of a digital representation of a physical obje ct

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16 to their first use in the aerospace industry, courtesy of the National Aeronautics and Space Administration (NASA) and Air Force Research Laboratory (AFRL (Grieves Michael, 2014) ) . Digital Twin has represented the breakthrough of numerous limitations such as digital description, computer algorithms, data acquisition, etc. (Qi et al., 2021b) . Figure 1 1 below shows the milestone progress made over the past decades on the Digital Twin concepts. Figure 1 1. D igital Twin d evelopment m ilestone | a dapted from (Madubuike et al., 2022) The lifecycle of a building primarily consists of design, construction, operation and maintenance, and end of life stages. Each of these stages requires efficient information exchange strategies for intero perability across all lifecycle stages (Vanlande et al., 2008) . Building information modeling, or BIM, is a concept that has been studied in depth in academic circles and put into practice on many construction sites throughout the globe (Azhar et al., 20 11) . Massive technical and operational developments in the AEC business are widely predicted as a result of the BIM. In addition, BIM was created to include extra building information such as specifications, schedules, cost estimates, and maintenance man agement into the 3D Computer Aided Design (CAD) model (i.e., 4D, 5D, and 6D) (Bryde et al., 2013) . With the emergence of IoT and sensor networks,

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17 the integration of real time sensing and the static data provided by BIM models are made possible (Tang et a l., 2019) . This basis of BIM and IoT integration has promoted the emergence of Digital Twin (Vivi et al., 2019) . Research Aim and Objectives Digital Twin has been applied in various industries and yielded results through performance improvement, real t ime monitoring, etc., especially in the aerospace and manufacturing sectors. A s research on D igital T wins in construction continues to expand , t he phrase "Digital Twin" has been mixed up with other technologies like BIM. This demonstrates that the concept is still ambiguous and poorly understood. The fact that there is still confusion regarding the difference between a model and a DT cou ld deter people from utilizing the technology, which would reduce its total uptake. Furthermore, there are few documented benefits in construction due to the low level application of Digital Twins in this industry sector. The traditional tools and methods employed by the construction industry have shown that these techniques cannot ensure consistent quality to meet the growing trend of technological advancement compared to other industries. Just like in any other new venture, it is crucial to understand the economic impacts and potential benefits of this innovative technology to enable construction pr actitioners make informed Digital Twin implementation decisions on their projects. The aim of this research is to conduct an investigation of the value and cost of Digital Twin implementation through interviews with current adopters in the construction ind ustry. The expected findings in this study will provide the research community and potential Digital Twin implementers, both within and outside the construction industry,

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18 with valuable information regarding its adoption. To achieve this aim, the specific r esearch objectives are as follows: 1. To conduct a review of available literature on Digital Twin concepts and develop a definition more suited for the Construction industry. 2. To review published case studies of Digital Twin implementations to identify the fea tures, enablers, barriers, costs, and benefits of Digital Twin implementation. 3. To conduct interviews with Digital Twin adopters in the construction industr y to investigate the costs and benefits of Digital Twin implementation in construction. 4. To formulate Digital Twin implementation considerations and guidance that maximizes realiz able benefits while minimizing costs for potential implementers in the construction industry. The term "Digital Twin" is used for a wide range of purposes as research on Digital T wins continues to expand in the body of knowledge related to construction. Digital Twins have been discussed in some context as a comparison to Building Information Modelling, (BIM). Some other discussions have identified the benefits that may not be direc tly applied or even feasible in a construction project. However, this ambiguity and poor understanding of the notion between a model and a digital twin may deter consumers from utilizing the technology and hence reduce the adoption of the technology as a w hole. Furthermore, as many scholars have remarked, the basis of developing a Digital Twin lies in the identification of its goals and limitations from the viewpoint of its users and implementers (Khajavi et al., 2019) . These users may include constructio n stakeholders and project executives. Although a review of the current body of knowledge reveals a few works of literature on the Digital Twin

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19 applications and implementation in construction, there is no research project that has yet assesse d the Cost Ben efit of its adoption. Involving the current implementers in the Digital Twin dialogue is critical to not just gaining a better understanding of the concept and purpose but also the cost impacts and specific benefits the technology may hold for the future o f the industry. Consequently, as the technology continues to evolve, more studies are required to gather more input from users of Digital Twins even more so for the Infrastructure Environment. To buttress the points made by (Davila Delgado & Oyedele, 2021) , more case studies that demonstrate the use of a digital twin in a construction setting are necessary to root the applications in actual scenarios. Research Structure: To achieve the objectives, this dissertation is structured as follows: the Literatur e Review which satisfies objectives 1 & 2, presents a Digital Twin definition that suits the Construction industry, and provides a review of on Digital Twin key features, benefits, some cost considerations, challenges facing its adoption, and applications across different industries. The research methodology is presented next to outline the approach undertaken to achieve each specific objective. Chapter 3 describes the methodologies and the justification to the research strategies adopted. Finally, chapter four presents the themed analysis of the discussions with Digital Twin implementers and construction industry practitioners. To address objectives 3 and 4, chapter 4 sheds light on the cost drivers of Digital Twin implementation in construction as well as on certain factors to be considered before and during Digital Twin implementation in a construction project. Conclusions and future research were

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20 presented in the last chapter. Figure 3 2 presents an overview of the research objectives. Figure 1 2 . Research o verview By evaluating and expanding on existing Digital Twin definitions, the first research question about the need to develop a comprehensive definition for Digital Twins in the construction sector was addressed. The second researc h question targeted

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21 the need to explore what features, benefits, challenges, and applications exist in the literature for various industry sectors. Through a review of a few web blogs, posts, and articles, research question 2 also attempts to investigate t he cost of building or implementing a Digital Twin for a construction project. Research Questions 3 and 4 through interviews solicit the input of practitioners and Digital Twin users on the cost benefits and guid ance that may be followed in implementing th e Digital Twin, respectively.

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22 CHAPTER 2 LITERATURE REVIEW Overview This section presents a review of digital twin literature published between 2010 to 2022 based on available original journal articles and review pape rs. This literature review was conducted using Google Scholar, Web of Science (Clarivate), and the catalog and databases of the University of Florida George Smathers Library. These sources were combined to ensure an extensive search is conducted using cert ain , and review document type returned 4,297 results between 2012 2022 for all field categories, most of which are irrelevant because retrieved results mentioned the search input but had no basic support literature on it. For the same date range and same broa d conducted. Results displayed were 436 articles, again most of which were irrelevant to narrow down the search. 73 results were returned for the same date range but once again, some of these results were irrelevant. Since the core focus of this study is on the cost implications of the digital twin implementation, another searc h was carried out with

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23 returned from this search, but none was found to be relevant as the works only plementation of digital A similar observation was made when a search using the above mentioned inputs and keywords w databases. This indicates that there is possibly no study that has focused on the cost benefits of digital twin adoption in construction and different industries. This study attempts to bridge this gap by investigating the cost implications and benefits of DT implementation in the construction industry. Table 1 presents publication categorization It can be observed from Table 2 2 that publications on Digital Twin in Construction were not seen until 2018 but have been on the increase since then. Table 2 1 . Web of Science c ategorization for search keyword = "Digital Twin in c onstruction" Construction and Engineering Categories Record Count % of 73 Engineering Civil 69 94.521 Construction Building Technology 50 68.493 Engineering Industrial 6 8.219 Energy Fuels 3 4.11 Engineering Mechanical 3 4.11 Management 2 2.74 Engineering Marine 1 1.37 Green Sustainable Science Technology 1 1.37 Mechanics 1 1.37 Transportation Science Technology 1 1.37 Total 73 100

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24 Table 2 2. Publication r esult summary for keyword = "Digital Twin in c Publication Years Record Count % of 73 2022 32 43.83 2021 24 32.87 2020 10 13.69 2019 6 8.219 2018 1 1.37 2017 0 0 2016 0 0 2015 0 0 2014 0 0 2013 0 0 2012 0 0 TOTAL 73 100 Digital Twin Definitions The concept of Digital Twin has been in existence since the early 21 st century when Michael Grieves in the year 2002 presented the idea of Product Lifecycle Management (Pires et al., 2019) he real and virtual environment, and the connection through the flow of data across the two environments (M. W. Grieves, 2005) . (Shafto et al., 2012) irtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometric level. At its optimum, any information that could be obtained from inspecting a physically manufacture Perhaps one of the most used definitions was given by the US National Aeronautics and physics, multi scale, probabilistic simul ation of a vehicle or system that uses the best

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25 available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin. It is ultra realistic and may consider one or more important and (Glaessg en & Stargel, 2012) . One definition that helped stretch the fields of Digital Twin application and transformed it into a more acceptable technology is the one given by (Ríos et al., (2015) product or counterpart of a physic (Tuegel et al., 2011) regarded Digital Twin fidelity digital models and environments for aircraft structural simulation Several other definitions of Digital Twin have been proposed although no consensus exists for most of these definitions. Although it is inexhaustive, Table 2 3 for different industries. Table 2 3. Some Digital Twin d efinitions INDUSTRY DEFINITIONS CITATION Aerospace unified system model that can coordinate architecture, mechanical, electrical, software, verification, and other discipline specific models across the system lifecycle, federating models in multiple vendor tools and configuration (Bajaj et al., 2016) Aerospace whereby models and simulations consist of as built vehicle state, as experienced loads and environments, and other vehicle specific history to enable high fidelity modeling of individual aerospace (Hochhalter et al., 2014) Aerospace system, which continually adapts to operational changes bas ed on the collected online data and information and can forecast the future of (Z. Liu et al., 2018) Manufacturing potential or actual physic al production from the micro atomic level to (Zheng et al., 2019) Manufacturing 2018) Manufacturing that represents all functional features and links with the working (Chen, 2017)

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26 Table 2 3. Continued INDUSTRY DEFINITIONS CITATION Manufacturing continually updated with the latter's performance, maintenance, and (Madni et al., 2019) Manufacturing different simulation disciplines that is characterized by the synchronization between the virtual and real system, thanks to sensed data and connected smart devices, math ematical models and real (Negri et al., 2017) Manufacturing engineering and operation data, in addition to behavior description using various simulation m (Boschert & Rosen, 2016) Manufacturing coordinate, and cooperate with the manufacturing process for (Kannan & Arunachalam, 2019) Manufacturing software services, and computational methods, which facilitates real time synchronization between a real world model (physical model) and its v irtual copy for improved monitoring to the efficiency of the (David et al., 2018) Health Care (Muskins, 2018) Built Environment digital replica of a physical (Ioannis Brilakis et al., 2019) Built Environment world system, designed to monitor, control and optimize its functionality. Through data and feedback, both simulated and real, a digital twin can develop capacities for autonomy and to learn from and (Arup, 2019) Construction reality, 2) collected data describing the experimental reality, 3) the virtual reality. The triad (physical model, big data and virtual (Angjeliu et al., 2020a) Construction time digital representation of the physical building or infrastructure. Usually, data is gathered by on site sensors that continuously monitor changes in the building and in the environment and update the BIM model with the most recent data and (European Construction Sector, 2 021) Construction the built or natural environment. What distinguishes a digital twin from (Bolton et al., 2018) Construction digital model related to either an existing, ongoing, or future construction project is created and linked throughout its lifecycle enabled through (Ammar et al., 2022)

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27 All the above definitions point out that a system must have a physical model, a digital model, and an information link between the two models to be termed a Digital Twin. This criteria for defining Digital Twin is highlighted by (Ioannis Brilakis et al., ( 2019). Certain authors have also distinguished the differences between a digital model, a digital shadow, and a digital twin (Fuller et al., 2020). It is possible for a digital model representing the physical assets to lack two way data interchange wit h the real world assets they represent. Moreover, if there is a transfer of data from the physical to the digital model in a one directional, then the digital model is c (Kritzinger et al., 2018). Digital T win D efinition for a C onstruction P roject Given the Construction industry's acknowledged requirement for a comprehensive definition of the term "Digital Twin,", this section attempts to propose a definition for the digital twin of a construction project. The justification for the suggested definition is presented in the paragraphs that follow. Although the physical asset is necessary for developing the Digital Twin of any asset as indicated in mo st definitions above, the presence of the physical asset is not a constraint on the development, usage, or insights gained from the Digital Twin of an asset (Madni et al., 2019) . Four levels of digital twin representations were described. From a description given by (Madni et al., 2019) , Level 1 does not rely on the existence of a physical twin as it serves as a conceptual model to help in making decisions regarding the layout of a potential system that would be constructed. After a physical twin has been established, a Level 2 digital twin that incorporates historical,

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28 performance, and maintenance data from the physical model can be created. An advanced level, or level 3, is an adaptive Digital Twin, which considers the user interface by learning the user's priorities and preferences in many concepts. This is accomplished by obtaining data from the physical model in real time and continually updating the digital model. Lastly, a n intelligent model created by incorporating machine learning skills linked to a greater level of autonomy by maximizing self learning capacities without human intervention is a level 4 digital twin. Although a digital twin may be created at any point in t ime, from pre construction and planning stage to post completion and maintenance, its usefulness extends throughout the whole lifecycle (Ammar et al., 2022) . In work on digital twins, they p ut forward two types of information t hat can be used in utilized in a project workflow: Project Intent Information (PII) and Project Status Information (PSI). PII refers to information about the as designed and as built state of the project and depicts the future state of a project during des ign and construction. PSI refers to information about the past state of the project and depicts the as built product and as project includes both the PII and PSI. Aimi ng to clear up any confusion around the concept of digital twins, which take into consideration all stages of a building project's lifespan, (Ammar et al., (2022) proposed that the concept of Digital Twin in construction projects requires the creation an d linking of an existing, on going, and future project throughout the lifespan of the project. The digital model is a copy of the physical project when the project is already underway or completed, and the two elements (i.e., virtual and physical) have a b i directional

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29 interaction made possible by cutting edge technologies like IoT, CPS, AI, and sensors. In the case of a future project, digital models created from previous projects feed into its implementation, producing a one directional relationship that becomes bi directional while the future project is being produced. Having stated that Digital Twin should not be termed a technology and following the edge technologies integrated to achieve a seamless bidirectional connection between the physical project and its virtual models with the goal of creating and linking existing, edge technologies will encompa ss technological devices, techniques, and most current high level IT developments capable of being integrable with available hardware and software tools used in different construction phases of a project. Digital Twin Key Features A D igital T win is essent ially a digital model of an asset, data from the asset, and a unique digital representation of the asset based on the template, and the capacity to track and observe the real world counterpart. In the case of a physical asset, much of the difficulty is in instrumenting the asset (through, say, IoT devices like sensors) such that the data collected closely corresponds to the entity being analyzed (Parmar et al., 2020). A digital template of the physical item is essential in order to construct its D igital T win. There must be some kind of calibration made to this model so that it conforms perfectly with the actual physical asset in question. After a one to one relationship

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30 between a physical asset and a digital template has been created, the asset's monitoring sensors may transmit data to the digital template to keep the Digital Twin up to date with the asset's current status. Data is provided by the physical asset to the v irtual model to generate model instances that reflect the performance, operational, maintenance, and structural health properties of the physical system. Analysis and s ituations to ensure the physical twin is fit for purpose. For example, (Madni et al., 2019) stated that instead of building multiple physical vehicles for tests, computer simulations of car braking systems can be executed to visualize the vehicle model p erformance in different real world scenarios. This can be a faster and cheaper means of testing multiple vehicles. Digital Twin as a virtual representation is easier to control, manipulate and study in a testbed environment when compared to its physical tw in in the real environment. This flexibility allows for the analysis of the system behaviors and properties in an efficient and cost effective manner. Valuable data is produced by the Digital Twin in different what if conditions. This data can be used to e nhance subsequent system designs, validate preliminary design decisions, optimize maintenance schedules, and prognosticate system responses to various field situations. (Madni et al., 2019) also identified several characteristics of Digital Twin, such as the use of historical data on a physical asset to determine preventive maintenance schedules, the use of the virtual model to monitor and understand the performance of the physical asset, and the ability to predict future performance and maintenance patte rns . (Madni et al., 2019) noted that utilizing predictive analytics data derived from the physical structure, future system performance may be anticipated after the

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31 refinement of assumptions. Furthermore, a Digital Twin optimizes and enhances services an d operations by integrating data from the IoT and the physical asset, and it may model the physical system's operational and maintenance data to account for the system's age. A Digital Twin according to (Tao, Zhang, et al., 2019) comprises five component s: the physical part, the virtual part, connections, data, and services. The virtual part since it is developed from the physical part mirrors the physical part in a controlled framework. The interaction between the virtual and physical parts enables data transfer and control. The author presented that some services such as virtual simulation, improved decision making, and control and monitoring of the physical convenience , and reliability. In order for the system's twinning to function, each physical component must have a matching virtual component, and each virtual component must have a corresponding physical component. (Jiang et al., 2021) . The goal of Building Information Modeling (BIM) is to improve the design, construction, and maintenance of a building throughout its life cycle by creating an accurate and interoperable repository of all relevant building information (Volk et al., 2014). B uilding information modeling (BIM) was created so that designers could include extra information such as a building's specifications, timeline, cost estimates, and maintenance management into a building's 3D CAD model. (i.e., 4D, 5D, and 6D) (Bryde et al., 2013) . However, building information modeling (BIM) alone provides only static data for the built environment and cannot update automatically, real time information into the models, at least not without other sources of data. A digital twin

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32 differs from BIM or a traditional Computer Aided Design model. Figure 2 1 shows some digital twin attributes and different important ways it differs from a CAD model. Figure 2 1 . Digital Twin capabilities as opposed to a CAD model (adapted from (Madni et al., 2019) Digital Twin Industry Applications Application in the Construction Industry A construction project goes through several stages as it develops. (Häkkinen et al., 2015) . These are the design and engineering phase, the construction phase, the operation, and maintenance phase, and the demolition and recovery phase. Researchers have mostly shown interest in the Digital Twin application in the design and engineering phase, and construction phase but less so in the demolition and recovery phase (Opoku et al., 2021) . Several digital means and technologies have been employed in the field of construction including Cyber Physical Systems (CPS), Building

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33 Information Modeling (BIM), laser scanning, sensor technologies, Radio Frequency Identification (RFID), web te chnology, etc. Some of these technologies when integrated effectively at different project phases hold the future of Digital Twin in the Construction Industry. D esign p hase In order to fully understand a Digital Twin, it is necessary to understand its phys ical counterpart. At the design stage, target physical projects and parts of the projects have not yet been built (Jiang et al., 2021) . To aid in the design of new projects, however, a Digital Twin of relevant current projects, and older generations of p rojects may be created using tools such as point clouds, sensor data, and design blueprints. For example, (Bansal, 2018) analyzed the spatial features of a project utilizing 3D visualization, 4D modelling, Virtual reality (VR), construction simulation and BIM. Geographic information systems (GIS), which allow simultaneous modeling of site boundaries and location based analy sis, were used to take into account the impact of site topography and existing facilities in the environment during site layout development. With proper integration of these tools and a high fidelity model, Digital Twin can be utilized at this stage to enh ance the quality and accuracy of the design. Digital Twin can also be developed for previous generations of projects with the goal of understanding new and future projects. Just like in the manufacturing industry where feedback on a product in the physical world is produced from the Digital Twin, the same can be applicable to the civil engineering or construction industry. A Digital Twin can be made for a previous building or bridge project and components and then used for research and simulation to design a new and upgraded generation of the project (Wang et al., 2020) .

