Keywords

1 Introduction

On average, the construction sector contributes about 8–10% to the economy of various countries, encourages growth, offers mass employment, and serves as a means of connecting other industries and the economy [19]. The industry drives growth by facilitating services and goods between sectors [7]. The worldwide construction business produced more than $10 trillion in 2017 [10].

Construction is one of the most informative businesses where information needs to be immediate, accurate, thorough, timely, and in a prominent style that the recipient understands [78]. Throughout the life cycle of a construction project, massive amounts of data are generated, from conceptual planning to decommissioning. The ability to manage the flow of information, evaluate the massive amount of data, and derive relevant insights is critical to project success [12].

Although the construction sector is often criticized for being conservative in technological breakthroughs and applications, it has made tremendous strides in improving information management through Building Information Modeling (BIM) in the last few decades [51]. According to Arayici et al. [6], the traditional paradigm of the construction industry has shifted from 2D-based drawings to 3D-based information systems using BIM. BIM has been an effective innovation tool for more than a decade to address building design holistically, strengthen communication and collaboration among key stakeholders, increase productivity, improve overall product quality, reduce fragmentation, and improve efficiency in the construction industry [67, 72].

While comprehensive research studies have been undertaken to study BIM’s use cases and analyze its benefits over the life cycle of a construction project, BIM does not capture data created throughout the operational and usage phase [35]. BIM is faced with substantial issues, with Big Data, Internet of Things (IoT), and Artificial Intelligence (AI) being touted as viable answers to automate and incorporate broader environmental conditions [13]. Thus, in an era of rapid change [11], BIM’s evolution ought to be precisely planned within a framework that encompasses all participants and technology [29]. Construction needs to incorporate new technologies as part of the fourth wave of technological innovation (Industry 4.0) [31]. Digital Twin (DT), a digitalization technique used to monitor and improve the operational efficiency of a physical asset by collecting real-time data that enables predictive maintenance and leads to well-informed decision-making, is a crucial component of the Industry 4.0 roadmap [35]. Investments in DTs are expected to balance with increased productivity as a result of predictive analytics [40] or even the supply of value-added services [73]. However, these objectives are frequently not achieved through the collection of data alone but rather through the use of data-driven decision-making [17]. Therefore, this chapter aims to review the use of DTs in construction projects and provide an overview of how to increase their use to improve the quality and productivity of construction projects.

2 The Concept and Origin of DT

Dr. Michael Grieves of the University of Michigan presented what he dubbed the Conceptual Ideal for Product Life Cycle Management (PLM) in 2002, and the notion of DT was born [32]. The PLM idea, which includes all parts of the DT, assumes that each system comprises two systems: a physical system that has always existed and a virtual system that stores all the information linked to the physical system. Since these two systems are interconnected, information can move between the physical and virtual worlds [23]. Other studies have also provided a simplified definition of DT. For example, according to Tao et al. [73], the idea and concept of DT are made up of the physical product, the virtual product, and the connected data that connects the physical and virtual products. The physical space, the virtual space, and the connected data are the three components of the many DT definitions. The twin concept was first used in NASA’s Apollo space program. The initiative created two identical spacecrafts so that conditions in space could be mirrored, simulated, and predicted by the vehicle on Earth. The vehicle that stayed on the base was the spacecraft’s twin that completed the mission [15]. Hernandez and Hernandez’s phrase “DT” was first used in their study [16]. According to Schleich et al. [64], NASA provided the first formal definition of DT in draft version of its technological roadmap in 2010 [59, 68]: “an incorporated multi-physical science, multiscale, the probabilistic reenactment of an as-built vehicle or framework that utilizes the best accessible actual model, sensor refreshes, armada history, and so forth, to reflect the existence of the comparing flying twin.”

3 Level of Integration and DT Paradigm

In any situation, one might establish a common understanding of DTs as digital counterparts of physical items based on the above definitions of a DT. The phrases digital model (DM), digital shadow (DS), and DT are frequently used interchangeably in these descriptions. However, the extent of data integration between the physical and digital counterparts differs between the offered descriptions. Some digital representations are created by hand and have no physical connection to the real world, while others are fully linked with real-time data exchange [38]. As a result, the authors propose that DTs can be divided into three subgroups based on their amount of data integration.