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34 For an existing structure, a Digital Twin can be helpful for engineers in reconstruction, expansion, and retrofitting, which is a structural alteration made to an existing structure to re duce or eliminate the possibility of damage from hazards like flooding, earthquakes, etc. For instance, for a highway expansion project, it is possible to create a Digital Twin of an existing road utilizing point clouds, sensor data, and historical design documents in order to facilitate the design of extra lanes, shoulders, and side slopes. (Shchegolova et al., 2020) . The use of Digital Twin in the design and engineering phases of a project has been facilitated by Building Information Modelling (BIM). A digital BIM model collaboratively assembles information from different stakeholders, allowing data to be modified, added, validated, and compared with real life scenarios with the goal of improving the overall project outcome (Kaewunruen & Xu, 2018) . BIM can provide visual 3D communication for Digital Twins and although BIM adoption has experienced enormous growth across areas of technology, process, and policy, (Hardin & McCool, n.d.) pointed out that not only is the use of 3D models necessary, but als o it enables significant adjustments to both the workflow and project delivery procedures. Building information modeling (BIM) and wireless sensor networks (WSN) may be used together to create an interactive, real time model that can be of great assistance to architects, engineers, and designers throughout the design phase of building projects (Lin & Cheung, 2020) . Collected data can be saved in the Digital Twin database, enabling designers to have a complete digital footprint and to make informed decisio ns about current and future projects (Tao, Sui, et al., 2018) . With BIM enabled Digital Twin, substantial improvements may be realized in construction delivery methods, material

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35 selection, energy management, procurement, supplier selection, etc. Furtherm ore, early design decisions relating to project feasibility, sustainability issues, and energy analysis could be informed using BIM as a pre construction guide. This can further foster collaboration and effective communication among stakeholders to establi sh trust as a work. C onstruction p hase The construction phase is a significant phase that produces the finished project although it is covered briefly in the lite rature. Most of the studies that apply the Digital Twin technology in the construction stage of a project do so with an emphasis on the structural integrity of the building. For example, the Digital Twin concept as described by (Angjeliu et al., 2020b) i s used to study the integrity of a historical masonry building structural system, where it was revealed that a more in depth understanding of the structural attributes of different building components can be achieved while utilizing the Digital Twin techno logy. Digital Twin models can be continuously updated taking advantage of the insights obtained particularly in complex parts of the masonry buildings. (Macchi et al., 2018) showed that Digital Twin is relevant for producing as built drawings needed for finite element analysis of existing structures especially when the design drawings are not available. (Macchi et al., 2018) pointed out the use of Digital Twin concepts in the production of precast concrete in the industry. The authors demonstrated the i mportance of real time networking of products, processes, and systems for innovative adaptive modular construction activities. The production of these error tolerable modules was made of prefabricated malleable high performance

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36 concrete parts using the Dig ital Twin concepts (Macchi et al., 2018) . (Opoku et al., 2021) believes that D igital Twin can help reduce construction costs, improve quality, ample information about the p roject. In the management of construction activities, although various technologies like GPS for locating and measuring work done, tag identification systems for tracking material/worker location, and smart sensors for quality monitoring are being used, th ese technologies are not integrated to support various management functions (Sacks et al., 2020) . Unfortunately, most stakeholders apply reactive measures and as a result, there are deviations between actual and planned performances. For construction pro cesses to be more proactive and portray accurate status information, Digital Twin can be used in these scenarios. For monitoring and management of construction processes, computer vision and visualization techniques can be utilized to track interior constr uction progress (Jiang et al., 2021) . The timeliness of Digital Twin is consequential in construction progress monitoring and management. Comparisons may be performed between BIM as planned 3D models and as built images, and the as built construction com ponents are decomposed to automatically develop the condition of construction processes (Roh et al., 2011) . The cyber Physical system (CPS) approach can also be adopted to improve bi directional coordination across virtual models. For example, (Matthews et al., 2015) investigated the use of cloud based BIM in the real time provision of information to aid in construction progress monitoring and management. This was employed during the construction of a reinforced concrete structure based on action based research. To monitor and manage construction quality, presented that a Scan vs BIM processing

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37 system can be utilized to monitor construction status and aid in automatic and thorough quality control. Operations and m aintenance p hase In the operations and m aintenance phase, the project has been handed over to the owners or users. Virtual models used could be exact copies of the assets but has no links to the constructed project. Digital Twin models can provide bi directional communications between the physic al and the virtual parts of the project. At this operations and maintenance stage of the project, Digital Twin can be applied using collected real time data to improve the operational efficiencies in facilities management, maintenance management, monitorin g, logistics processes, and energy simulation and to provide the facility manager the opportunity to make informed decisions about the enable facilities managers to perfo (Khajavi et al., 2019) . To obtain real time occupancy data, authors have used Image Recognition in case studies to detect the movements of users in a monitored area of an office building. However, there is still a disconnection between this real life data obtained through image sensors and the BIM models (Opoku et al., 2021) according to the National Building Specification (NBS) is a dimension that includes in the model information to support the facility management and operational activities. (Kaewunrue n & Xu, 2018) presented a 6 dimensional BIM for life cycle management of a railway turnout system where they modeled the system in 3D using Revit 2018 and discovered that the 6D focuses on carbon footprint over the lifecycle of the railway

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38 system. They r eached the conclusion that Digital Twin can be used in visualizing and prioritizing maintenance options. D emolition and r ecovery s tage This seems to be the most ignored phase by researchers in terms of Digital Twin application in the industry (M. Liu et al., 2021) . Consequently, there are no papers identified to have applied Digital Twin technology to this phase of a construction projec t. However, (M. Grieves & Vickers, 2016) argued that information about the predecessor of the asset can be useful in solving same issues similar to the ones projected to be encountered since the assets may share similar attributes. One important use of D igital Twin stated by (Opoku et al., 2021) is in the protection and conservation of historic assets that may one day need to be decommissioned and demolished. To enhance collaborative information management pertaining to existing structures, some scholar s have also employed Historical Building Information Modeling (HBIM ). Authors s howed a process of designing HBIM using augmented reality (AR) and virtual reality (VR) to improve user community interest in cultural tourism. Digital Twin and HBIM when integr ated can help efficiently boost data management, identify potential hazards, technical solutions, and asset conservation. In summary, it can be observed that most literature discusses Digital Twin applications at the design and engineering phase of the pro ject with the use of BIM models. This is the phase where substantial work has been done so far regarding the applications of Digital Twin technology in the field of construction. While the manufacturing industry has experienced enormous applications and wo rks of literature, the construction industry has only seen little impact although the impact is enough to present a promise of opportunity to proactively address the challenges the industry

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39 faces. In the construction stage of a project, lots of data are ge nerated throughout the construction process. Hence, creating a Digital Twin requires a significant amount of data to be incorporated at this level to assist the operation and maintenance phase. Unlike the traditional method of construction, applying Digita l Twin in the construction phase will not only reduce cost in an effective manner but can also improve the overall quality of the finished project. In the operations and maintenance phase, researchers have identified that due to the operations being operat ed by different stakeholders, there is difficulty in the integration of data between different project lifecycles and issues connecting the physical object to the Digital Twin (Opoku et al., 2021) . Another problem is that there is not enough data on infr astructural projects because some of this data is not measurable in an actual situation. More efforts need to be employed to create opportunities for Digital Twin to be used in a wider range of available usable data. Future works are also required in the d emolition and recovery phase. In general, Digital Twin will provide an abundance of dynamic data, and present meaningful knowledge development about the physical world. This way, project stakeholders will essentially benefit from its application from the s tart and lean construction processes towards a smart project lifecycle management (Opoku et al., 2021) . Application in Other Industry Sectors D T a pplications in s mart c ities Due to the rapid development in connectivity through IoT, the use of digital tw ins in smart cities has increased dramatically from year to year. The city's traffic and transit systems, power plants, utility providers, water systems, and garbage collection

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40 systems all provide huge amounts of data (White et al., 2021) which may subsequently be used by smart cities to enhance transportation, sustainability, citizen well being, and municipal management. As the number of smart cities developed continues to increase, the more connected communities there are, and consequent ly, with this comes more digital twin use (Fuller et al., 2020) . In addition, the Digital Twin may facilitate growth by both serving as a test bed inside a virtual twin for the purposes of scenario testing and data driven environmental learning and adapt ation (Fuller et al., 2020) . The enormous gathering of data with the help of embedded sensors has continued to pave way for research targeted at the creation of advanced AI algorithms (Mohammadi et al., 2020) . Current and future smart city development may benefit from the usage of digital twin enabled technologies such as cloud based digital platforms by improving our understanding of utility distribution and consumption. Some e xamples of Cloud systems that incorporate data from many sources related to smart cities include Smart World Pro, Open Cities Planner, and Platform of Trust (Hämäläinen, 2021) . By combining graphic 3D city models, building and geospatial data, IoT devices, and other data sets, Smart World Pro may concurrently generate a virtual reproduction of the physical components of a smart city. The Smart World Pro's dashboard feature groups smart city projects into project portfolios and offers visual views for the various smart city organizations. Smart city developers may integrate data s ets like 3D models, photos, documents, and geographic and vector data using the Open Cities planner platform. Any online browser may use the Open Cities planner, which is expandable and increases the options for outlining and exploring cities from the pers pective of the street up to a larger city level. The Platform of Trust

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41 integrates data from a variety of data sources and providers. The platform of Trust is scalable and allows for data interoperability and integration from small to large scale needs (Häm äläinen, 2021) . To illustrate the evolution of smart cities (White et al., 2021) , presented a public and open digital twin model of the Docklands area in Dublin, Ireland, demonstrating how such a model can be used for urban planning of skylines and gr een space, facilitating user interaction and feedback on proposed changes. D T in m anufacturing For many years, manufacturers have always been looking for a way to efficiently track and monitor their products to save time and money. Following the industrial revolution, (Roy et al., 2020) offered a synopsis of the many stages of the manufacturing industry from Industry 1.0 to the present day Industry 4.0. The steam engine was used in Industry 1.0, and while it first appeared to be an upgrade, it was ineffic ient and time consuming. The assembly line idea was introduced by industry 2.0, which also resulted in shorter production lead times. In order to replace manpower, Industry 3.0 adopted computer integrated production. Currently, the integration of the phys ical world to virtual world (Digital Twin) in the context of digitization has given rise to industry 4.0. Digital Twin application in the manufacturing industry has successfully created opportunities for better visualization of the manufacturing processes , simulation, and optimization of production related systems including production logistics (Kritzinger et al., 2018) . It has provided an exhaustive prognosis to improve decision support systems and production planning. Recognition and assessment of meas ures relating to predictive maintenance are achievable through the Digital Twin application

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42 et al., 2017) . It also helps to recognize equipment failures and diagnose equipment remotely to proffer fixes. Digital Twin can assess and quantify unce rtainties in performance and cost using simulation based modeling and data analytics (Madni et al., 2019) . According to (Qi & Tao, 2018) , the manufacturing industry becoming more digitalized makes it easier to spot process bottlenecks and give workable alternatives, making production leaner and more competitive. Other applications of digital twin in the manufacturing sphere include system simulation for hollow gas pipeline monitoring (Zhang et al., 2017) , machine monitoring utilizing a Finite Element (FE) model of a Computer Numeric Control (CNC) machine (Scaglioni & Ferretti, 2018) . There are various strategies for applying digital twins in the manufacturing industry based on product manufacturing stages during the product lifecycle. (Qi & Tao, 2018 ) described these digital twin application categorizations in four headings as shown in Figure 2 2 below. Figure 2 2 . D igital Twin a pplication c ategorization in m anufacturing | a dapted from (Qi & Tao, 2018)

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43 D T in h ealthcare Teleoperated surgical procedures are becoming more common in healthcare, necessitating communication in both directions, which is the concept behind the Digital Twin (Madubuike et al., 2022) . Some research cites the potential for using D igital Twin techn ology in a healthcare setting. An example of this technology is a human form D igital Twin used to monitor the health status of a real person. Day to day activities and well being of the human are simulated given the effects of positive and negative lifesty le adjustments could have on this person. The Digital Twin will also provide advice on how to enhance their health in light of the forecasts. According to reports, this D igital Twin will especially be beneficial for those with diabetes. It will monitor the ir everyday activities and provide suggestions for ways to enhance their quality of life. The Digital Twin may also be used to gather comprehensive data on an individual's biological, physical, and lifestyle data over time (Roy et al., 2020) . Another pro mising area in the healthcare scene is the performance of surgical operations. The ability of doctors to perform pre surgery checks remotely through a Digital Twin will not only minimize the exposure of risks to humans but can potentially boost efficiency in the long run. This concept was also presented by (Laaki et al., 2019) in an open research where a digital twin system was developed that comprise of a robotic arm connected to an HTC Vive virtual reality (VR) system using a 4G mobile network. Furtherm ore, as identified by (Marescaux et al., 2001) , a remote surgery called cholecystectomy surgical removal of the gall bladder, was carried out in 2001 where the surgeon and patient were over 6000 km apart. This was only possible with the digital twin. H owever, the limitation of this setup was that the connection was stringently designed for a single operation and hence would require separate set ups for other operations. Also, in research published

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44 in 2020, (Croatti et al., 2020) examined the use of D i gital Twin and agents in the treatment of trauma. The goal was to enhance the recording of trauma cases and help the medical staff do their tasks prior to the arrival of the trauma patients. Another digital twin application in healthcare worth highlighting is the application developed for the 1,800 beds capacity Greenfield hospital in Singapore (W. Liu et al., 2020) . The project involved improving the supply chain logistic system through Robotic Process Integration (RPA) and SIMIO ver.9.0. The key compone nts of the digital twin framework include the RPA solution development, 3d building information, flow simulation and optimization, and scenario analyses. According to the research by (W. Liu et al., 2020) , the hospital supply chain system was optimized, which reduced building operational expenses, operational uncertainty, and tight budgetary limitations. D T in a viation The full lifespan of an airplane can be covered by Digital Twin where high fidelity models and rich data are essential conditio ns for its deployment. In aviation, its five areas of application are product manufacturing, product assembling, activities operation, maintenance, and other aviation related fields (Xiong & Wang, 2022) . Similar to digital twin use in manufacturing as a tool to allow predictive maintenance and optimize production speed, in the aviation field Digital Twin is primarily used as a means for predictive maintenance, for example, to detect hazardous changes in the structural aircraft and then to initiate self he aling mechanisms decision support, optimization, and diagnostics. The authors of (Yang et al., 2013) work presented an aviation Digital Twin that used an automated image tracking technique to gain an understanding of how steel and aluminum alloy crack ti ps deform and expand. The knowledge obtained enables the Digital Twin model to forecast sub cycle fatigue crack

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45 growth processes of aviation materials across the entire aircraft lifespan, enabling reductions in development and maintenance costs and times ( Kraft, 2016) . Similarly, by employing a finite element model of an aircraft wing with shape memory alloy particles inserted in crucial areas of an aircraft structure, (Bielefeldt et al., 2016) presented a Digital Twin for detecting fatigue cracks. Simu lations of the reaction of the localized particles in the critical sections of the aircraft wing subjected to loads encountered during flight were created in order to detect structurally unsafe alterations. Some cutting edge aircraft Digital Twins evaluate guided wave responses to carry out real time damage predictions in aircrafts (Seshadri & Krishnamurthy, 2017) . The signal weakens in some directions and reflects in others when the directed wave meets with damage. As a result, the signal responses from damaged and undamaged structures exhibit different signal amplitudes and phase changes. By analyzing the cumulative signal responses at multiple pre selected sensor sites using a genetic algorithm, damage detection and evaluation can accurately estimate da mage size, position, and orientation (Barricelli et al., 2019) . The work of (Zakrajsek & Mall, 2017) proposed a Digital Twin model (DTw) for a specific tire of an aircraft at touchdown. High fidelity testing data was used to develop the proposed DTw, w ith the aim of improving tire touchdown wear prediction in order to reduce the likelihood of tire flat spots and accidents, both of which could have a significant impact on the overall cost of the program and the aircraft's impact on the environment. By fa ctoring in the probability of failure (POF) for various distributions of landing characteristics such as yaw angle, sink rate, touchdown speed, and tire's state (new or old), the generated DTw guides the

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46 various landing parameters. This is because flat tir es are primarily caused by non ideal touchdowns (generating spin up) during landings (Zakrajsek & Mall, 2017) . With the further development of technology, we may expect to see the capabilities and applications of Digital Twins spread to new fields. Indus try sectors where digital twins may be used to boost productivity and efficiency are summarized in Figure 2 3. There are reports of applications in Supply Chain Management, Retail, Government, Telecommunications, Luxury Goods, Architecture, etc. Figure 2 3 . Various Digital Twin a pplication s ectors Enabling Technologies of Digital Twin The 5 dimension model, as discussed by (Qi et al., 2021a) , disclosed that several enabling technologies are needed to enable various DT modules (i.e., physical entity, virtual model, DT data, smart service, and connection). A complete

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47 understanding of the physical asset is essential for the Digital Twin. Variou s modeling tools are crucial for the virtual model. For the purpose of monitoring physical assets and processes in real time, visualization tools are also essential. The efficacy of Digital Twin is directly affected by the accuracy of the virtual models. A s a result, the models need to be improved and verified by verification, validation, and accreditation (VV&A) technologies. Finally, Digital Twin's physical entity, virtual model, data, and service are linked together to allow for communication and the exc hange of information. Internet technologies, interaction technologies, cybersecurity technologies, interface technologies, communication protocols, etc. are all utilized in the connection. A framework for digital twin enabling technologies outlined by (Qi et al., 2021a) is shown in Figure 2 4 below. Figure 2 4 . Digital Twin e nabling t echnologies f ramework | r emodeled from (Qi et al., 2021a)

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48 The growing interest in digital twin technology was significantly driven by technologies such as the Internet of Things (IoT), Data Analytics, Extended Design (XR)/Virtual and Modelling Technologies, Cloud Computing, and Artificial Intelligence . The i nternet of t hings (IoT) (Ashton, 2010) . The term was given to describe the connection of devices to the internet to give the developer the ability to monitor and track almost everything we do, resulting in a sm arter world. The (IoT) is "an infrastructure comprised of connected systems, systems, people, and information resources, coupled with intelligent services that allow them to manage and react to information from both the real world and the virtual world" (I SO, 2018). Every aspect of human life in terms of communication, health, construction, transportation, etc. benefits from the spread of these IoT devices [Curry, 2017]. It has a huge impact on emerging concepts such as smart construction and smart cities. Since D igital T wins are based on the automatic and bidirectional exchange of data between physical objects and their digital counterparts, IoT is an essential block in the process of developing D igital Twin applications. IoT and the Digital Twin when linke d can bring about comprehending how the performance of the physical twin in the operational environment (Uhlemann et al., 2017) . As a result, it will be easier to do predictive maintenance and optimize the physical system using analytics and artificial i ntelligence, leading to more efficient operations and even brand new business models. In manufacturing for example, multi sensor information like moisture content, temperature, production status, etc. can be sent to the Digital Twin to obtain real time con ditions of the physical twin to facilitate predictive modeling. Furthermore, linking Digital Twin and IoT can provide insights for a

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49 company into how a product is managed by customers enabling users to optimize maintenance schedules, resource utilization, and failure/fault detection (Madni et al., 2019) . Simply put, Internet of Things (IoT) sensors allow Digital Twins to be in sync with the status of physical assets by detecting one or more conditions in physical assets, converting those conditions into signals that machines and people may understand, and establishing an online connection with other devices. A wide variety of Internet of Things (IoT) sensors, including global positioning system (GPS) devices, image sensors, proximity detectors, radio freq uency identification detectors, motion detectors, and biosensors, are widely used in construction (Lee & Lee, 2021) . Sensors are one of the essential tools that Digital Twin to IoT connection. Without sensors, much of the information from the physical wo rld would not be captured digitally. Sensors provide access to information about components and resources in the physical systems. In the construction industry, they can provide data about facilities being constructed as well as processes employed througho ut the lifecycle of the constructed building. Global Positioning System (GPS), Radio Frequency Identification (RFID), Inertial measurement Units (IMUs), bar codes, cameras and laser scanners are some of the sensing technologies adopted in construction (Aka nmu et al., 2021a) . Data a nalytics The phrase "data analytics" was coined in what is now known as the interdisciplinary discipline of "data science," which places a focus on gathering and presenting data for the purposes of analysis. (Fuller et al., 202 0). The need for raw data is important. Data lifecycle, as pointed out by (Tao, Qi, et al., 2018) includes data

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50 collection, data transmission, data storage, data processing, data fusion, and data visualization. Sources of data include software, hardwar e, and network (Qi & Tao, 2018) . Information on software may be gathered by means of open database interfaces and APIs. Hardware data can be dynamic status data or static attribute data and instruments may include barcodes, QR codes, radio frequency ide ntifiers, cameras, sensors, and other IoT technologies (Qi et al., 2021b) . Data can be transmitted through wire transmission and wireless transmission technologies, and both depend on the access methods, channel multiplex modulation and coding, transmis sion protocols, multi access schemes, and multi user detection technologies (Qi et al., 2021b) . The essence of storing collected data is for further processing, analysis, and management. Database technologies enable data storage, however, due to the hete rogeneity and rise in the volume of multisource DT data, traditional databases at some point may no longer be sufficient. Attention is now being drawn to big data storage technologies like distributed file storage (DFS), cloud storage, and SQL databases (Q i et al., 2021b) . Essentially, data processing is the act of gleaning information from a trove of raw, unorganized data. The relevant data processing technologies can essentially clean, compress, smoothen, reduce or transform these fuzzy data (Qi et al., 2021b) . Extended r eality (XR), v irtual d esign and m odeling t echnologies The construction industry has employed the use of different modelling technologies and virtual design tools e.g., BIM , VR, AR, and other gaming engines (Akanmu et al., 2021b) . These tools are being used to create virtual models of

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51 structures and simulate construction activities. The outstanding benefit of BIM is not only its ability to present a 3 dimensional representation of the physical world, but it also stores a large amount of data which helps all involved parties have a close to an accurate idea of the project cost, schedule, timeline, etc. Augmented reality concepts have also been used to achieve close to an accurate representation of the physical environment in certain situations. Some of the popular modelling tools designed to prepare and simulate these models are REVIT, ARCHICAD, SYNCHRO, LUMION, UNITY, etc. Cloud c omputing Cloud computing involves providing hosted, on demand services through the Internet. This innovation makes it possible t o save and retrieve data quickly and easily online. Data processing and storing data in the cloud is made available to Digital Twins because of cloud computing, enabling massive amounts of data to be accessible whenever needed regardless of their physical location. Since Digital Twins are hosted on the cloud, it is possible to store and process massive volumes of data without incurring the usual storage and processing costs. Artificial i ntelligence Artificial intelligence (AI) is a branch of computer science that aims to recreate the cognitive processes that underpin human intelligence in order to build machines with social intelligence. With AI, computers and machines can see, understand, and transl ate written and spoken languages, recognize images and signs, process data, and even proffer solutions to problems. Robotics, image recognition, and language recognition are all areas of research within the field of artificial intelligence. Using techniqu es like Deep Learning, Machine Learning, Neural Networks, and expert

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52 systems, AI can provide Digital Twins with a powerful analytical tool that can automatically sift through data, identify patterns, draw conclusions, and provide solutions. Benefits of Dig ital Twins for the Built Environment The Institution of Engineering and Technology (theiet.org, 2022) , in their publication, noted that Digital Twin is a crucial tool for all businesses operating in the engineering sector, as it may bring about significant gains that would have been impossible to get via more conventional means of project delivery. Despite the remarkable benefits, Digital Twin technology is still underutilized in the construction industry for varying reasons including cost uncert ainties. As seen in the literature, several organizations are taking advantage of the value digital twins can provide. EY (Ernst & Young Global Limited), a UK organization is using digital twins in their supply chain business network. Connecting data from numerous sources and systems throughout a supply chain (such as Internet of Things sensors and GPS signals, for example) yields a digital duplicate with the same supply characteristics, entities, and financial aims as the original (EY analysis, 2021) . Fr om their analysis shown in Figure 2 5, a digital twin can provide tangible benefits including a 10% 30% reduction in annual sales, general and administrative (SG&A) expenses, a one time 5% 10% improvement in inventory visibility, and throughput, and a 1% 2% annual revenue growth. operating costs by up to 35%, reduce carbon emissions, improve user experiences, and create a healthier workplace.

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53 Figure 2 5 . Realized tangible benefits of D igital Twin to EY company's P&L | source: (EY analysis, 2021) (Nicholas D. Evans, 2022) outlined the ways in which the built environment might benefit from the usage of Digital Twin to create value and justify investment. These cases cover a described r ange from Maintenance and Operations, to sustainability, energy efficiency, Security and safety, to predictive maintenance and data monetization. The author opined that cost optimization is only but one aspect of the business value that Digital Twin can un lock, identifying that these value added services are key to the quantification of digital twin monetization. As shown in Figure 2 6, these value added benefits were divided into four categories: Building Operations and Maintenance, Sustainability and Ener gy Efficiency, Safety and Security, and Predictive Analytics and Data Monetization.

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54 Figure 2 6 . DT Value added services for b uildings and a sset operations, adapted from (Nicholas D. Evans, 2022) Following the identified value added benefits resulting from digital twin adoption in building and infrastructure, the work of (Nicholas D. Evans, 2022) described some use cases where building owners and facility managers can take advantage of the technology. 1. Building Operations and Maintenance: The literat ure pointed out that building managers, by observing occupant movement patterns and density, can determine where and how tenants move. Besides the advantage of assistance in booking reservations of meeting rooms and parking areas, departmental planning, st aff relations, and logistics all stand to gain from the

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55 building's "Digital Twin". For the occupant, a digital twin may serve as a portal for access to all tenant services. Equipment can be maintained and tracked easily enabled by a uniquely positioned dig ital twin. All activities and essential data can be time stamped and stored respectively as such data is invaluable for predictive maintenance. Just like in the aerospace industry, building parts can be tracked and inspected using blockchain technology. In parking garages, parking operators can optimize their operations by integrating garage cameras, access systems, parking space sensors, and Electric Vehicles (EV) charging stations, connecting these systems to the digital twin to improve safety while measu ring the utilization of parking spaces. Another example where digital twin adds value is in advance data center deployments. Micro data centers, server rooms, and data storage racks can be maintained and monitored using digital twins. 2. Sustainability and En ergy Efficiency: Operators of buildings may examine their energy use with the help of a digital twin in order to achieve greater levels of energy efficiency and flexibility. For example, the digital twin may be used to make apparent the presence of a moni toring and actuation system for a virtual power plant that employs sophisticated algorithms. Acquisitions, inventions, and renovation projects all need funds, and digital twins may KPIs and twin. 3. Safety and Security: For operational safety, digital twins can provide improved insight during emergencies, making evacuation plans more effective using live occupancy data. It can

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56 also security and monitoring which includes scanning vehicles at parking garage entries, detecti ng weapons, and enforcing other security measures. If the smart two factor identification and encryption solutions are integrated into a building, digital twin can enable the building operator to perform real time access monitoring, bringing the next level of security and transparency for databases. 4. Predictive Analytics and Data Monetization: Digital twins of the building can be used to do computational what if analysis based on physics models to simulate crowd flow, airflow, and disease transmission analy ses. Artificial intelligence enhanced camera feeds can be used to support operational safety and security. These enhanced camera systems can help identify hazards, provide people with movement data, etc. all easily accessible through the digital twin. Also , architects can better design or redesign buildings using digital twin collected data from building operations and asset utilization. (Lengthorn Paul, 2020) identified some benefits of digital twin use for building and infrastructure, emphasizing that t hese gains are a result of some form of improved information (data) management. Digital Twin provides a information about the system and the built environment can be accessed continuously, enabling rehearsal of maintenance ac tivities to reduce downtime, understand performance improvement, and increase certainty and safety. It makes it easier to plan preventative maintenance and reduce the number of reactive activities, thereby saving costs. With a digital twin, changes to buil dings or infrastructure can be assessed and planned with greater certainty because of improved quality of information. In the article by (Barnard Lucy & Lescohier Jenny, 2021) , Swinerton, a construction firm base in San Francisco shows what Digital Twin can realistically do for

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57 construction. The author asserts that Swinerton is making use of Digital Twin to cut down on the time and money spent by employees, customers, and other stakeholders on trips to job sites (by as much as 50%). By creating digital t wins of projects at every stage, the organization can accurately measure spaces and rooms from afar and remotely monitor building progress with realistic precision. One of the Assistant project managers said that digital twins are far more helpful than 2D drawings and plans for seeing how spaces come together. These managers basically assist customers in navigating Digital Twins of physical locations, saving them time and money by eliminating the need for customers to physically visit the site. Through its integration with Autodesk BIM Collaborate, the digital twin has also helped project teams communicate more effectively with one another and with stakeholders, allowing them to resolve issues remotely, raise and track Requests for Information (RFIs), and s peed up overall project timelines, all of which contribute to cost savings and expedited payment. Digital Twin redefines efficiency and productivity for AEC industry. This was pointed out by (Barnard Lucy & Lescohier Jenny, 2021) ur photorealistic, dimensionally accurate digital twins, users can organize, analyze, and store critical information regarding a building site throughout the entirety of its life cycle ess strategy at Matterport. This frees up time that would have otherwise been spent on site documentation and coordination, allowing experts like Swinerton to focus on greater return on investment (ROI) activities like speeding up building timetables. (Ammar et al., 2022) i dentified 40 digital twin applications which were summarized under seven (7) themes to demonstrate what Digital Twin can do. The

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58 themes or identifiable benefits were coded to correspond to the generally acceptable Construction Speci fications Institute (CSI) divisions. The themes included Real time monitoring, Increased information transparency, Advanced preventive measures, Better stakeholder collaboration, Higher accuracy realization, Analysis and feedback, Advanced what if scenario analysis and simulations, and Real time tracking. Challenges to Digital Twin Adoption Since Digital Twin requires reliable access to data, real time connection and variabi lity in characteristics (Pires et al., 2019) . As Digital Twin run in parallel with IoT technologies, they may arguably share similar challenges. Most of these challenges have to do with data analytics and the Internet of Things, and the first step in tac kling these problems is to identify them (Fuller et al., 2020) . It has been identified that one big challenge with data analytics currently is the cost of running data infrastructure systems. For Instance, the cost of running a high performance graphics processing unit (GPUs) capable of running ML algorithms can cost anything between $1,000 to $10,000. The system also needs regular software and hardware updates for successful operation. Although Amazon, Google, Microsoft, NVIDIA, etc. are providing on dem and GPUs through the cloud, the poor infrastructure and high cost are still challenging for data analytics (Fuller et al., 2020) . Also identified as one of the challenges found in the field of the Internet of Things and Industrial Internet of Things (II oT) is the data privacy, security, and trust concerns. Accompanied by the rapid growth of IoT devices, is the challenge of collecting large volumes of data. This challenge becomes even more problematic with the advent of big

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59 data (Fuller et al., 2020) . A s some of this data could be sensitive customer data as in the case of businesses, it could be a target for criminals with intention of taking control where nearly 15 m illion IoT devices worldwide were compromised and used to launch a distributed denial of service attack (Vishwakarma & Jain, 2020) . Other challenges are highlighted in other literature regarding the adoption of Digital Twin. These challenges were grouped into three categories: virtual modeling, company structure organization, and real time data and synchronization 2017) . There is a need for validation of virtual models against the real processes on which they are based. A major challenge l ies with the lack of existing methodologies and techniques for validating the virtual models which are causing the process to become difficult and time consuming. The Author also stated that with the creation of Artificial Intelligence based models, there is a problem with the integration of the available modeling techniques with the AI. It is believed that the use of AI can trigger a drastic improvement in the creation of virtual models by making them more adaptive. There is a digitalization problem associ ated with digital twin implementation in AEC projects (Lu & Brilakis, 2019) . There is difficulty in digitalizing all works in every phase of the project: design, construction, operation and maintenance, and demolition phases. As digital twin requires a t remendous amount of data and full digitization of work processes, the dynamic nature of the processes and job site tasks makes it challenging to achieve. Another issue with adopting Digital Twin has to do with the company structure. As the Digital twin has emerged as a technology that holds great promise for AEC

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60 organizations, implementing it depends not only on the technical ability but also on the structure of the organization. It has been reported that the partitioned nature of the organizations makes it difficult to access all the knowledge from different departments . Suggestions were made to have a unified organizational structure that encourages the exchange of knowledge from sections that are more equipped with digital engineerin g skills. Digital twin implementation not only depends on technical know how but also on a construction industry environment that supports training, standards, skills, and management. Figure 2 7 shows some of the currently identified challenges of implemen ting Digital Twin in construction and the built environment. Figure 2 7 . Digital Twin i mplementation c hallenges in c onstruction The Cost of D igital Twin Implementation Cost, like in any business is always a crucial consideration when implementing any new technology. As the concept of representing physical entities virtually is being embraced by most organizations due to the value they bring, it is crucial to assess the

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61 p ossible cost benefits of using the Digital Twin to establish a framework that has the potential of improving the construction industry technology adoption. As expected, the cost estimate for the application of Digital Twin could a direct factor of the scal e, the scope, and the purpose of a project (West & Blackburn, 2017) . Depending on the complexity of the asset or project, the cost of developing a virtual model varies. An analysis by (West & Blackburn, 2017) was performed using a Cost Constructive Mod el (COCOMO) II based approach to roughly estimate the scope, cost, and time required to develop a robust Digital Thread/Digital Twin model for the Next Generation Air Dominance (NGAD) aircraft. Their results, albeit subject to large uncertainties, showed t hat the cost of Digital Thread/Digital Twin makes the concept impractical to fully implement as the Digital Twin development and sustainability could cost between $1 and $2 trillion roughly equivalent to the combined Air force/Navy RDT&E budget for FY17. This is not to mention the effort and timeline it would require for completion. In a web blog of the Digital Transforms Physical by (McMahon Colin, 2022), most executives and organizations are adamant about the Digital Twin adoption mainly because the i mplementation is not cheap. It was emphasized that a Digital Twin for industrial companies costs at least one million USD to implement and oftentimes, goes well beyond that. It takes more effort to deploy a digital twin than just buying and installing soft ware. In part because, even though Digital Twin are products, it is advantageous to see them as ecosystems because each twin necessitates the coexistence of physical and virtual components. When it comes to Digital Twin, many businesses face problems with data sourcing or a lack of data standardization. A company should consider "How accessible is my data?" before deciding on a digital twin

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62 strategy. The upfront cost of deploying a digital twin includes spending money on an accessible data infrastructure, w hich should never be underestimated. Again, the digital to maximizing revenue and productivity in any implementation scenario. Another web blog from SpaceIQ also iden tified that costs associated with a Digital Twin are significantly a function of the infrastructure required to generate the data needed to build one (Maresco, 2022) . The article listed some key considerations in developing a digital twin from a cost vie wpoint. Depending on the firm and the complexity of the twin, a digital twin's real construction cost will vary. To clearly comprehend investment costs, businesses should look at the cost of architecting such a system and split it down into the individual costs associated with the software, IoT hardware, and training (Maresco, 2022) . These associated costs are briefly discussed below: DT s oftware The Digital Twin software is simply a bridge between data from the various connected devices in your IoT and the processes that use them (Clifton, 2022) . The software is connected in such a way that the software pulls data from the physical asset in real time. While it is possible to license the software, it is the most essential upfront cost since it powers the Digital Twin itself. D T hardware The cost of building or implementing a Digital Twin is also a significant function of the IoT hardware such as s ensors that stream this huge amount of data. From motion sensors to temperature sensors, floor sensors to proximity beacons, the data offered by

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63 a well connected IoT has a tremendous impact on the cost of assembling a Digital Twin. It also comes with an in creased expense as the need for more complex software grows. Education and t raining Depending on the complexity, Digital Twin requires significant education and management, upfront training and onboarding, and continuous personnel training as technologies evolve. In a LinkedIn post, (Roest, 2021) identified that asset owners would have to consider the cost of hiring a specialized IT company if the Digital Twin is to be integrated into their production process or logistical process. Typically, a Digital Tw in would be a part of an IoT framework, all of which adhere to some external standards for data collection. Implementing such an IoT framework requires the services of a professional service provider who integrates the models and algorithms that are the co re of the digital twin functionality. The author of the post also discussed the D igital T be the developers of a digital twin for their product. For them, the costs depend on the lev el of complexity that is needed. There are many environments like MATLAB Simulink or Modelica that let you build simulation models relatively easily. Sometimes, the model can be entirely data driven, and no explicit modelling is involved. In that case, the entire toolset from machine learning is available to train a model. But if the behavior is more complex, then you will probably need simulation engineers to do the work and it will probably cost more. In general, you would be looking at an effort of a few persons for months or even a few years if some basic research is involved to understand what is going on. In that case, however, the effort is probably not only aimed at building the D igital T win as such, but rather at enlarging your understanding of the processes that

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64 are relevant to you. A D igital T win is then only a form in which this knowledge is (Roest, 2021) also pointed out that provided that the structure of the physical twin remains the same, the maintenance of a D igital T win will perhaps be a lot cheap in comparison with its physical counterpart. If the physical asset is stable, the cost of maintenance will be low for a well built D igital T win although it should still be accounted for as it will be a recurring cost for many years to come. If the physical asset is unstable, then the cost of your D igital T win can be very significant. It is important to note that these costs described above are generic and analysis should be done depending on the context by each D igital T win user or i mplementer. Digital Twin Costs for a Building Project For the Built Environment, there are limited cases of Digital Twin implementation, most of which are either experimental, depend on new technology development, or have inconsistent function and scope making them difficult to compare. A Digital Twin application in the infrastructure or built environment will usually be an expansion of the building information modelling (BIM ) and Geographic Information System (GIS) with additional data integration and information management methodologies. (Lengthorn Paul, 2020) worked up some theoretical cost models to provide an order of cost based on building areas for different building types (Table 2 4). From Table 2 4 below and based on the literature, the cost of procuring a Digital Twin is dependent on factors like building type and size, the complexity of building systems as well as the scale of the structure. (Lengthorn Paul, 2020) suggests that by

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65 developing and running a Digital Twin, we have the potential to save up to 18% on maintenance costs just by moving to more data driven predictive maintenance. Table 2 4. Theoretical c ost model of Digital Twin for different building type s, adapted from (Lengthorn Paul, 2020) Building type COST ESTIMATES (including Client Team COSTS) DT ESTIMATED BUDGET Typical grade A commercial office building (size: 600,000 ft 2 ) Consultancy cost: $1.3 $1.8 per ft 2 Overall budget allowed: $1.9 $2.7 per ft 2 Assumed fixed cost of software and system integration: $300,000 inclusive Between $1.2m $1.7m Typical high school, secondary school, training facility (size: 300,000 ft 2 ) Consultancy cost: $0.5 $0.7 per ft 2 Overall budget allowed: $1 $1.3 per ft 2 Assumed fixed cost of software and system integration: $150,000 inclusive Between $290,000 $410,000 A group of typical university buildings (size: 1,000,000 ft 2 ) Consultancy cost: $0.4 $0.9 per ft 2 Overall budget allowed: $1.4 $1.9 per ft 2 Assumed fixed cost of software and system integration: $300,000 inclusive Between $1.4m $2m Typical general hospital building (size: 2,100,000 ft 2 ) Consultancy cost: $1 $1.5 per ft 2 Overall budget allowed: $1.4 $2 per ft 2 Assumed fixed cost of software and system integration: $300,000 inclusive Between $3m $4.2m A typical large high tech distribution center (size: 300,000 ft 2 ) Consultancy cost: $0.4 $0.6 per ft 2 Overall budget allowed: $0.9 $1.2 per ft 2 Assumed fixed cost of software and system integration: $150,000 inclusive Between $300,000 $400,000 A t ypical high tech factory or laboratory (size: 300,000 ft 2 ) Consultancy cost: $0.8 $1.2 per ft 2 The overall budget allowed: $1.7 $2.4 per ft 2 Assumed fixed cost of software and system integration: $300,000 inclusive Between $600,000 $800,000 A typical large shopping mall (size: 3,000,000 ft 2 ) Consultancy cost: $1 $1.4 per ft 2 The overall budget allowed: $1.3 $1.9 per ft 2 Assumed fixed cost of software and system integration: $150,000 inclusive Between $4m $5.6m