A DM is a digital depiction of an existing or projected physical object that does not include any automated data transfer between the physical and digital objects. A more or less detailed description of the physical object could be included in the digital depiction [38]. Suppose there is also an automated one-way data flow between the state of an existing physical object and the state of a digital item. In that case, this combination is referred to as DS, according to the concept of a DM. A change in the physical thing causes a change in the digital item, but not the other way around. It is referred to as a DT if the data flow between an existing physical thing and a digital entity is fully integrated with both directions. The digital item could likewise operate as a controlling instance of the physical thing in this scenario.

Figure 1 shows the data flow, where (a) mentions the data flow in DM, (b) mentions the data flow in DS, and finally, (c) presents the data flow in DT.

Fig. 1
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Data flow in DM, DS, and DT (adapted from: Kritzinger et al. [38])

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The DT paradigm necessitates a more significant level of detail and accuracy from unassuming manufactured resources, buildings, city locale, and ultimately statewide DTs [14]. Grieves [23] proposed a DT method that provided a holistic perspective on the complex system that a DT represents. Therefore, the primary DT units considered here, as illustrated in Fig. 2, are physical components, virtual clones, and data that connect each other.

“Data” in its different forms provide the connection loop between the system’s “Virtual-Physical” duality. Such as, Grieves [23] recognizes data from “Physical” to “Virtual” to be raw and in need of processing. In contrast, data from the “Virtual” to the “Physical” are dependent upon a few changes that can be handled data and put away information through digital models—with higher levels of significance. Nevertheless, the data are eventually reflected in the “Physical” via actuators. As a result, the “Physical” portion collects real-world data before sending it to be processed. In exchange, the “Virtual” part uses its embedded engineering models and AI to find information used in the “Physical.”

Fig. 2
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DT paradigm (adapted from: Boje et al. [13])

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4 The Technologies for Applying DT

DT applications use a variety of data-related, high-devotion displaying, and model-based simulation advancements. Sensors, radio-frequency identification (RFID) tags, gauges, readers, scanners, cameras, and other data-related technologies are used to create data, which are the foundation of the DT. These gadgets generate massive amounts of unstructured, semi-structured, and structured data regularly. Because transmitting the relevant data to the DT in the cloud server is complex and expensive, edge computing preprocesses the data acquired. 5G technology is used to exclude the chance of data leaks and provide real-time data communication [44]. He et al. [28] explored how IoT and signal processing algorithms and approaches can be used in DTs to collect real-time data from many sources.

The adoption of high-fidelity modeling technologies necessitates the construction of DT models. Physical or semantic data models can be used to create DT models [44]. Semantic data models are educated using AI approaches using known data sources and yields, while actual models require an exhaustive comprehension of their actual characteristics and current connections. A multi-physics approach is required for the high-fidelity simulation of DTs [44]. Negri et al. [52] created simulation modules that mimic particular industrial equipment behavior. The primary simulation model used standard interfaces to connect with the modules and facilitated simulation throughout the production system’s life cycle [54]. As a result, DT can be constructed using various modeling levels.

Simulation is an unavoidable feature of DTs when model-based simulation technologies are used. The DT simulation allows the virtual model to interact with the physical entity two-way in real-time. Schroeder et al. [66] recommended using Automation Markup Language to design attributes connected to the DT to determine the two-way interaction with the physical side. The model proved effective at sharing data among the DT’s many systems [54]. Despite the gradual reception of DT in the assembling industry, the idea’s contextualization in different areas, such as the construction sector, is as yet in its early stages [53]. As a result, significant efforts should be made to use DT in the construction sector to address the industry’s complicated difficulties.

Most of the technology used in the industry could be affected by DT. For instance, as-built BIM for facilities’ management [75], designed to offer information on the condition of buildings when they are commissioned, cannot offer an updated depiction of the building’s present state, and DT can help with this situation. Several data-collecting technologies create information used in isolation for construction monitoring [54]. There are only a few circumstances in which more than one technology is used. The difficulty is maintaining a unified and integrated strategy in which numerous monitoring systems may feed data into a project data to serve numerous administrative activities. DT can propose a comprehensive and incorporated utilization of these advances to give a fruitful method of construction managing and controlling.