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66 CHAPTER 3 METHODOLOGY In this section, we discuss the methodological approach utilized to carry out the research. Every research project must take research methodology into account since it determines how the study's goals will be attained. According to Ro bson (2011), methodology refers to the theoretical, political, and philosophical foundations of social research as well as the applications of research techniques and their consequences for research practice. Methodologies outline the steps that need be ta ken for an inquiry to move forward, including identifying issues that merit investigation, structuring the problems to allow for their study, developing proper data creation, and lastly connecting the problem with the data obtained, analysis, and conclusio ns/inferences reached logically (Jackson et al., 2007) . This chapter presents the different methods available for conducting research, the chosen methodology by the investigator, as well as the justification for the adopted methodology for invest igat ing the various topics relevant for the completion of this study. Types of Research Methodologies Quantitative Analysis To get the desired outcome, the quantitative approach employs numerical or quantified data, or codes for data analysis. Quantitative research collects numerical data and uses mathematical techniques like statistics to analyze the data to understand issues or phenomena (Aliaga & Gunderson, 2002). Quantitative approaches are used to determine if a theory's prediction generalization is accurate. Usually, polls, questionnaires, experiments, and surveys are all examples of sources of data for

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67 quantitative resear ch. Additionally, compared to the qualitative technique, quantitative research frequently necessitates the collection of a significant number of datasets. Qualitative Analysis Unlike the quantitative approach, the Qualitative technique involves gathering unstructured data, analyzing text or pictures, representing data in tables and figures, and allowing for the presentation of individualized interpretations of findings (Creswell David, 2009) . Typically, qualitative research is done to gain a thorough kno wledge of human behavior and the underlying causes, beliefs, and motives behind it (Silverman, 2006) . There are several kinds of qualitative research instruments. A suitable qualitative research tool would be chosen based on a set of predetermined object ives. In line with the stated aims and objectives of this study, Extensive Literature reviews and Interviews were identified as relevant tools for the completion of this study. Triangulation/Mixed Method approach Because no one study or approach is univers ally acceptable for all research problems, it is sometimes suggested that combining qualitative and quantitative methodologies would improve the integrity and trustworthiness of results (Jick, 1979) . This Mixed method also known as Hybrid research or Tri angulation research method, is a combined methodology. While relying solely on one method may skew or distort the researcher's perception of the specific aspect of reality under investigation, the triangulation method builds on the benefits and eliminates the drawbacks of a single method for a more thorough assessment and understanding of the research problem.

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68 Cost s and Benefit s Investigation Merriam webster dictionary define costs as an outlay or expenditure (as of effort or sacrifice or payment ) made to achieve an object ive or acquire an asset. However, t he term "costs" is also used to describe the negative effects of a plan as well as the monetary and other outlays that were necessary to implement it. Benefits encompass not just the good results that occ ur from doing it, but also the bad results that are avoided. Proof of outcomes that have been accomplished is typically simpler to get than evidence of avoided outcomes. Some evaluations also include benefits, additional resources leveraged if these are th en eventually used. A summary of the types of costs and benefits is shown in Figure 3 1. Figure 3 1 . Types of b enefits and c osts for projects | adapted from (Rogers et al., 2008) As pointed out by (Rogers et al., 2008) , the viewpoint from which the costs and benefits of a project are analyzed is another important factor to take into account. To a funding organization, for example, volunteers' time may be seen as a benefit since it increases the overall amount of resourc es available to the project, but to the volunteers themselves, it may be seen as a cost because their time is now unavailable for use on another endeavor.

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69 Literature Review A literature review is an investigation of prior research to comprehend the conte xt of a study, identify difficulties, track advancements, and offer guidance on generating answers (Snyder, 2019) . It is also referred to as a theoretical framework or research background. The fundamental goal of a literature review is to help researcher s gain the necessary understanding of all relevant research on a subject and to pinpoint the study's strengths and limitations. Since literature review serves as the foundation for the planned study, it is important to choose pertinent material in a method ical manner that assures the resources are current and trustworthy. There are different types of literature reviews as identified by various authors. Integrated reviews or Narrative Reviews or Standard/Traditional reviews analyze and summarizes the pertine nt literature on a certain subject. A research technique called systematic review identifies and critically evaluates relevant literature by gathering and analyzing data from the literature. This systematic review type may also be referred to as best evide nce syntheses or practice based research syntheses (Dunst, 2009), depending on the application (Liberati et al., 2009) . The goal of the systematic review according to (Snyder, 2019) , is to locate all empirical data that meets the pre established inclus ion criteria to answer a certain research question or hypothesis. Quantitative systematic reviews, also known as meta analytic reviews, offer a statistical way of assessing the impact and magnitude of the combined studies that are important to the research question (Sun et al., 2010) . This study uses the "systematic review technique" to critically evaluate numerous studies within a research topic and utilize this to solve the primary research problem of this study.

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70 Interviews Another qualitative research tool used to gather data from a chosen demographic or individual is Interviews. According to Trigueros (2017), an interview is a qualitative research method that comprises asking chosen study participants questions and getting their answers. There are spec ific circumstances where interviewing strategy is commonly used. For example, according to (Easwaramoorthy & Zarinpoush, 2006) , this strategy should be used when the study calls for in depth knowledge of people's ideas, beliefs, experiences, and feelings . Structured, semi structured, and unstructured interviews are some of the many interview patterns used in data collection. A structured interview requires the interviewer to pose a preset sequence and a standard set of questions on a certain subject. With this approach, the respondent would give responses based on a list of options the interviewer had prepared. In a semi structured interview, the interviewer must pose predefined questions while the respondents offer their own explanations for the responses . An unstructured interview does not call for the interviewer to have any sort of established standards or criteria. Nevertheless, the interviewer uses a wide question that enables the respondents to give answers in the course of an unstructured dialogue. Some other interviewing techniques include in depth interviews, clinical interviews, face to face interviews, and history, tales, and life stories (Easwaramoorthy & Zarinpoush, 2006) . In depth interviews call for the interviewer to examine the population (individually or in groups) and openly express thoughts, drives, and emotions on the subject at hand (Oxman, 1998). For clinical interviews, the goal is to deepen understanding and influence the interviewee's way of thinking (Sewell, 2016). A type of in d epth interview aimed at obtaining specific impressions, opinions, and attitudes is the

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71 face to face interview. The other types of the interview include in depth interview which requires the interviewer to study the population (individually or in groups), t hus, freely expressing idea, motivations, and feelings about the concerned topic. The Clinical interview tries to increase understanding and cause a change in the line of thoughts of the person being interviewed (Jacobs et al., 2016) . The face to face in terview is a version of an in depth interview targeted at detailed perceptions, opinions, and attitudes (Easwaramoorthy & Zarinpoush, 2006) . History, tales, and life stories are social science techniques where a person informs on his or her life, surroun dings, environment, education, job, social status, religion, beliefs, history, and specific social aspects related to his or her life either directly or indirectly. Methods Adopted and Justifications: Quantitative cost benefit assessments attach monetary value to identified benefits and costs of an endeavor . However, attaching monetary values to mos t Digital Twin identified benefits presents further challenges. Monetizing many of the benefits, for example, improved collaboration as a result of Di gital Twin adoption seems impossible. There are also challenges regarding organization unwillingness to disclose financial records or sensitive data to external persons for security reasons. For these reasons, this research study adopts a qualitative cost benefit assessment approach especially as the D igital T win concept is still at its early stages. The tools adopted include literature search to identify what currently exists in terms of costs and benefits. I nterviews were then conducted with industry practitioners and Digital Twin implementers with focus on their specific use cases in the construction

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72 environment . For this study, we adopted the semi structured interview approach to obtain the relevant responses needed to address some of t he objectives of this study. This method was adopted because the open ended form of questioning offered by semi structured interview approach gave the interviewees the autonomy to narrate personal experiences, beliefs, and thoughts that explore relevant id eas indirectly related to the discussed research objective. Using a structured interviewing method would have limited the flexibility and scope in responses thereby creating little opportunity for respondents to be more detailed. The researcher also design ed a questionnaire as an alternative for respondents that may not be available for interview. However, all eight respondents provided responses through interview sessions, making the questionnaire redundant.

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73 CHAPTER 4 I NTERVIEW WITH DIGITAL TWIN IMPLEMENTE R S Overview This chapter discusse s finding s from interview s conducted with 8 industry professionals who are implementing Digital Twins and have gained experience working with Digital Twin in a construction related environment. Before presenting the findings from the interview, t his chapter first describes the goal of the interview, how interviewees were selected , the interview questionnaire design as well as data analysis after obtaining relevant information from these practitioners. I nterview Objective The primary objective of the interview was to explore the views of industry practitioners who have applied or are currently applying Digital Twin in their respective workspaces with the goal of gaining insights into the cost and benefit s associated with Digital Twin implementation (Research Objective 3). The semi structured interview approach adopted by the researcher was helpful in keeping the questions open ended in nature , a l lowing the researcher to follow them up with probe questions that further scrutinizes their responses. These interviews were exploratory in nature with the view of gaining high level insight into the costs and benefits associated with the implementation of Digital Twins, possible guidelines for potential implemente rs as well as other subtle considerations which may not be readily identifiable by Literature. Choice and S election of Interviewees implications of Digital Twin implementation, in terviews were conducted with 8 industry

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74 professionals. Target respondents were industry professionals who have particularly gained experienced in construction and are currently using or have used Digital Twins well enough to provide relevant information fo r this research. For the interview, a combination of two methods were used in selecting the respondents: Convenience sampling and Linear snowball sampling. Convenience sampling is a type of sampling method that allows the researcher to subjectively make s election at random among people who are happy to contribute to the research. Linear snowball approach allowed the participants to make referrals to other subjects who also represent the targeted study sample because they possess sufficient construction in dustry and Digital Twin use experience. These interviewees possessed construction experience s ranging from 4 to 24 years . Table 4 1 shows the profiles of the interview ees each coded using DTU denoting Digital Twin User. Table 4 1. Interviewee p rofiles Interviewee Organization Type Position Construction Exp. (in years) Discussion duration DTU1 Software agnostic system integrator for Buildings Chief Executive Officer 9 75 minutes DTU2 General Contractor Project Manager (VDC) 24 45 minutes DTU3 Architecture, Visualization, and software developer President/CEO 4 90 minutes DTU4 Engineering and Infrastructure Technologies Company Vice President, Digital Twin 6.5 55 minutes DTU5 General Contractor BIM/VDC Manager 19 30 minutes DTU6 General Contractor VDC Manager 23 90 minutes DTU7 Smart Infrastructure Industry Senior Consultant Digital Twins 4 65 minutes DTU8 Smart Infrastructure Industry VP Digital Twins of Smart buildings 7 65 minutes

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75 Interview Question naire Design For this study, the researcher used a qualitative research approach to investigate Digital Twin cost s and benefits as no monetary values were attached to the information obtained from the interviewees. The qualitative method was chosen because the goal was to obtai n useful insights in form of subjective opinion from interviewees which could help to generate new ideas related to the research objectives. As explained by (Creswell & Poth, 2016) , qualitative research is an inquiry approach to understanding that enable s the researcher to develop a comprehensive, complex image, examine language, provide in depth viewpoints from informants, and carry out the study in a natural environment. In order to gain information about Digital Twins from the e, a semi structured interview approach was chosen among the several data gathering techniques utilized in qualitative research. Interviews allow the researcher to get more in depth information about the topic, particularly when the topic is unclear and co mplicated. Researcher designed a list of questions and categorized these questions into 4 sections : Section 1 (General Information), Section 2 (Cost Related), Sections 3 (Benefit Related ) , and Section 4 ( Cost b enefit r elationship and Guidance to Digital Twin implementation). D esigned questionnaire can be found in the appendix section of this study. Questions in s ection 1 sought general information abo ut the interviewee s and the organization in which they work. in this sections, researcher also ask ed questions about specific roles interviewees played in their respective organization, number of years of experience gained while working with Digital Twin and in the construction environment, a nd specific use cases in construction where Digital Twin have been applied . Also in this section , the researcher attempted to get each interviewees subjective opinion on

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76 how successful or unsuccessful they considered their Digital Twin implementation for the identified use cases. Questions regarding any anticipated ROI and payback time were also asked and finally, main enablers and barriers to case study based implementation plans were discussed. Questions in sections 2 focused on obtaining information on some cost implications and major cost drivers of D igital Twin implementations in the construction environment. In Section 3, tangible benefits seen during implementation were discussed while in section 4, the researcher tried to obtain key considerations/guidance that can ensure full potentials of Digital Twins are realized for a construction project while driving down the overall implementation costs . Data Analysis Simple steps were taken to analyze the data gotten through the interviews. Thematic analysis of interview data was conducted in the following steps. These steps are a modified version of the analysis identified by (Braun & Clarke, 2006) : Step 1: B ecome familiar with the data by reading and digesting the transcript . Step 2: D evelop terms that identify features of the data relevant to the resear ch. Step 3: Look for themes, which are patterns that capture something important about the data and are closely related to the research objectives. In this step, the codes created in Step 2 are evaluated and arranged into bigger topics. Step 4: Review and refine the themes in the previous steps. Step 5: Report on the results. Conducting the Interviews before recording the meeting. The interview procedure began by outlining the purpose of the study and giving a description and definition of the Digital Twin in the context of a

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77 building project that was suggested by the author. The researcher also shared their personal main motivation for conducting the study, and briefly explained some of the challenges and limitations of the study. The next questions asked by the researcher f ocused on the interviewees' background in the construction industry and their familiarity with digital twins. The interviewer also carefully answered any queries the participants had regarding digital twins in order to ensure they fully understood the conc ept. Next, interviewees were asked to provide general information regarding their organization, their specific roles, use cases where they have applied Digital Twins, years of experience in the use of Digital Twin, estimated ROI for Digital Twin implementa tion if available, and factors that helped determine their specific objectives for Digital Twin implementation. The virtual recordings were transcribed almost verbatim afterwards. For each interview question, the researcher read the transcript of each inte rviewee and coded the discussions under three major themes: the Cost implication of Digital Twin implementation in Construction, the benefits of Digital Twin in Construction, and possible implementation guidelines potential implementers could follow for a successful application in construction projects. A code is a type of raw data extracted from interviews and provides a summary attribute that symbolically represents data or information gathered by the investigator (Labra et al., 2020). Examples of code used to classify and describe obtained data under themes include the following, for Cost: hardware tools, software tools, data infra structure, training and expertise, maintenance, and updates. Coding used for the benefits: project information transparency, real time tracking and analysis, Improved synergy between stakeholders, Safety and hazard prevention, Cost savings, Predictive Analysis, Material

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78 and Resource Tracking. Similarly, coding used for the Implementation Guidelines: Choosing the right Project D elivery Method, Digital Twin inclusion in project contract, Identifying Digital Twin scope and purpose, bring in other Domain Experts, Data Management/Standardization, Obtain Executive Approval, Pick the right hardware and software tools, Digital Twin impl ementation plan, and monitor/measure processes. Findings: Cost of Digital Twin Implementation in Construction Although many have cited the numerous benefits, there is scanty knowledge about the actual upfront investment costs of Digital Tw in implementation in construction. It Is crucial to fully comprehend costs and benefits and to assess them in terms of the building's whole lifecycle. However, it takes more than just buying and installing some software to establish a D igital T win. While D igital T wins may be thought of as products, it is helpful to think of them more in terms of ecosystems because of the interdependence of their physical and digital parts. The quality of the data used to create the D igital T win is also crucial and may have direct effects on the overall cost. Due to their reliance on data, D igital T wins will be unable to provide a return on investment (ROI) of any significance if the data they need is not adequately read and extracted. For the interview question on the Cost i mplication of implementing Digital Twin in Construction, interviewees answered to the best of their knowledge each providing a subjective perspective of the topic. For privacy and security reasons, each interviewee was assigned a code DTU1, DTU2, DTU3, etc . denoting Digital Twin User. Their company type, positions, and years of experience are presented in Table 4 1 above. Upon interviewing DTU1, a software agnostic system integrator for Buildings, to understand the economics and cost implications of Digita l Twin in a construction project,

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79 the following responses were impossible to obtain. This is because generally, organizations have not adopted Digital twins in a widespread manner as most are still going through the proof of concepts and pilots. Secondly, there is no such thing as Digital twin technology because it involves all sorts of different components. We could be integrating up to 20 systems in order to make it work. we could be integrating G IS, BIM, etc. we cannot simply quantify the cost of using these integrated systems owing to the fact that most of these technologies are still transformational. Transformational technologies make you do things in many different manners. For instance, you c annot say that step A of implementing a D igital Twin for a system used to cost me $20/hr. and now costs me $15/hr. Digital Twins do not work that way and are completely transformational almost the same way BIM was transformational going back to the design process. However, we can put a cost savings to Digital Twins. When project owners ask us what it is going to cost to go into BIM workflow, we do not provide a number because of course we cannot. We usually tell them the strategy will actually save them mon DTU1 stated that since Digital Twin is a complex amalgamation of various technologies making the cost difficult to ascertain, he pointed out that BIM indirect cost savings seen in many different forms could be likened to Digital Twin. Accordin g to foundation for Digital Twin development in construction. When we looked at the use of BIM, we could not look at the direct cost savings but we were able to consider the indir ect cost savings. Certain things we looked at were, what is the reduction in RFIs? Consider an RFI process for a typical project that cost between $3,000 to $5,000. We

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80 were seeing up to 95% cost reduction when we started using BIM while implementing the 3C so because RFIs are basically failures to communicate. The second thing we observed was a drastic reduction in change orders as a result of adopting the strategy. Claims were a lmost non existent in this workflow as there was enhanced communication DTU3 opinion on the issue buttressed on the problem associated with the nature track and quantify cost information from a construction environment is that, unlike the manufacturing industry which works within a programmed environment, construction projects are dynamic in nature and most effects of changes cannot be readily measurable . Any changes in the manufacturing processes are immediately measurable in terms of failure rates, resultant delays, etc. even if you find two buildings that are exactly the same, geographical locations and weather conditions during construction may differ causing variations in many factors. For any of the projects, cost savings may be realized due to factors like early delivery of materials or even favorable weather working conditions as compared to predicted but even these cost savings are generally diffi DTU3 also emphasized the idea that Digital Twin is still in its development stage making it impossible to predict and monitor a possible ROI and payback time yet. He its nascent stage, most projects are facing various challenges with implementation making it impossible to track the cost or predict its ROI and payback time. For current projects using the technology, any developed ROI will be

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81 abstract and most likely wil ROI with ROI for Digital Twin, noting that since BIM forms the basis for Digital Twin implementation in construction, their utilization may share same ROI. DTU3 cited a previous study he helped to c (Teicholz, 2013) . The study showed the evaluation results of potential ROI for investment in BIM FM technology integration and its associated processes. Findings indicated a return on investment of around 64% and a payback time of 1.56 years. Although the analysis's assumptions are broad, the outcome suggests that effective BIM FM integration may provide considerable advantages for the owner. These gains are the result of smart use of a digital database fo r storing building information and the savings made possible by collecting data during the design and construction process rather than waiting until the structure is finished. Via the utilization of this data, facility managers and personnel may improve th e building's efficiency and performance over its lifespan through better maintenance choices. Cost Drivers for Digital Twin Implementation in Construction Just like any other technology project, understanding the true cost of a Digital Twin implementation for a construction project can help owners decide whether to start, halt, or continue its deployment. It also aids in the selection of vendors and the appraisal of projects. It is an excellent resource for making sound decisions. As revealed by interview s conducted by the researcher, only a few industry practitioners have started using Digital Twin but no organizations in the construction industry have yet been able to successfully track their expenses while integrating Digital Twin solutions into their p rojects. However, it was identified that there are two major

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82 types of cost drivers for implementing Digital Twins. These can be categorized as: (1) direct costs and (2) indirect costs. For direct costs, interviewees noted that Hardware, Software, develop ing a Data structure, Expert Training or Hiring, and Maintenance and Updates all contributes to the costs involved. Indirect costs associated with Digital Twin implementation include utility bills, office supplies, labor costs, expenses incurred for securi ty and privacy programs, etc. Direct Costs Hardware Depending on the technologies being used, the key hardware technologies driving Digital Twins include computers, sensors and actuators, network devices like routers, edge servers, IoT gateways, drones and UAVs, HD cameras, semi automated machines like bulldozers, intelligent compaction equipment, Virtual Reality Wearables, 3D printers, Robots, Laser scanners and other geospatial technologies, phones, RFID tags, construction exoskeleton wearables, and other devices may be required, each having different roles they play for a construction project. These technologies have proven highly beneficial. The cost of these technologies can, however, vary widely depending on the specifications and capabilitie s of the hardware. Digital Twin implementation in construction may in the future require sophisticated technologies like autonomous equipment like S Robot dog, and autonomous heavy machines. The costs of th ese technologies vary by location and change with time. Table 4 2 below lists a few of the technologies that may the acquired for Digital Twin implementation for a construction project.

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83 Table 4 2. Some Digital Twin enabling tools for c onstruction Construct ion Hardware/Equipment Likely Costs Drones $1500+ VR headset $430 $650 Sensors $85 each Active RFID tags $10+ Outdoor cameras $200 Indoor Cameras $100 Bulldozer $150,000 $200,000 Robot dog $75,000+ Laser scanners $20,000 $100,000 Construction exoskeletons $40,000 $120,000 Software The analytics engine that turns raw observation into valuable insights is a crucial part of Digital Twinning. Digital Twin set up typically requires specialized simulation software, which can be purcha sed and/or licensed on a subscription basis. The cost of this software will depend on the specific features and capabilities it offers. For example, BIM, as identified by some industry practitioners, serves as a platform for Digital Twin implementation for construction projects. BIM digitally captures the physical and functional attributes of a building to help guide decisions throughout construction and operation by digitizing the building's information and graphically incorporating it into a measurable 3 D model interface. This interface incorporates geographically related data regarding assets, materials, costs, and schedules, and it adds additional capabilities to construction simulation, project management and planning, cost prediction, and energy analy sis. The commonly used industry phrase "4 D BIM" refers to the coupling of three dimensional models with a fourth dimension of time, allowing project participants to see scheduled tasks before physically executing them. "5 D BIM" adds another cost dimensi on. Managers may predict conflicts using BIM long before they develop, lowering risks and costs throughout the project's lifetime. Some small construction

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84 businesses have been resistant to using BIM since software packages cost between $6,000 and $12,000, not including training. Several pieces of software have been developed to aid in the monitoring of asset performance and the running of simulations to forecast possible outcomes or maintenance that could confront the asset in manufacturing and other sector s where Digital Twin has evolved greatly. Digital twin software is used by business users to acquire insights about the performance of their goods, services, and processes. Technology experts use the Digital Twin software to plan, create, and manage comput er networks, simulate new technologies, and discover possible problems with system performance. Manufacturers use it to plan and monitor production, detect errors, and find opportunities for improvement. Researchers can utilize digital twin software to lea rn about new technologies and investigate the interplay of various systems and processes, and Consumers may utilize the software to personalize their experiences and receive information about the performance of their products and services. Table 4 3 presen ts an inexhaustive list of some software and their monthly subscription costs. The costs of these digital twin software vary and depend on certain factors like the features and capabilities the systems offers, the software vendors, organizational size, the specific industry in which it operates, and the number of users. For smaller organizations, Digital twin software costs may range from $20,000 to $50,000 for a basic system. For larger organizations, the software cost may range from $100,000 to $200,000 ( sourceforge.net, 2022) . For organizations that require additional services, such as custom development, maintenance, and training services, the costs of the digital twin software may env be higher, ranging from $500,000 to millions of dollars, depending on

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85 the complexity. These digital twin software costs continue to evolve as new features and capabilities are added and competition amongst vendors continues to increase. Other considerations that should be made also include costs of software that can be in tegrated with Digital Twin software like analytics platforms that provide real time insights and visibility into the performance of the Digital Twin, databases that can be used to store and manage data associated with the Digital Twin, machine learning (ML ) tools, Artificial Intelligence (AI) solutions which can be used to automate and optimize the performance of the digital Twin by predicting and responding to changes in the construction environment, and IoT platforms which enable the Digital Twin to inter act with the physical environment in which it is being used. This may include controlling physical assets such as lighting, temperature, or other environmental components, as well as receiving and responding to data from sensors or other physical devices. Table 4 3. Some Digital Twin e nabling s oftware Digital Twin Software Vendor Costs Enterprise Process Center (EPC) Interfacing Technologies $10/month/user DC E DigitalClone for Engineering Sentient Science Corporation Ayla IoT Platform Ayla Networks AWS IoT Platform Amazon Asite ASite $375/month Beamo 3i Inc. $890/month Haltian Empathic Building Haltian $5.31/month/user Giraffe Giraffe $250/user/month MapleSim Waterloo Maple Simens NX Siemens $287/month GeoSpin GeoSpin $80/month Matterport Matterport $10/month Predix Platform GE Digital Siemens APM Siemens Energy Authlink Authlink $49/month Azure Digital Twins Microsoft Ansys Twin Builder Ansys Unity Unity $185/month Procore Procore $4500/year Trimble Connect Trimble $10/user/month min. Jonas Construction Software Jonas Enterprise $249/user/month OpenSpace Capture OpenSpace $100+/month Allplan Engineering Civil Allplan smartWorldPro Cityzenith ArcGIS CityEngine Esri R&D Center $2,000 $4,000

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86 Interviews with some construction professionals revealed that some of the above vendors can be consulted to design specialized software for a particular purpose in a construction project. It is difficult to have an estimate of what the software subscriptio ns will cost as every Digital Twin is different for every purpose and objective. In construction, specialized software, such as Trimble Connect, Quadri, Agile Assets, or Tekla, can be used to access digital twins. Others applications that can be used in co njunction include Procore, Revit, Navisworks, BIM 360, PlanGrid, Enterprise Construction software, OpenSpace capture. These solutions provide project stakeholders with a centralized, single source of truth for the storing and distribution of digital twins. For example, Trimble Connect a cloud based collaboration software that helps businesses in the construction industry connect, review, and coordinate projects with stakeholders in real time, can be integrated into a Digital Twin platform. Also, digital T wins can also be integrated with existing software like OpenSpace Capture, an AI powered, fast, and simple platform that can fully document jobsite activities, analyze progress, reduce risks, resolve conflicts, has the capability to perform 360 degree capt ure, automatic image mapping, and enhance project collaboration. SmartWorldPro Digital Twins help owners and managers better monitor, coordinate, analyze, and predict outcomes at any scale, from optimizing building maintenance functions to climate change i mpact forecasting. ATOM is an advanced, one of a kind engineering tool that integrates an augmented reality display into a construction safety headgear, as well as its own processing capability. ATOM allows construction crews to see 3D models on site with millimeter precision.

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87 In this study, we have touched on a list of a few software, concepts, and costs without getting into specific software implementations. This is because there is currently no such system that can get everything operating out of the bo x in all cases as every construction project is unquestionably unique. Data s tructure for DT One of the initial expenses of deploying a Digital Twin includes investing in an easily available data infrastructure. Creating Digital Twins of physical models a t a building site is now possible with the use of algorithms based on computer vision methods, multi modal sensor data, and deep neural networks. Inferences about 3D structures are usually made using sensors including laser scanners (LIDARs), radars, therm al imaging cameras, and regular picture and video cameras. There are many available methods for properly gathering data for digital twins. Smartphone Cameras are affordable and have cameras with enough resolutions and image quality to obtain data for Digit al Twin us. For example, progress may be tracked by comparing an as built reconstructed model to an as designed BIM model using random daily construction photos obtained on multiple cellphones with varying cameras and lighting conditions. This technique ma y also be used to manage temporary resources on an infrastructure construction site, such as employees, equipment, and supplies. In addition to video surveillance cameras and head mounted cameras and body cameras, time lapse cameras can also be used to obt ain a sequence of captured photographs which translates to a time moving video. Digital Twin users for a construction project may also consider the use of UAVs like drones or fixed wing planes equipped with high resolution cameras and sensors operable in a fully autonomous mode. This fully automated data capturing, and processing method could be a better

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88 preference especially as the cost of labor in construction is constantly on the rise. For example, Microsoft HoloLens is gradually making its way to the bu ilding site in the shape of a hard helmet. It is generally used to visualize 3D models over real world environments, which aids in the translation of intended structures to spatial representations. Also, Google Clip, a small, battery powered, AI enabled ca mera aimed at the domestic consumer market, was recently introduced by Google. This is only the beginning for customized hardware paired with onboard AI software for real time picture data processing and analysis. To build the Digital twin representation o f a construction site using the raw image data from various cameras mentioned earlier, implementers can adopt automatic data processing methods using the recent advancements in computing power, GPU processing, and deep learning algorithms. It has been esti mated that self driving vehicles can produce up to 2 gigabytes per second (GB/sec) of data, and that building sites produce the same amount of data, if not more. To keep projects on track and below budget, leading organizations in the sector are increasing ly turning to cutting edge technology like Digital Twins, which mimic the functioning of real world products like vehicles. To better analyze these data, the cost of designing a data processing infrastructure should not be overlooked. Some of these data pr ocessing approaches may include: 3D reconstruction photogrammetry and structure from Motion (SfM) technique: A science of taking measurements from photographs to recover the exact positions of surface points. With this technology, 3D scenes can be reconstr ucted from given photos. Object detection and recognition: used for robotic application on construction sites. With this technology, robots are able to navigate paths and recognize and pick up objects.

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89 Object Tracking: Typically used in robotics for tracki ng the position, velocity, and dynamics of an item over time. Utilizing this technology, we can better plan where to place employees and machinery on the construction site. Also, hand signals on a construction site may be automatically identified using obj ect tracking to decipher gestures in human computer interactions. These afore described data processing strategies can use several gigabytes of data. Setting up the system to capture these data will cost time, money, and an enormous amount of other resourc es. In addition, even if an organization has already invested in things like sensors and the CAD software required to generate a basic representation of an asset, it may want to think about investing in things like edge computing devices, which help analyz e data. In essence, the infrastructure of the digital the digital twin creation and implementation in construction. Training and e xpertise To successfully implement a Digital Twin solution in a project, experts will need to be hired or employees trained on how to use the digital twin hardware and software tools. This will require additional time and resources. According to the Association of Talent Development (ATD) Re search, ( 2021 State of the Industry | ATD , n.d.) , to determine per divided by the number of employees. Employee expenditures on training may include salaries and non salaried develop ment, delivery (including learning infrastructure), learning provider, and tuition reimbursement. On average, organizations spend $1,252 on each employee to help them grow professionally, according to the ATD. This number is merely an average, but it may b e used as a benchmark to help you think about where your money could go. Training a new employee on the use of Digital Twin technologies may cost anywhere from zero to tens of thousands of dollars, depending on variables

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90 such as firm size, employee skill l evel, the complexity of the tools being taught, the amount of time lost in productivity, etc. Training employees should be seen as an investment rather than an expense, since they represent a growing asset. From the start, contractors and project owners wh o are also employers of labor for a project should set a clear ROI expectation, so that the known benefits realized from employee training on Digital Twin use outweigh the initial investment. Organizations that have the legacy of delivering comprehensive t raining to employees have a 218% higher income per employee and have a chance of enjoying a 24% higher profit margin (Isidora, n.d.) . Maintenance and u pdates The cost of software maintenance stems from changes made to software after it has been provided to the end user. Program does not "wear out," but it does become less useful as it ages, and there will always be bugs in the software. Digital twin technology may require ongoing maintenance and updates, which could incur additional costs. The mo st common mistake tech entrepreneurs make is underestimating the cost of maintaining software. It is widely assumed that software expenditures are a one time expense spent when the product is developed/purchased. On the contrary, industry analysts believe that regular maintenance expenditures account for more than 90% of all costs associated with a relatively contemporary piece of software, which most businesses do not account for. This disrupts the budget and, in some situations, throws the entire project off schedule. As digital Twin involves many moving parts, one or more software integrated with various IoT devices with varying degrees of complexity and resource requirements, the cost of maintaining and updating the system would vary. As identified by (G alorath

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91 Incorporated, 2022) , a basic maintenance package for software might comprise a variety of services, depending on the company's software requirements and current market conditions. These services are broadly categorized into three groups. Correcti ve software maintenance: this may involve the work done to troubleshoot program/software problems that show up during the initial testing phase or once it is out to the users. This maintenance process usually makes up 20% of all software maintenance costs. Adaptive Maintenance: These are all the alterations and additions done to the software to make it conform to the latest norms in the business. A significant proportion (about 25%) of annual software maintenance expenditures are attributable to adaptive ma intenance. Enhancing Maintenance: This is usually done to include/remove any program functions as requested by the board, consumers, or users of the software. Enhancing maintenance can make up over 35% of all software maintenance costs. The nascent stage o f Digital Twin deployments in construction makes it impossible to determine the maintenance costs. Potential implementers should, however, note that the bulk cost of issues is caused by the need for enhancements in functionality as the software solution ev olves over time. Given that software maintenance costs generally account for 75% of TCO as identified by (Galorath Incorporated, 2022) , it is imperative that users do precise maintenance cost calculations in order to correctly analyze the Total Cost of O wnership (TCO) of Digital Twin software. Indirect C ost s Industry professionals also revealed indirect costs of implementing Digital Twin to include certain utility expenses , office supplies that may not be directly used for the purpose of Digital Twin use alone, labor costs for personnels used to achieve certain Digital Twin related objectives, expenses incurred for security and privacy programs, etc. Indirect costs may also include the cost of running, updating and maintaining assets

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92 (hardware and software ) not directly linked to the Digital Twin system alone. Other overhead costs like administrative expenses also classify under indirect costs. Opportunity Costs As identified by interviewees, adoption of digital twin technolog ies should be done with a mind set that prioritizes the consumer, fosters a culture of innovation, and boosts operational efficiency. As part of a digital transformation, Digital Twin is a tool for change, especially for the construction industry. C onstruction businesses should be askin g questions such as what opportunities will the company miss and how much will it cost the company if it does not execute the Digital Twin technology? Opportunity costs are certain rewards foregone by organizations by making decisions to pursue one course of action over another. Having a solid grasp of the opportunity costs associated with a number of alternatives helps decision makers choose the best course of action (GTtechnology white paper, n.d.) team $100,000 to set up a Digital twin system that moves your concrete maturity monitoring system to the cloud. By moving to the cloud, you reduce your staff by one quarter and let your system manager monitor the system and provide feedback remotely. If we assume that the current staff payroll is $250,000 per year plus benefits and your annual sales revenue is about $700,000. If as a result of implementing this strategy, you were able to save time, effectively track performance, increase your annual revenue to $850,000, and cut down the annual payroll to $187,500. Your savings would be $87,500. This means your opportunity cost of not implementing this technology would be negative this value ( $87,500). There are other elements to consider as the opportunity cost of not implementing the Digital Twin in construction. For construction projects, current traditional strategies

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93 may not offer the opportunities these elements could create for project stakeholders. Some of these elements include: Customer Experience: As the Digital Twin concept of gets more understood with time, its adoption in the construction industry will get more customer focused . Unless data is transformed into useful information, it serves no use to collect it. However, most construction companies lack the necessary tools to efficiently gather, evaluate, and put data to good use. Digital twins can provide for more efficient information management, which in turn improves stakeholder cooperation and customer satisfaction . Operat ional Efficiencies: the application of Digital twin is more than just installing sensors and monitoring the state of assets and buildings during or after construction. With the integration of AI and ML technologies, building performances can be predicted a nd adjustments can be made to realize cost savings and improve overall performance. It is only a matter of time before organizations will realize the opportunity cost of not leveraging the capabilities of these latest technologies to improve operational ef ficiencies. Workforce needs: O ne of the challenges the construction industry faces is skilled labor shortages. This labor shortage is partly exacerbated by the changing nature of the industry. To maintain efficiency despite the skills shortage, companies must embrace technologies like the Digital Twin to perform special routine or repetitive tasks to reduce the number of required employees. As Digital Twin becomes more acceptable, the failure of organizations to implement training programs to tra nsition the workforce to be digitally ready can cost organizations opportunities for growth. The opportunity costs of not using Digital Twin will be enormous with time since they are not one time. It will eventually be a cost that grows year after year as the difference between positive experiences of using Digital Twin and negative experiences of sticking with traditional project execution strategies grows wider. Other Non financial C osts and I nhibiting Factors In contrast to the financial costs associate d with developing and deploying a digital twin, the non financial costs encompass all the obstacles that may not have any monetary consequences but still have an effect on its widespread adoption. In this

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94 us ing Digital Twin is a factor that should be foreseeable. Just as for any new technology, adopting the Digital Twin usually requires significant changes to be made from reengineering the regular constructional/operational processes, re viewing contract doc uments , change of contract types, training staff to use the new technology, or even a total shift of paradigm, thus putting employees outside their comfort zones. A counterargument is that Digital Twin will create mo re employment opportunities, but others may see the new technology as a threat to their current employment because of the uncertainty it represents. The resistance of workers is an important social aspect that should be addressed while making the decision to implement Digital Twin. Employees who refuse to utilize the technology properly and who sabotage it might be more likely to cause its failure than any technological flaws or high expenses. Owners of projects themselves may also be resistant to the introduction and use of Digital Twin. It is p ossible that they would rather stick to the traditional ways and have services supplied by a trusted design professionals and workers than by automated systems. People may be reluctant to utilize Digital Twin because they are either uneasy with or lack the necessary technical expertise to make use of more complex autonomous systems and machines such as robotic technologies and IoT devices. Time is also a cost, particularly in the construction industry, where stringent deadlines and financial penalties for late completion apply to all projects. Implementing a software solution, especially when personnel is still in the learning phase, is an excellent illustration of time cost. Adjusting necessary features, introducing new

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95 processes, managing and processing d ata, and training people on the worksite all take time. Benefits of Digital Twin Implementation in Construction If digital twins are properly deployed, the total cost of ownership for physical assets may be significantly reduced. However, the value must be evaluated in its entirety, from the opportunity cost of not utilizing the technique through the sustainability advantages reaped while the asset is in operation. Interviewees were asked to elaborate on some benefits experience firsthand while using Digita l Twin for their projects. The researcher asked specific questions about financial benefits or cost savings realized from implementing Digital Twin in their respective construction projects. Most of the feedback obtained indicated that their project teams and stakeholders are still in the early stages of Digital Twin deployment and hence, obtaining fiscal track record s at this stage is challenging . They however, identified ways they have benefited from integratin g Digital Twin s into their systems. In this s ection, transcripts from interviews were read and categorized in the following themes: Project Information Transparency, Real time Tracking and Analysis, Improved Synergy between stake holders, Safety and hazard prevention, Cost savings, Predictive Mainten ance, and Material and Resource Tracking. Project Information Transparency The key to a successful building project is effective communication among all parties involved. The construction industry presents a complex environment for project execution due to its diverse and fragmented structure. Thus, boosting process openness and overcoming information gaps between construction stakeholders are crucial components to the success of the project and to developing good relationships