5 DT Implementations in the Construction Sector

As interest in DT grew, the construction sector began to follow suit in this area. While DT and BIM definitions may appear identical, construction experts have pointed out the differences between the two ideas. Although BIM and DT have certain similarities, according to Khajavi et al. [35], they differ in several aspects, including the objective, technology, end-users, and facility life stage. In the body of knowledge of construction, the applications of BIM have been thoroughly researched. The architects and engineers do not operate BIM with real-time data [35]. However, they use BIM to perform clash detections and material simulation during the project design phase and contractors to perform production controls, construction analysis, site management, and security management [76].

On the other hand, by analyzing real-time parameters, the DT monitors the physical asset and optimizes its operational efficiency [35]. For example, a building’s DT can be utilized for operation and maintenance by allowing facility managers to undertake what-if analysis, improving energy efficiency and inhabitants’ comfort [35]. The data gathered by a DT during the facility’s operation and maintenance phase could be recorded in a database, and architects would use it on future designs [60]. Most of the DT uses in construction are in the facility’s operation and maintenance phase, whether the project is residential or industrial. The phrase “DT” is not explicitly addressed in most articles and is sometimes referred to as BIM or BIM-based facility management system (BIM-FM),existing literature on DT in construction is challenging to find [31].

However, from the notion began to shift from its contextualization stage to an initial stage in the construction business, there has been a constant rise of 191 papers in 2020 and 226 in 2021. Figure 3 shows that the increase of research papers focused on the DT within the construction field depends on research done through Google Scholar using keywords such as construction digital twin, digital twin and construction, and digital twin technologies within construction sector. Researchers have begun to look into the real-world uses of DTs in the construction sector. Some of the publications covered more than one step of the lifespan and were thus counted multiple times in each phase [54]. The majority of DT implementations in the construction sector were concentrated on a single life cycle phase. Investigators were preoccupied with the architectural and construction part of the project, but they overlooked the use of the DT concept throughout the demolition and recovery phase.

Fig. 3
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Trend of DT publications in the construction sector

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6 DT Application in the Life Cycle Phase of Construction Projects

The initiation, design, implementation, operation and maintenance, and demolition of construction projects are all parts of the life cycle. A holistic approach is required to manage the project lifespan from design to maintenance, operation to recycling, logistics to monitoring [21, 47, 58, 80]. Throughout the project lifespan, data are abundant. As a result, massive amounts of data gathered from design, production, procurement, resource management, logistics, utilities, and maintenance data sources have a ton of potential for further developing structure life cycle management operations as far as predictive and preventive information feed [34, 58]. With ongoing data use bringing about excellent, further developed precision, unsurprising cycles, superior execution, further developed information concentrated frameworks and services, and different advantages, extensive data might turn into the establishment of construction organizations’ upper hand in expanding proficiency in design, production, operation, and maintenance [56, 69].

6.1 Design and Engineering Phase

The iterative optimization of the models shortens the whole design phase and eliminates the risk of extra costs during rework [41]. A construction project progresses through a series of stages [25]. Inception, brief, design, and engineering are all included in the design and engineering stage [20].

By permitting data to be added, changed, and checked against real-life scenarios, BIM models aid in the resolution of problems among various construction parties and decrease conflict among project stakeholders [62]. BIM use has seen significant advancement in terms of maturity in technology, process, and policy [72]. Hardin and McCool [27] stated that in addition to 3D models, BIM requires remarkable changes in project delivery and workflow operations. For DTs, BIM can allow visual, three-dimensional communication. The use of the sensors that monitor and collect data and BIM together creates an active model that can be used to implement DT in the construction industry [42]. The point at this issue supplies designers with helpful knowledge during the project’s design. Designers can use DTs to get a complete digital footprint of a project and make informed judgments [74]. Data gathered with DT can be saved in a dataset and utilized by architects in ensuing ventures [60]. Collected data can aid in material and supplier selection, energy and supply chain management, and others. Furthermore, early design decisions regarding project feasibility, sustainability issues, and more topics could be informed using BIM and serve as guidelines for pre-construction [30].