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96 between the participating par ties. Because BIM is used in the construction industry as a static 3D visualization tool for the built environment, it is not able to fully promote information transparency during the different construction phases. According to DTU3, time data from a building's surroundings gathered through smart IoT sensors, D igital T wins may help improve the visibility of information flows within construction operations and between stakeholders, hence fostering a culture of this claim, DTU2 gave a few instances when D igital Twins improved the accessibility and management of grant permits based on 2D plans, a process which is time consuming, may use D igital T wins to undertake automated and tra nsparent checks on legal compliance of the building submission models throughout the procurement and contracting phases of a project. Digital Twin allows the building authority to verify the geometry and semantics of the model before sending the report to the architect. From the initial planning stages through to the handoff to the building owners and facilities managers, this strategy may guarantee that all buildings have fulfilled certain minimal standards for a high quality DTU4 believes that str eamlined handover and more effective data use are key equipment, systems, finishes, zones, and other metadata may be automatically recorded and then imported into a facilities management system. Facility Managers and owners may choose to extract files from BIM models and import these files into a Computerized Maintenance Management Software system and use them throughout his can help generate high quality data and

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97 avoid data entry costs. Detailed building models may be updated with information regarding equipment assembly, ducting, plumbing, electrical systems, etc. as they become available throughout the process of docume nting the as built state. Back when we started using BIM, we usually incorporate this data into the CMMS system, either via a COBie import (which is a Construction Operations Building Information Exchange platform) or through direct integration with BIM. W hen all the equipment has been installed, the COBie data may be updated with the serial numbers, and the FM system will be ready for use upon building DTU5 also highlighted the benefits Digital Twins could afford construction companies, technology vendors, material suppliers, and project owners with respect to of Digital Twin helping construction teams save money on ongoing pr ojects by providing us with a list of vendors and suppliers that accurately reflects real transactions between suppliers, subcontractors, and owners across different projects. If successfully implemented, the information stored and managed can help save mo ney for future projects by making informed decisions based on material and transaction information Real time Tracking and Analysis Real time data can be captured, streamed to a digital platform and shared with proje ct stakeholders thanks to the physical to virtual connection ensured by the Digital Twin and powered by the development of CPS, IoT, sensors, and AI. As a consequence, during the project's full lifespan, stakeholders may monitor its progress in real time, perform assessments, and take part in closed feedback loops. Interviewees were able to identify benefits relating to the Real time tracking and analysis that can be

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98 made possible by Digital Twin implementation. In order to achieve an eco friendly building throughout both the construction and operation stages of building projects, energy and water usage must be kept to a minimum as the problem of non sustainable structures becomes an increasing concern. IoT sensors placed all around the building may be used to monitor energy and water usage in real time . While data on energy and water consumption is gathered from both real and virtual assets for analysis and feedback, the whole system may be monitored through Digital Twin to find out what has to be done to cr eate a more sustainable environment. A database that can be trained for future projects to give insights on facility design requirements, providing information on variables like pressure, power and lighting, cooling, and heating. This trained model may be created using all the data gathered throughout the operation and use of the facility. DTU 6 noted the huge benefits Digital Twins can bring to the industry for various Cons truction processes makes the implementation challenging. As technologies get more advanced, video cameras and IoT devices can be leveraged and integrated to form a Digital Twin system for an ongoing construction project. Digital twins may be used to keep t rack of a project's progress as it is being worked on and to compare it to authorized shop drawings, material submittals, cutting lists, calculation notes, timetables, etc. to make that work is being done in accordance with the contract's By using Digital Twins to perform on site monitoring and tracking, a construction site dewatering, for instance, Digital Twins

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99 may be useful for keeping an eye on the water levels in the wells that make up the dewat deep, dewatering systems pose a great risk to workers. Even while contractors take this unpredictability into account by increasing the number of wells and safety elements in de watering system design by using backup pumps and generators, accidents still occur, and floods infiltrate the work site. Contractors may examine the situation and take precautions against accidents or floods in the case of a predicted system failure with t he use of digital twins that are used to monitor water levels within the wells and around the Improved Synergy b etween Stakeholders Throughout its lifespan, the completion of a construction project is a team effort that calls for th e participation of many different entities. Therefore, construction project teams are distinct from other teams in other sectors since they consist of people who work for a wide variety of companies. The success of the project depends on the strength of th e ties created between the many participants. Of all the four C's models (Collaboration, Communication, Coordination, and Cooperation) necessary to form an effective project team, Collaboration is seen as the most potent, since it entails everyone involved working together toward a common goal. The use of digital twins allows for improved communication and coordination between construction industry players at every stage of a project's lifespan. Trade contractors on a typical construction site often operate independently, each focusing on their own part of the project. This isolation and narrow concentration may lead to competitive dynamics and poor communication, both of which can have a detrimental influence on the overall project outcome. When trades do n o t work together

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100 to resolve coordination issues and save rework, everyone loses. This is especially true when it comes to mechanical, electrical, and plumbing (MEP), and finishing operations. To avert such complications, contractors and subcontractors can in this situation utilize a Digital Twin of the MEP and finishing work to compare actual progress with the planned schedule on the job site in real time. As a result, collaboration is improved, possible conflicts are prevented and seamless handoffs between trades are ensured. One other way project teams may benefit from Digital Twin is on the issue of concrete mix designs. Schedule delays, cost overruns, and disputes on the job sites are all the more likely when contractors have to reject a ready mix concre te truck upon arrival because the concrete mix design employed at the batch plant is different from the authorized concrete mix design. In order to connect the batch plant with the construction site, a digital twin may be used. As a result, contractors may have real time data access to the production of concrete at the batching plants allowing them to confirm the mix designs before dispatch. Safety and Hazard Prevention Interviewees noted that a lack of data availability, accessibility, and effective utiliz ation was a major factor in why stakeholders are not proactive in addressing certain jobsite issues. It was also found via interviews that Digital twins as a platform that connects information from different sources and stakeholders, allow construction pro ject teams to anticipate scenarios through the integration of data from smart IoT sensors with algorithms to generate predictions and take necessary preventative measures. For e construction sites a zero harm zone, a goal shared by all parties involved in the building process. Opportunities for proactive safety management may be created via the use of

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101 wearable sensor technology, visualization technologies, the Internet of Things , and Artificial Intelligence. Managers and officials in charge of safety may monitor the whereabouts of potential hazards, employees, and machinery to cut down on accidents. Digital twins may provide a two way communication channel, allowing employees to a prospective or ongoing project may be fed site accident data acquired from multiple projects to perform safety simulations and detect possible dangers prior to the ir occurrence. Again, by installing sensors within a building and performing numerous risk simulations relating to overcrowding, and fire, Digital Twin may also be utilized for complex structures with a huge flow of people to enhance interior safety manag ement. The use of a Digital Twin may increase safety on construction sites by keeping track of all potential danger behaviors, assisting with the customization of training programs in light of the recorded risk behaviors, and triggering real time alerts in response to possible hazardous scenes. Cost Savings Today's construction industry is under intense pressure to deliver higher quality structures at cheaper costs. As a result, the company's management expects them to constantly innovate in order to increa se efficiency and become more competitive. The Digital twin concept holds a promise of boosting the efficiency of modern buildings by making them more sustainable in many varying aspects. Interviews with Digital Twin users revealed that a critical area whe re projects using Digital Twin will benefit is through savings from enhanced communication. Digital T wins serve as a single source of truth, streamlining the channels utilized to

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102 communicate project modifications or decisions. This eliminates the risk of m isinterpretation or omission of relevant data, reducing the need for Requests for Information (RFIs) which might cause the project to be delayed and incur avoidable expenses. As identified by interviewees, although these myriads of benefits are valid, it i s difficult to quantify and attach a monetary value to them. 5% of Said DTU2. He added that Digital twins by having a focus on assets, as well as costs and schedule, should be able to eliminate some back and forth actions completely and so on overall, save somewhere between 3% and 5% of the construction costs by simply communicating in a differen t way. Facility owners can realize significant cost savings from Digital Twin use during should be noted that the main benefits of D igital T win will not come during constr uction but during the operation and maintenance phase of the building. This is because, after handover, the building will cost less to run, there will be reduced energy requirements, and the building will be more efficient. Think of the average efficiency of the use of the building use, the occupation rate of the building at any one time. Say for example, how do you schedule the efficient cooling and heating of a school building in a hot climate that has a part of the building under a shade and hence natura lly cooler than other parts? One significant benefit of using Digital Twin has to do with the control systems which are quite often like thermostats that heat up and cool spaces based on settings below or above certain temperatures. The use of thermostats for fixed temperature is

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103 okay, but what happens when you have changes in pedestrian traffic during different times of the day? You end up getting irregular peaks and lows in temperatures. If you can get a more predictive view to correlate the sensors which are heating and cooling the building with the people using and leaving the building, you can preheat and pre cold a building automatically thereby reducing its overall energy use. This will inevitably Pre dictive Analysis Throughout the course of a construction project's lifespan, several different parties will need to make many choices. As computing technologies, data science, and artificial intelligence (AI) continue to progress at a fast pace, Digital Tw in provides new possibilities to use these technologies to execute what if scenario analysis and simulations enabling the system to learn from data and assisting project stakeholders in better decision making. One of the Digital Twin users interviewed poi nted out how Digital Twin could assist building authorities in conducting predictive analysis on buildings. To verify building compliance, the government and building authorities usually conduct inspections at various points during a project lifecycle (i.e ., from the submission of project documents to the start of building operation). Inspections about building height, power use, energy consumption, etc. are all examples of attributes that might be recorded for later use. With the use of digital twins, the building authority can easily access and gather this data in a centralized location. To better optimize the performance of future buildings and to adjust specifications developed to address issues relating to climate change, building authorities can use th ese individual Digital

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104 Twins to run energy, fire protection, or land use simulations for example, to help predict complex situations and make informed decisions. Another common example or application of Digital Twin is on energy performance evaluation and optimization in buildings. The prediction and optimization of sustainable construction. The construction industry is under pressure to adjust its practices and start using m ore eco friendly materials in light of the growing need to combat climate change and move toward a low carbon emission economy. Digital twins may be useful in this situation to analyze the effects of various energy efficiency remodeling strategies on a bui lding throughout its lifecycle (design, construction, operation, and maintenance). Material and Resource Tracking The Construction process comprises of many moving parts and particularly for sophisticated projects, the idea of traceability is crucial. It may be difficult to keep track of everything that goes into a project, from raw supplies to finished goods to personnel records. When materials and other resources in a construction project are being tracked, accurate and timely data is generated that can be used by stakeholders at all stages of the project to better understand and carry out their responsibilities. Due to digital twins' capacity to combine disparate data sources, construction projects and their many moving parts may be tracked in real time. As identified by interviewees, by providing real time data on where resources are being used, and when and how much more these materials are required, a Digital Twin may act as a decision support system for the construction industry, helping to minimize o perating costs and prevent delays. Construction projects cannot proceed without the

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105 availability of materials, so making sure you always have enough on hand when needed is crucial. Again, it is very common to find that construction sites are sometimes chao tic in nature, with several pieces of heavy equipment moving about at fast speeds and varying environmental factors. Awareness of the construction equipment's locations, the activities to be performed, and the site circumstances are essential for maximizin g productivity and safety. In order to improve operations and increase worker and equipment safety, a Digital Twin of the construction equipment and the construction site may enable direct connection with operators to alert them of any change that demands rapid intervention. Key Considerations before Implementing Digital Twin in Construction In theory, Digital Twin can be developed for anything. However, creating a twin of a complex project or building construction process is very difficult in the real worl d. As not so many construction companies are leveraging the capabilities of Digital Twin, it is currently challenging to obtain easy to follow guidelines for implementing Digital Twin in construction. During the interviews, industry professionals and Digit al twin users were asked to throw light on the factors potential implementers can consider before deploying the technology in their construction projects. Although not exhaustive, this section discusses crucial considerations to be made before implementing Digital Twin in a construction environment. As pointed out by interviewees, some necessary factors to consider include choosing the most efficient project delivery strategy, Digital Twin inclusion in the Project contract, Using other domain experts, and D ata Standardization. These are further discussed below.

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106 Choosing the right Project Delivery Method Construction project delivery methods influence how different parties collaborate throughout the project's pre construction, design, and construction stages . As defined by the Project Management Institute (PMI), construction project delivery constitutes a system of structure of the relationships of the parties, the roles and responsibilities of the parties, and the overall sequence of activities necessary to complete the project. Several kinds of project delivery systems exist for project owners and stakeholders including Design bid build (DBB), Design Bid (DB), Construction Management at Risk, and Integrated Project Delivery (IPD). Digital Twin integration in to a construction project will work for any of the aforementioned strategies, however, one of the interviewees opined that IPD could provide the most efficient system for Digital Twin to thrive. DTU1 stated that for potential Digital Twin implementers to r eap the full benefits of Digital Twin, the project owner may have to first choose a delivery method that encourages a before setting up a Digital Twin solution for a Co nstruction project is to set up a for an organizational change with respect to the nature of the contract. Organizations may choose to switch from design bid build to d esign build or even an Integrated the traditional Design ensures team members are all accountable for the pe rformance of other team members and shares in the overall project responsibility while bridging communication gaps across members. In integrated project delivery (IPD), the owner, architect, and

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107 contractor all sign a single contract with the benefit of for malized cooperation between all parties. Digital Twin Inclusion in the Contract If Digital Twin is to be used for a construction project, another crucial consideration that should be made by potential implementers is the inclusion of Digital entation plan in the contract from inception. Every necessary detail of the roles and responsibilities of all parties to use the Digital Twin should be specified in the initial contract documents so all team members will be on the same page. Before the des ign process ever starts, while everyone participating in the project signs a single contract outlining their roles and responsibilities, stakeholders would need to define who does what, and who takes responsibility for certain activities and errors as it p ertains to the Digital Twin. This is also the stage to consider who owns the Digital Twin models and how the Digital Twin will be handed over and transitioned from the construction team to the facility management team after project completion. Using O ther Domain Experts Project stakeholders usually comprise people from different fields most of which are related to construction. Implementers may also consider using the services of other domain experts as Digital Twin is a combination of different compon ents. It is not an the of a kind as the entity it simulates. Although there are pre built infrastructures, hardware, and software platforms available to speed up development, they will not solve all problems on their own, especially for a scenario like the building site. Potential implementers still need engineers who can put the pieces of the hardware and software jigsaw together.

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108 Additionally, they will need data scientists to solve data driven prob lems, machine learning specialists, cloud specialists, as well as experts in other domains. Data Management/ S tandardization Many businesses even in the manufacturing sectors have difficulty developing digital twins due to difficulties in data sourcing or a lack of data consistency. The informational backbone upon which digital transformation projects, digital twins included, are built is a prerequisite to the development of such a system. Therefore, construction businesses should ask questions like, "How ac cessible is my data?" before committing to a digital twin strategy. It is a red flag that more work needs to be done if you require a dedicated human worker on a regular basis to sort pieces of information before they are usable. In reality, building and i mplementing a digital twin for requires performing an extraordinary amount of data piping work and a Digital Twin is only as good as the data that goes into it; however, one should be careful not to overwhelm the system with a consolidated data platform. C arefully observing the data that your teams are currently utilizing can help you narrow down the scope of the data. Care should also be taken in selecting the right data acquisition and data processing tools and techniques such as wireless sensing technolo potential is reached when it uses up to date, cleanest, correct, and most complete data. Guid ance to Developing and Implementing Digital Twin in Construction As with its physical counterpart, the da ta, models, and simulation tools used in the creation and deployment of a digital twin might vary greatly depending on the project's lifecycle stage. Despite this, every Digital Twin comprise of features such as physical asset, virtual counterpart, relevan t data, communication networks, analysis

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109 module, etc. all of which are necessary to produce a framework that guides the development and implementation of the Digital Twin irrespective of the use case. With insights gathered from interviews with industry pr actitioners and ideas from the work of (Ariansyah et al., 2020) , the researcher formulated a 7 step guid ance to developing and implementing a Digital Twin for a project , and cover s the following: Define the Digital Twin Scope and Purpose ; Virtual Represe ntation Development , Information Flow and Data Exchange system Development , Data Analysis system Development , T ake Actions , Verification and Validation , and Optimize and measure . Step 1: Define the Digital Twin Scope and Purpose For each Digital Twin system being set up, the specific purpose and scope should be clearly defined to achieve a seamless implementation process. As identified by interviewees, there is no such thing as the Digital Twin of a n entire building. Such a Twin that encompasses all features and processes cannot be created, at least not at Planning Digital Twin, a Design Digital Twin, a Procurement Di gital Twin, and could go all the way to multiple Digital Twins in Operations and maintenance. Similar to the way various data have been linked together and used to design phone applications like google maps, calculator, etc. data can be linked together and used to write applications While defining the scope and objectives of the Digital Twin, you will also need to identify major sources of delays in your project and prioritize work systems. As noted by scenario where Digital Twin needs to be developed. The user may choose to apply Digital Twin specifically for a work order syste m, for example, for a typical construction

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110 to an electrical problem ranked No. 7. The stakeholders involved should first develop a Digital Twin model for the shut o ff valves to save as much time as possible. This prioritization strategy is even more important because most projects are awarded on a Ultimately, stakeholders would first need to define purpose of Digital Twin by identifying specific areas where improvements are expected and should recognize certain limitations that could impede progress such as accuracy of data and model, lack of data infrastructur e or access, etc. Based on these needs, use it to define specific objectives of the to be designed Digital Twin. Step 2 : Virtual Representation Development Accurately developing a virtual representation of the real asset is an essential step to take in dev eloping and implementing a Digital Twin. It is also important to select the right tool as this may have effect on the model operators and developers. Furthermore, depending on the project stakeholders involved and the nature of the data necessary to comple te the job at hand, there are a variety of options for creating a virtual representation of the real object. While a design professional may only require a 2D illustration to obtain certain sensor information or detect clashes of building components while monitoring the current condition of a physical asset, a non professional may require a 3D representation of the asset in an augmented or virtual reality environment to comprehend the spatial relationship among these building components.

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111 Step 3 : Information Flow and Data Exchange S ystem Development The data and model are what allow Digital Twin to thrive and coexist with its physical twin. There are several potential data generators, including hardware sensors, internal software, historical data, and user da ta. While the hardware sensors gather the characteristic information about the physical asset e.g., pressure, temperature, humidity, etc., the internal software contains algorithms that mirror the behaviors of the real asset. The historical data are all pa st data that can also be integrated into the digital model for future analysis while User data come in form of information collected by humans to track the action and performance of the physical asset. These data can be stored either in a local server (loc al storage) or on Cloud (cloud storage). It is essential to know which one of these storage options is best at every point. In some cases, there might be need to use a combination of both. It is also necessary to build up a communication connection between the virtual asset and the real counterpart with the necessary speed and latency configuration. Wired or wireless connections such as sensors are also viable options for establishing this link. Digital Twin network accessibility is conditional on the natur e of the physical object being mirrored, the Digital Twin's intended use, and the conditions of the physical environment. After a link is established, data processing procedures may be necessary to convert the raw data into a format suitable for optimizati on mapping for both virtual and physical spaces. Consequently, it is necessary to take measures for cyber security protection of the whole Digital Twin system. Step 4 : Data Analysis S ystem Development The Digital Twin's true value lies not only in its abil ity to reveal the intricacies of the physical system, but also in its ability to draw out useful insights from vast quantities

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112 of data gathered from many sources. Considering the kind of data, the models and tools to be used, and the need for visualization , one may identify the required insights that contribute to the anticipated improvements indicated while defining the Digital Twin's requirements. To successfully carry out the data analysis, 3 possible methods may be adopted: Manual method: which has to d o with a human expert to obtain the relevant information form the Digital Twin Semi automatic method: which relies on utilizing AI technologies to analyze data patterns and physical asset behaviors before the human expert can extract insights for the Digit al Twin. Automatic method: which used AI methods to transform raw data into useful insights or actionable data. Step 5 : Take Actions After system analysis, it is then necessary to make a choice and/or take some kind of action in light of the newfound know ledge. If the hoped for benefits of the Digital Twin have been fully realized, the pre established condition action rules will reveal this. Humans may carry out the Digital Twin's recommendation by taking the necessary actions manually or actuators can be programmed to do so. Step 6 : Verification and Validation Verification of the Digital T win must be done to make sure it can reliably reproduce the physical system's behavior with just negligible variations. To determine whether the data and models have been correctly integrated, a series of tests may be run on the physical asset, the data co mmunication network, and the virtual counterpart of the real system. These tests are important because they verify that the Digital Twin is

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113 able to provide the expected insight and carry out the necessary analysis. It is also essential to establish a proce dure that can be used to validate the system. This validation checks whether the specification reflects the expectations of the stakeholders and if the Digital Twin accomplishes its intended goals. Step 7 : Optimize and M easure: Implementing Digital Twin w ill no doubt be a game changer for future construction projects, but after adoption, attention should be focused on optimizing the internal processes with the goal of improving the system to achieve more results. Doing so will involve repetitive progress t racking, reevaluating, and readjusting of the implementation plans. As pointed out by DTU7, continuous optimization of the Digital Twin system is inevitable as the we continue to experience technological advancements. DTU7 specifically added that early ado pters should make little rooms for mistakes, but most importantly gain relevant insights and ideas from the errors for better applications in the future. These insight s will help to fine tune your strategies for implementing your Digital Twin as you gather experience with using the software, IoT devices, and other enabling technologies. The outcomes and advantages gained may be enhanced by conducting periodic monitoring of the project's development, measuring the benefits, as well as making modifications as needed.

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114 CHAPTER 5 CONCLUSION S Since the inception of Digital Twin, investigation of its potential in construction and building has steadily increased. Th is notion of giving a digital duplicate and a two way communication system for an asset has proved to be most beneficial in industry sectors such as manufacturing and aerospace, but, not so much in the construction industry. As publications on Digital Twin in the construction domain continue to rise, t his work is a first attempt to address an existing research gap on the cost implications of Digital Twin implementation in construction . U sing qualitative semi structured intervie w approach , this study sheds light on realizable benefits of Digital Twin application in Construction , key considerations and guid ance potential implementers could follow to fully maximize Digital Twin potentials and minimize overall implementation cost s . To tackle the first of four research objectives, a comprehensive literature review conducted revealed diffe rent Digital Twin definitions that currently exist in various fields in the literature including construction. Furthermore, the authors of this work proposed a Digital Twin definition more suited for a construction project while highlighting in the definit ion , key concepts like the integration of different technologies and the use of the Digital Twin throughout the lifecycle of a project. To address the second research objective, upon review of the current literature, the authors identified the presence of physical asset, virtual part , connection between both twins, availability of data being transferred bi directionally, and the services the system provides as the key features of Digital Twin. Furthermore, identified as B enefits of Digital Twins implementat ion in the construction environment include but is not limited to Transparency of Project

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115 Information, Real time Tracking and Analysis Capabilities, Improved Synergy between Stakeholders, Safety and Hazard Prevention, Cost Savings, Predictive Analysis, and Material/Resource Tracking. This study also validates the industrial applications of Digital Twins as provided by numerous literatures , some of which include use in Facility Management, Architecture, Aviation, Healthcare, and Smart Cities. Additionally, this study identified Supply Chain Management, Retail, Government, Telecommunications, Luxury Goods, Agriculture, etc. as other areas where applications are beginning to appear. Upon review of publications to find relevant information on the cost of developing and implementing Digital Twin in Construction, none was found to be useful in providing a detailed cost analysis on the subject . One article found emphasized on the impracticality of Digital Twin development for a military aircraft due to its outrageous cost. In a web blog , (Lengthorn Paul, 2020) developed a theoretical cost model for Digital Twin for different building type although model was subject to a lot of uncertainties as certain cost items were assumed making the model not so reliable. Interview conducted with 8 industry professionals revealed that while Digital Twin is still at its early stage in construction among other challenges, the cost of its implementation is difficult to track and a comprehensive cost analysis almost i mpossible to develop. While this study as a result does not provide a comprehensive cost benefit ratio analysis or statement of net costs and net benefits expressed in monetary terms, it does, however, expose some major cost drivers as it relates to Digita l Twin implementation for construction projects. Albeit inexhaustive, this work discussed the hardware and software tools, Expert hire and training, building data infrastructure, and

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116 Maintenance and Update as some of the direct cost drivers of Digital Twin use while also highlighting some indirect cost drivers, opportunity costs and other non financial costs associated with Digital Twin adoption . The open ended nature of the interviews held with industry professional helped in expos ing some key consideratio ns potential Digital Twin implementers could make before adoption decision. Some of these considerations comprise Inclusion of Digital Twin in c ontract at project programming stage, Choosing the right project delivery strategy to ensure effective Digital T win benefit , Use of other domain experts and Data Standardization. Finally, interviewees offered some insights on possible guid ance to developing and implementing Digital Twin in Construction. Moreov er, with these obtained ideas combined with research base d findings, the researcher formulated a 7 step gui dance to follow in developing and implementing the Digital Twin for a project. Research Limitations This work contributes significantly to the body of knowledge on Digital Twin adoption; however, it has the following limitations : First, only databases such as Web of Science, Scopus , Google Scholar, and the University of Florida Digital Libraries were use d for the searches done. Hence, certain pertinent materials regarding Digital Twins and their application in the construction industry may have been omitted. As a result, it's possible that the results don't represent the breadth of the literature on D igit al T win use in the construction sector . It is possible that the analysis of literature will fall short of a comprehensive examination of Digital Twins due to subjective evaluations made by the researcher. No case study was specifically analyzed to obtain cost information as high level analysis of major implementation cost drivers were considered. Finally , considering the qualitative research strategy adopted and semi structured interview ing approach used , the evaluations obtained should be considered subj

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117 therefore not represent a more general typical situation. Future Studies Some of the limitations of this work pro vide interesting new avenues for future studies and should be taken into account when attempting to draw conclusions from this research. As no specific case study was examined in this work to obtain a cost analysis, it is recommended that future research c onsiders case studies analysis for Digital Twin applications in construction projects . The use of a variety of data sets and a wider range of literature in conducting these future studies are also recommended. Future studies may need to conduct a comprehen sive cost benefit analysis and/or develop a cost model leveraging the findings of this research such as the Digital Twin cost drivers , key considerations before adoption, and Digital Twin implementation guid ance for a Construction Project .

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118 APPENDIX SEMI STRUCTURED INTERVIEW QUESTIONS FOR DIGITAL TWIN IMPLEMENTERS Section 1: General Information Questions: 1. What does your organization do? ________________________________________________________________ 2. What specific role do you play in your organization? __________ ______________________________________________________ 3. How long has your organization been implementing Digital Twin and on how many projects? ________________________________________________________________ 4. What use cases have you applied the digital t win technology to and what were the objectives of your DT implementation? ________________________________________________________________ To what extent were these objectives achieved? Do y ou consider your DT implementation successful? Why was it successful/unsuccessful? ________________________________________________________________ ________________________________________________________________ 5. What is your estimated payback time or timef rame for ROI after DT implementation? What factors helped determine this payback timeframe? ________________________________________________________________ 6. Are you currently on track with the expected payback period? What is enabling/hindering the achievement of the expected payback? ________________________________________________________________ ________________________________________________________________ 7. What were the main enablers for effective DT implementation? ________________________________________________________________ 8. What were the main barriers to effective DT implementation? ________________________________________________________________ Section 2: Cost Related Questions: 2.1 What were the main tangible and intangible costs associated with your DT implementation for your use case(s)?

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119 (These may be identified as costs relating to R&D, or costs of switching from traditional work practice to DT systems. I ncurred costs related to labor and new hires, Cost of employee training in the use of DT tools, Cost of procuring information/operational technology tools (DT software, sensors, etc.) Operational costs including maintenance cost of DT related equipment and tools, legal fees, insurance, and data/cyber security). ______________________________________________________________________ ______________________________________________________________________ Please rank these from the highest to the lowest. Section 3: Benefit Assessment Questions: 3.1 What were the main benefits of DT implementation in your projects? These benefits may include but are not limited to: Cost savings and increased profits Realized project objectives. Time savings in form of accelerated work flows and reduced downtimes Improved product quality or business performance Improved internal and external collaborations. Opened new business processes opportunities. Observed productivity improvement. Improved business proactivity through predictive analysis ______________________________________________________________________ ______________________________________________________________________ Please rank these benefits from the highest to the lowest. Section 4: Cost benefit Relationshi p and Guidelines to Implementation: 3.2 What do you consider the main relationship between costs and benefits in your DT implementation? ___________________________________________________________________ ___________________________________________________________________ 3.3 What implementation guidance/guidelines would you recommend for potential implementers who are interested in applying Digital Twin for their Construction Project? ___________________________________________________________________

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120 LIST OF REFERENCES 2021 State of the Industry | ATD . (n.d.). Retrieved January 3, 2023, from https://www.td.org/state of the industry/2021 state of the industry Abdullah, F. (2004). Constr uction industry and economic development: the Malaysian scene . 138. https://books.google.com/books/about/Construction_Industry_and_Economic_ Dev el.html?id=tOq1AAAAIAAJ Akanmu, A. A., Anumba, C. J., & Ogunseiju, O. O. (2021a). Towards next generation cyber physical systems and digital twins for construction. Journal of Information Technology in Construction , 26 , 505 525. https://doi.org/10.36680/j.itcon.2021.027 Akanmu, A. A., Anumba, C. J., & Ogunseiju, O. O. (2021b). Towards next generation cyber physical systems and di gital twins for construction. Journal of Information Technology in Construction , 26 , 505 525. https://doi.org/10.36680/j.itcon.2021.027 Ammar, A., Nassereddine, H. , AbdulBaky, N., AbouKansour, A., Tannoury, J., Urban, H., & Schranz, C. (2022). Digital Twins in the Construction Industry: A Perspective of Practitioners and Building Authority. Frontiers in Built Environment , 8 . https://doi.org/10.3389/fbuil.2022.834671 Angjeliu, G., Coronelli, D., & Cardani, G. (2020a). Development of the simulation model for Digital Twin applications in historical masonry buildings: The integration between numerical and experimental reality. Computers & Structures , 238 , 106282. https://doi.org/10.1016/J.COMPSTRUC.2020.106282 Angjeliu, G., Coronelli, D., & Cardani, G. (2020b). Development of the simulation model for Digital Twin applications in historical masonry buildings: The integration between numerical and experimental reality. Computers & Structures , 238 , 106282. https://doi.org/10.1016/J.COMPSTRUC.2020.106282 Ariansyah, D., Fernàndez Del Amo, I., Erkoyuncu, J. A., Agha, M., Bulka, D., De, Digital Twin Development: A Step by Step Guideline . https://ssrn.com/abstract=3806372 Arup. (2019). Digital twin report . www.arup.com/digitaltwinreport Ashton, K. (2010). RELA TED C ONTENT RFID Powered Handhelds Guide Remains Committed Mobile RTLS Tracks Health care Efficiency RFID Journal rnet of RFID Journal . http://www.rfidjournal.com/article/print/4986 Azhar, S., Hein, M., & Sketo, B. (2011). Building information modeling (BIM): Benefits, risks and challenges .

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121 Bajaj, M., Zwemer, D., & Cole, B. (2016). Architecture to geometry Integrating system models with mechanical design. AIAA Space and Astronautics Forum and Exposition, SPACE 2016 . https://doi.org/10.2514/6.2016 5470 Bansal, V. K. (2018). Use of GIS to consider spatial aspects in construction planning process. Https://Doi.Org/10.1080/15623599.2018.1484845 , 20 (3), 207 222. https://doi.org/10.1080/15623599.2018. 1484845 Barima, O. (2016). Leveraging the Blockchain Technology to improve construction Value Delivery: The Opportunities, Benefits and Challenges . https: //www.researchgate.net/publication/311289392 Barnard Lucy, & Lescohier Jenny. (2021). California contractor shows what digital twins can realistically do for construction CONEXPO CON/AGG 365 News . News.Conexpoconagg.Co m. https://news.conexpoconagg.com/news/california contractor shows what digital twins can realistically do f or construction/4885.article Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019). A survey on digital twin: Definitions, characteristics, applications, and design implications. IEEE Access , 7 (Ml), 167653 167671. https://doi.org/10.1109/ACCESS.2019.2953499 Bielefeldt, B., Hochhalter, J., & Hartl, D. (2016). Computationally Efficient Analysis of SMA Sensory Particles Embedded in Complex Aerostructures Using a Substructure Approach. ASME 2015 Conference on Smart Materials, Adaptive Structures and Intelligent S ystems, SMASIS 2015 , 1 . https://doi.org/10.1115/SMASIS2015 8975 Paradigm. Organizacija , 50 (3), 193 207. https://doi.org/10.1515/ORGA 2017 0017 Bolton, A., Butler, L., Dabson, I., Enzer, M., Evans, M., Fenemore, T., Harradence, F., Keaney, E., Kemp, A., Luck, A., Pawsey, N., Saville, S., Schooling, J., Sharp, M., Smith, T., Tennison, J., Why te, J., Wilson, A., & Makri, C. (2018). Gemini Principles . https://doi.org/10.17863/CAM.32260 Boschert, S., & Rosen, R. (2016). Digital Twin The Simulation Aspect. Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and Their Designers , 59 74. https://doi.org/10.1007/978 3 319 32156 1_5 Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology , 3 (2), 77 101. https://doi.org/10.1191/1478088706QP063OA Bryde, D., Broquetas, M., & Volm, J. M. (2013). The project benefits of Building Information Modelling (BIM). International Journal of Project Management , 31 (7), 971 980 . https://doi.org/10.1016/J.IJPROMAN.2012.12.001 Chen, Y. (2017). Integrated and Intelligent Manufacturing: Perspectives and Enablers. Engineering , 3 (5), 588 595. https://doi.org/10.1016/J.ENG.2017.04.009

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122 Clifton, D. (2022). Digital Twin Software: Maximize Solutions and Benefits . Spaceiq.Com. https://spaceiq.com/blog/digital twin software/ Creswell David. (2009). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches John W. Creswell, J. David Creswell Google Books . https://books.google.com/books?hl=en&lr=&id=KGNADwAAQBAJ&oi=fnd&pg= PP1&dq=Creswell,+J.+W.+(2009).+Research+design:+Qualitative,+quantitativ e,+and+mixed+methods+approaches+(3rd+ed.).+Los+Angeles,+CA:+SAGE+ Publications.&ots=XDJi6e2lgW&sig=f0JL1JaUppQKFh7CSDx FzhFF 1s#v=onepage&q=Creswell%2C%20J.%20W.%20(2009).%20Research%20de sign%3A%20Qualitative%2C%20quantitative%2C%20and%20mixed%20meth ods%20approaches%20(3rd%20ed.).%20Los%20Angeles%2C%20CA%3A%2 0SAGE%20Publications.&f=false Creswell, J., & Poth, C. (2016). Qualitative Inquiry and Research Design: Choosing Among Five Approaches John W. Creswell, Cheryl N. Poth Google Books . https://books.google.com/books?hl=en&lr=&id=DLbBDQAAQBAJ&oi=fnd&pg= PP1&dq=Creswell,+J.+W.,+and+Poth,+C.+N.+(2016).+Qualitative+Inquiry+an d+Research+Design:+Choosing+Among+Five+Approaches.+Thousand+Oaks, +CA:+Sage+publications.&ots= hu218HUTx&sig=bpINzb0 9MdioezSIt6TwZNFn634#v=onepage&q=Creswell% 2C%20J.%20W.%2C%20and%20Poth%2C%20C.%20N.%20(2016).%20Quali tative%20Inquiry%20and%20Research%20Design%3A%20Choosing%20Amo ng%20Five%20Approaches.%20Thousand%20Oaks%2C%20CA%3A%20Sag e%20publications.&f=false Croatti, A., Gabellini, M., Montagna, S., & Ricci, A. (2020). On the Integration of Agents and Digital Twins in Healthcare. Journal of Medical Systems , 44 (9), 1 8. https://doi.org/10.1007/S10916 020 01623 5/FIGURES/2 wear prediction and pattern recognition using artificial neural network and DNA based computing. Jo urnal of Intelligent Manufacturing , 28 (6), 1285 1301. https://doi.org/10.1007/S10845 015 1155 0/FIGURES/19 David, J., Lobov, A., & Lanz, M. (2018). Lear ning experiences involving digital twins. Proceedings: IECON 2018 44th Annual Conference of the IEEE Industrial Electronics Society , 3681 3686. https://doi.org/10.1109/IECON.2018.8591460 Davila Delgado, J. M., & Oyedele, L. (2021). Digital Twins for the built environment: learning from conceptual and process models in manufacturing. Advanced Engineering Informatics , 49 , 101332. https://doi.org/10.1016/J.AEI.2021.101332 Dixit, S., Mandal, S. N., Sawhney, A., & Singh, S. (2017). Relationship between skill development and productivity in construction sector: A literature revie w. International Journal of Civil Engineering and Technology , 8 (8), 649 665. Easwaramoorthy, M., & Zarinpoush, F. (2006). INTERVIEWING FOR RESEARCH What is an Interview? www.fao.org/docrep/W3241E/

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123 European Construction Sector. (2021). European Construction Sector Observatory Country profile Denmark . EY analysis. (2021). Can a supply chain dig ital twin make you twice as agile? ey.com/mfg Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital Twin: Enabling Technologies, Challenges and Open Research. IEEE Access , 8 , 108952 108971. https://doi.org/10.1109/ACCESS.2020.2998358 Galorath Incorporated. (2022). Software Maintenance Cost Galorath . https://galorath.com/software maintenance costs/ Glaessg en, E. H., & Stargel, D. S. (2012). The digital twin paradigm for future NASA and U.S. Air force vehicles. Collection of Technical Papers AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference . https://doi.org/10.2514/6.2012 1818 Grieves, M., & Vickers, J. (2016). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary Perspectives on Complex Sy stems: New Findings and Approaches , 85 113. https://doi.org/10.1007/978 3 319 38756 7_4/COVER Grieves, M. W. (2005). Product lifecycle management: the new paradigm for enterprises. International Journal of Product Development , 2 (1 2), 71 84. https://doi.org/10.1504/IJPD.2005.006669 Grieves Michael. (2014). Digital Twin: Manufacturing Excellence through Virtual Factory Replication . GTtechnology white paper. (n.d.). TRANSFORM NOW! THE OPPORTUNITY COST OF DIGITAL INACTION . www.gutenberg technology.com Häkkinen, T., Kuittinen, M., Ruuska, A., & Jung, N. (2015). Reducing embodied carbon during the design process of buildings. Journal of Building Engineering , 4 , 1 13. https://doi.org/10.1016/J.JOBE.2015.06.005 Hämäläinen, M. (2021). Smart city dvelopment with digital twin technology . 291 303. https://doi .org/10.18690/978 961 286 362 3.20 Hardin, B., & McCool, D. (n.d.). methods, and workflows . 375. Retrieved August 10, 2022, from https://books.google.com/books/about/BIM_and_Construction_Management.ht ml?id=BSOsBwAAQBAJ Hochhalter, J. D., Leser, W . P., Newman, J. A., Glaessgen, E. H., Gupta, V. K., Yamakov, V., Cornell, S. R., Willard, S. A., & Heber, G. (2014). Coupling Damage Sensing Particles to the Digitial Twin Concept . http://www.sti.nasa.gov Ioannis Brilakis, A., Fischer Senior Fellow, H., Pan, Y., Borrmann, A., Mayer, H. G., Rhein, F., Vos, C., Pettinato, E., & Wagner, S. (2019). Built Environment Digital Twinning .

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124 Isidora, M. (n.d.). What is the average cost of trai ning a new employee? | eduMe . Retrieved January 3, 2023, from https://www.edume.com/blog/cost of training a new employee Jackson, R. L., Drummond, D. K., & Camara, S. (2007). What Is Qualitative Research? Https://Doi.Org/10.1080/17459430701617879 , 8 (1), 21 28. https://doi.org/10.1080 /17459430701617879 Jacobs, I., Singh, E., Sewell, K. L., Al Sabbagh, A., & Shane, L. G. (2016). Patient attitudes and understanding about biosimilars: an international cross sectional survey. Patient Preference and Adher ence , 10 , 937. https://doi.org/10.2147/PPA.S104891 Jiang, F., Ma, L., Broyd, T., & Chen, K. (2021). Digital twin and its implementations in the civil engineering sector. In Automation in Construction (Vol. 130). Elsevier B.V. https://doi.org/10.1016/j.autcon.2021.103838 Jick, T. D. (1979). Mixing Qualitative and Quantitative Met hods: Triangulation in Action. Administrative Science Quarterly , 24 (4), 602. https://doi.org/10.2307/2392366 Kaewunruen, S., & Xu, N. (2018). Digital twin for sustainability evaluation of railway station buildings. Frontiers in Built Environment , 4 . https://doi.org/10.3389/fbuil.2018.00077 Kannan, K., & Arunachalam, N. (2019). A Digital Twin for Grinding Wheel: An Information Sharing Platform for Sustainable Grinding Process. Journal of Manufacturing Science and Engineering, Transactions of the ASME , 141 (2). https://doi.org/10.1115/1.4042076/477555 Khajavi, S. H., Motlagh, N. H., Jaribion, A., Werner, L. C., & Holmstrom, J. (2019). Digital Twin: Vision, benefits, boundari es, and creation for buildings. IEEE Access , 7 , 147406 147419. https://doi.org/10.1109/ACCESS.2019.2946515 Kraft, E. M. (2016). The US air force digital thread/di gital Twin life cycle integration and use of computational and experimental knowledge. 54th AIAA Aerospace Sciences Meeting , 0 . https://doi.org/10.2514/6.2016 0897 Kri tzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC PapersOnLine , 51 (11), 1016 1022. https://d oi.org/10.1016/J.IFACOL.2018.08.474 Laaki, H., Miche, Y., & Tammi, K. (2019). Prototyping a Digital Twin for Real Time Remote Control over Mobile Networks: Application of Remote Surgery. IEEE Access , 7 , 20235 20336. https://doi.org/10.1109/ACCESS.2019.2897018 Labra, O., Castro, C., Wright, R., & Chamblas, I. (2020). Thematic Analysis in Social Work: A Case Study. Global Social W ork Cutting Edge Issues and Critical Reflections . https://doi.org/10.5772/INTECHOPEN.89464

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125 Lee, D., & Lee, S. (2021). Digital Twin for Supply Chain Coordination i n Modular Construction. Applied Sciences 2021, Vol. 11, Page 5909 , 11 (13), 5909. https://doi.org/10.3390/APP11135909 Lengthorn Paul. (2020). Cost to build a Digital Twin . https://www.consultengsurvivor.com/cost to build a digital twin Leviäkangas, P., Mok Paik, S., & Moon, S. (2017). Keeping up with the pace of digitization: The case of the Australian construction industry. Technology in Society , 50 , 33 43. https:/ /doi.org/10.1016/J.TECHSOC.2017.04.003 Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta analyses of studies that evaluate he alth care interventions: explanation and elaboration. Journal of Clinical Epidemiology , 62 (10), e1 e34. https://doi.org/10.1016/J.JCLINEPI.2009.06.006 Lin, Y. C., & Cheung, W. F. (2020). Developing WSN/BIM Based Environmental Monitoring Management System for Parking Garages in Smart Cities. Journal of Management in Engineering , 36 (3), 04020012. ht tps://doi.org/10.1061/(ASCE)ME.1943 5479.0000760 Liu, M., Fang, S., Dong, H., & Xu, C. (2021). Review of digital twin about concepts, technologies, and industrial applications. Journal of Manufacturing Systems , 58 , 346 3 61. https://doi.org/10.1016/J.JMSY.2020.06.017 Liu, W., Zhang, W., Dutta, B., Wu, Z., & Goh, M. (2020). Digital twinning for productivity improvement opportunities with robotic process automation: Case of greenfield hospital. International Journal of Mechanical Engineering and Robotics Research , 9 (2), 258 263. https://doi.org/10.18178/ijmerr.9.2.258 263 Liu, Z., Meyendorf, N., & Mrad, N. (2018). The role of data fusion in predictive maintenance using digital tw in. AIP Conference Proceedings , 1949 (1), 020023. https://doi.org/10.1063/1.5031520 Lu, R., & Brilakis, I. (2019). Digital twinning of existing reinforced concrete bridges f rom labelled point clusters. Automation in Construction , 105 , 102837. https://doi.org/10.1016/J.AUTCON.2019.102837 Macchi, M., Roda, I., Negri, E., & Fumagalli, L. (2018). Exploring the role of Digital Twin for Asset Lifecycle Management. IFAC PapersOnLine , 51 (11), 790 795. https://doi.org/10.1016/J.IFACOL.2018.08.415 M adni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging digital twin technology in model based systems engineering. Systems , 7 (1). https://doi.org/10.3390/systems7010007 Madubuike, O. C., Anumba, C. J., & Khallaf, R. (2022). A REVIEW OF DIGITAL TWIN APPLICATIONS IN CONSTRUCTION. Journal of Information Technology in Construction , 27 , 145 172. https://doi.org/1 0.36680/j.itcon.2022.008

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126 Marescaux, J., Leroy, J., Gagner, M., Rubino, F., Mutter, D., Vix, M., Butner, S. E., & Smith, M. K. (2001). Transatlantic robot assisted telesurgery. Nature 2001 413:6854 , 413 (6854), 379 380. https://doi.org/ 10.1038/35096636 Maresco, D. (2022). How To Measure Digital Twin Cost Plus A Look At ROI . Spaceiq.Com. https://spaceiq.com/blog/how to measure digi tal twin cost/ Matthews, J., Love, P. E. D., Heinemann, S., Chandler, R., Rumsey, C., & Olatunj, O. (2015). Real time progress management: Re engineering processes for cloud based BIM in construction. Automation in Const ruction , 58 , 38 47. https://doi.org/10.1016/J.AUTCON.2015.07.004 McMahon, C. (2022). The ROI of Digital Twin for Industrial Companies | PTC . https://www.ptc.com/en/blogs/corporate/roi of digital twin for industrial companies Mohammadi, N., Vimal, A., & Taylor E. John. (2020). Hawai i International Conference on System Sciences 2020. Negri, E., Fumagalli, L., & Macchi, M. (2017). A Review of the Roles of Digital Twin in CPS based Production Systems. Procedia Manufacturing , 11 (June), 939 948. https://doi.org/10.1016/j.promfg.2017.07.198 Nicholas D. Evans, M. R. (2022). Digital Twin Use Cases for Building Owners and Operators WGI . Https://Wginc.Com/Digital Twin Use Cases for Building Owners and Operators/. https://wginc.com/digital twin use cases for building owners and operators/ Opoku, D. G. J., Perera, S., Osei Kyei, R., & Rashidi, M. (2021). Digital twin application in the construction industry: A literature review. In Journal of Building Engineering (Vol. 40). Elsevier Ltd. https://doi.org/10.1016/j.jobe.2021.102726 Parmar, R., Leiponen, A., & Thomas, L. D. W. (2020). Building an organizational digital twin. Business Horizons , 63 (6), 725 736. https://doi.org/10.1016/j.bushor.2020.08.001 Pires, F., Cachada, A., Barbosa, J., Moreira, A. P., & Leitão, P. (2019). Digital Twin in Industry 4.0: Technologies, Applications and Challenges . Qi, Q., & Tao, F. (2018). Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison. IEEE Access , 6 , 3585 3593. https://doi.org/10.1109/ACCESS.2018.2793265 Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021a). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems , 58 , 3 21. https://doi.org/10.1016/J.JMSY.2019.10.001 Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, Y. C. (2021b). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems , 58 (PB), 3 21. https://doi.org/10.1016/j.jm sy.2019.10.001 Ríos, J., Hernández, J. C., Oliva, M., & Mas, F. (2015). Product Avatar as Digital Counterpart of a Physical Individual Product: Literature Review and

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127 Implications in an Aircraft. Advances in Transdisciplinary Engineering , 2 , 657 666. https://doi.org/10.3233/978 1 61499 544 9 657 Roest, M. (2021, February 16). (99+) The business case for digital twins | LinkedIn . https://www.linkedin.com/pulse/business case digital twins mark roest/ Rogers, P., Humble, R., Helman, Z., FUNNELL, S., Scougall, J., Hassall, K., Tyler, P., ELSWORTH, G., Kimberley, S ., STEVENS, K., & University, C. I. for R. R. (2008). Evaluation of the Stronger Families and Communities Strategy 2000 2004: Final report . RMIT University, CIRCLE. https://researchrepository.rmit.edu.au/esploro/outputs/report/Evaluation of the Stronger Families and Communities Strategy 2000 2004 Final report/9921861541001341 Roh, S., Aziz, Z., & Peña Mora, F. (2011). An object based 3D walk through model for interior construction progress monitoring. Automation in Construction , 20 (1), 66 75. https://doi.org/10.1016/J.AUTCON.2010.07.003 Roy, R. B., Mishra, D., Pal, S. K., Chakravarty, T., Panda, S., Chandra, M. G., Pal, A., Misra, P., Chakravarty, D., & Misra, S. (2020). Digital twin: current scenario and a case study on a manufacturing process. International Journal of Advanced Manufacturing Technology , 107 (9 10), 3691 3714. https://doi.org/10.1007/S00170 020 05306 W/FIGURES/26 Sacks, R., Brilakis, I., Pikas, E., Xie, H. S., & Girolami, M. (2020). Construction with digital twin information systems. Data Centric Engineering , 1 (6). https://doi.org/10.1017/DCE.2020.16 Scaglioni, B., & Ferretti, G. (2018). Towards digital twins through object oriented modelling: a machine tool case study. IFAC PapersOnLine , 51 (2), 613 618. https://doi.org/10.1016/J.IFACOL.2018.03 .104 Seshadri, B. R., & Krishnamurthy, T. (2017). Structural health management of damaged aircraft structures using the digital twin concept. 25th AIAA/AHS Adaptive Structures Conference, 2017 . https://doi.org/10.2514/6.2017 1675 Shafto, M., Rich, M. C., Glaessgen, D. E., Kemp, C., Lemoigne, J., & Wang, L. (2012). Modeling, SiMulation, inforMation technology & ProceSSing roadMaP Technol ogy Area 11 . Shchegolova, N., Talalai, V., Gorshenina, C., & Smirnova, D. (2020). Software modeling application for verification of transportation planning engineering hypotheses. IOP Conference Series: Materials Science and Engineering , 832 (1), 012055. https://doi.org/10.1088/1757 899X/832/1/012055 Silverman. (2006). Qualitative Research All Issues . https://journals.sagepub.com/loi/qrj Snyder, H. (2019). Lit erature review as a research methodology: An overview and guidelines. Journal of Business Research , 104 , 333 339. https://doi.org/10.1016/J.JBUSRES.2019.07.039

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128 sourceforge.net. (2022). Best Digital Twin Software of 2023 . https://sourceforge.net/software/digital twin/ Statista. (2015). GDP share 2017 | Statista . Statista.Com. https://www.statista.com/statistics/605372/china construction industry gdp share/ Suescún, L., Sanchez, E., Gómez, M., Garcia Arias, F. L., & Núñez Zarantes, V. M. (2016). The state of construction e Business in the UK . 53. Sun, S., Pan, W., & Wang, L. L. (2010). A Comprehensive Review of Effect Size Reporting and Interpreting Practice s in Academic Journals in Education and Psychology. Journal of Educational Psychology , 102 (4), 989 1004. https://doi.org/10.1037/a0019507 Tang, S., Shelden, D. R., Eastman, C. M., Pishdad Bozorgi, P., & Gao, X. (2019). A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Automation in Construction , 101 , 127 139. https://doi.org/10.1016/J.AUTCON.2019.01.020 Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data driven smart manufacturing. Journal of Manufacturing Systems , 48 , 157 169. https://doi.org/10.1016/J.JMSY.2018.01.006 Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019). Digital Twins and Cyber Physical Systems toward Smart Manufacturing and Industry 4.0: Co rrelation and Comparison. Engineering , 5 (4), 653 661. https://doi.org/10.1016/j.eng.2019.01.014 Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu , S. C. Y., & Nee, A. Y. C. (2018). Digital twin driven product design framework. Https://Doi.Org/10.1080/00207543.2018.1443229 , 57 (12), 3935 3953. https://doi.org/10.1080/00207543.2018.1443229 Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital Twin in Industry: State of the Art. IEEE Transactions on Industrial Informatics , 15 (4), 2405 2415. https://doi.org/10.1109/TII.2018.2873186 Teicholz, P. (2013). BIM for Facility Managers IFMA Google Books . https://books.google.com/books?hl=en&lr=&id=wHQ DghScf2YC&oi=fnd&pg=P T8&dq=bim+for+facility+managers&ots=N8RTOt1Gvf&sig=8ZkGIKkNJmW8Bg SgNtrHzzHnkk0#v=onepage&q=bim%20for%20facility%20managers&f=false theiet.org. (2022). Digital Twin for the Built Environment . https://www.theiet.org/impact society/sectors/built environment/built environment news/2019 news/digital twins for the built environment/ Trading Economics. (2022). GDP from Construction Countries List . Tradingeconomics.Com. https://tradingeconomics.com/country list/gdp from construction

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129 Tuegel, E. J., Ingraffea, A. R., Eason, T. G., & Spottswood, S. M. (2011). Reen gineering aircraft structural life prediction using a digital twin. International Journal of Aerospace Engineering . https://doi.org/10.1155/2011/154798 Uhlemann, T. H. J ., Lehmann, C., & Steinhilper, R. (2017). The Digital Twin: Realizing the Cyber Physical Production System for Industry 4.0. Procedia CIRP , 61 , 335 340. https://doi.org/10.1016/J.PROCIR.2016.11.152 Vanlande, R., Nicolle, C., & Cruz, C. (2008). IFC and building lifecycle management. Automation in Construction , 18 (1), 70 78. https://doi.org/10.1016/J.AUTCON.2008.05.001 Vishwakarma, R., & Jain, A. K. (2020). A survey of DDoS attacking techniques and defence mechanisms in the IoT network. Telecommunication Systems , 73 (1), 3 25. https://doi.org/10.1007/S11235 019 00599 Z/TABLES/5 Vivi, Q. L., Parlikad, A. K., Woodall, P., Ranasinghe, G. D., & Heaton, J. (2019). Developing a dynamic d igital twin at a building level: Using Cambridge campus as case study. International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data Informed Decision Making , 67 75. https://doi.org/10.1680/ICSIC.64669.067/ASSET/IMAGES/SMALL/ICSIC.6466 9.067.F6.GIF Understanding the adde d value of integrated models for through life engineering services. Procedia Manufacturing , 16 , 139 146. https://doi.org/10.1016/J.PROMFG.2018.10.167 Wang, J. W. , Gao, C., Dong, S., Xu, S., Yuan, C. W., Zhang, C., Huang, Z. bin, Bu, S. S., Chang, Q., & Wang, Y. (2020). Current Status and Future Prospects of Existing Research on Digitalization of Highway Infrastructure. China Journal of Highway and Transport , 33 (11 ), 101. https://doi.org/10.19721/J.CNKI.1001 7372.2020.11.010 West, T. D., & Blackburn, M. (2017). Is Digital Thread/Digital Twin Affordable? A Systemi Procedia Computer Science , 114 , 47 56. https://doi.org/10.1016/j.procs.2017.09.003 White, G., Zink, A., Codecá, L., & Clarke, S. (2021). A digital twin smart city for citizen feedback. Cities , 110 , 103064. https://doi.org/10.1016/J.CITIES.2020.103064 Xiong, M., & Wang, H. (2022). Digital twin applications in aviation industry: A review. International Journal of Adva nced Manufacturing Technology , 121 (9 10), 5677 5692. https://doi.org/10.1007/S00170 022 09717 9/TABLES/4 Yang, J., Zhang, W., & Liu, Y. (2013). Subcycle f atigue crack growth mechanism investigation for aluminum alloys and steels. 13th International Conference on Fracture 2013, ICF 2013 , 3 , 2010 2019. https://doi.org/10.2514/6.2013 1499 Zakrajsek, A. J., & Mall, S. (2017). The development and use of a digital twin model for tire touchdown health monitoring. 58th AIAA/ASCE/AHS/ASC Structures,

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130 Structural Dynamics, and Materials Conference, 2017 . https://doi.org/10.2514/6.2017 0863 Zhang, H., Liu, Q., Chen, X., Zhang, D., & Leng, J. (2017). A Digital Twin Based Approach for Designing and Multi Objective Optimization of Hollow Glass Production Line. IEEE Access , 5 , 26901 26911. https://doi.org/10.1109/ACCESS.2017.2766453 Zheng, Y., Yang, S., & Cheng, H. (2019). An application framework of digital twin and its case study. Journal of Ambient Intelligence and Humanized Computing , 10 (3), 1141 1153. https://doi.org/10.1007/S12652 018 0911 3/FIGURES/10

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131 BIOGRAPHICAL SKETCH Christian Abugu was born and raised in Enugu City, a Southeastern part of Nigeria. He attended Federal Government College, Enugu where he dev eloped interests in mathematics, engineering, technical drawing, and eventually construction. Upon completion of his high school in 2008, he went to obtain his Bachelor of Engineering from the prestigious University of Nigeria, Nsukka campus, Enugu Nigeria from the D epartment of Civil Engineering. After graduation in 2013, he gained over 5 years of hands on experience working first as a Surveyor for JLG Developers Construction Company, Site Engineer for JCD Limited, and Maintenance/Facility Manager for, Nex t Cash and Carry Limited, all in Nigeria. His passion for construction propelled him to leave Nigeria to pursue his master's degree in University of Florida. He received an assistantship to work for the School of Design, Construction, and Planning, Univers ity of Florida while he majored in Construction Management. He soon developed interest in using technology for construction project improvement and decided to focus on the Digital Twin as his research area. The nascent nature of the Digital Twin concept re vealed opportunities for research for Christian as he investigated the Digital Twin potential benefits for Construction projects, costs, and implementation guidelines for future users in construction industry. He receive d his Master of Science in Const ruction Management in Spring of 2023 and join ed a GC firm where he contribute s to construction project improvements in the US.