Lin and Cheung [42] proposed advanced monitoring and control systems for underground garage environment management using BIM and Wireless Sensor Network (WSN) technologies. WSN has been used to monitor and record physical conditions, and they discovered that their suggested system was an effective system for environmental monitoring. Lu et al. [45,46,47] argued the use of semi-automatic geometric digital twinning depends on images and CAD drawings for existing buildings. Moreover, a case study on a portion of an office building has been conducted. They discovered that DT-conducted applications are a practical approach in the building’s operations phase. It was also explored the gap between geometric digital twinning and existent structures. For the initial product development and study of multiple design ideas, the authors used high-resolution models. Using a DT help with digital fabrication planning has been discovered by the study as well.

6.2 Construction Phase

The construction phase, also known as the production phase, is where the finished product is created. The majority of studies using DT technology throughout the project’s construction period has zeroed in on deciding the object’s primary framework respectability. The notion of the DT is utilized to analyze the structural system integrity in historical masonry buildings [4]. The authors created a simulation model for a historic masonry building for DT applications. The study found that employing DT technology, structural behaviors at various stages of the building may be easily comprehended. In addition, the authors found that DT models, particularly in complicated portions of the masonry building, can be regularly updated utilizing the knowledge gained. At the point when forces are applied to the structure, the object’s structural systems guarantee that the item does not fizzle [54]. For example, significant damage to the Milan Cathedral was reported throughout the last century, demanding significant restoration efforts [50].

Shen et al. [70] demonstrated a system integrating facility life cycle information via an agent-based web service. The goal of this approach was to use data collected throughout the project life cycle, from planning to design to construction, to help facility managers make better decisions. BIM and real-time asset tracking and real-time assessment monitoring technologies like wireless sensors and RFID were used to create the suggested information integration framework. It is worth noting that this integrated method has been successfully used in two industrial projects, however, the authors were unable to publish the results due to the nature of the industries.

6.3 Operation and Maintenance Phase

The project is typically out of the constructor’s control during the operation and maintenance phases. As a result, managing and gaining access to the object’s data become complicated. The virtual model could be a replica of the thing, but it has no connection to the actual project [3]. At this stage of the project, the project’s users are concerned about its reliability and convenience. Several stakeholders are involved in the project, which prohibits data from being integrated between processes and stakeholders. DT can improve information flow between diverse stakeholders. DT is used in monitoring, logistics operations, facilities, and energy management during the project’s operation and maintenance phase.

Lin et al. [43] developed a novel mobile-automated BIM-FM for usage by facility management technicians during the operation and maintenance phases. A commercial building project was used to test the mobile BIM-FM system. The results confirmed the system’s performance, opening the door for facility management workers to be more efficient and more accessible data updates from facility management to the BIM environment. Peng et al. [57] investigated an airport terminal and demonstrated the value of combining data mining, data analysis, and BIM for building operation and maintenance. Using BIM and sensor data in facility operation and maintenance provides a massive amount of data that facility managers may examine. Facility managers are frequently confronted with increasingly non-intuitive datasets as well as error-prone human data entry, posing a variety of issues. The authors proposed a BIM-based data mining approach to evaluate the accumulated data, extract essential rules and patterns, and detect erroneous records to address this issue. The BIM database is initially connected to a data warehouse in this proposed approach. The database is then cleaned using three different data mining methods: cluster analysis to uncover associations of similarity among records, outlier identification to clean the database, and advanced pattern mining algorithm to find deeper logic links among records. Rather than going over an overwhelming number of individual entries, facility managers deal with a few high-quality data records. Chen et al. [18] proposed a framework for automating maintenance work orders to improve Facility Maintenance Management (FMM) and decision-making. The FMM framework was built by providing an IFC extension for maintenance tasks that linked data from BIM and Facility Management Systems (FMSs). When BIM and FMS data were combined, component errors were shown, and geometrical and semantic information about the dialed component could be derived from BIM models. A modified Dijkstra algorithm can be used to construct the maintenance work order schedule automatically. The algorithm considers four factors: the type of problem, the level of emergency, the distance between complements, and the location. The suggested framework’s viability was tested in both indoor and outdoor 3D situations.

Kaewunruen and Lian [34] demonstrated the 6D BIM for managing the life cycle of a railway turnout system. The authors used Revit-2018 software to create a 3D model of the system and discovered that the 6D aims at carbon footprint throughout the whole project life. According to the findings, DTs can be used to visualize and prioritize maintenance alternatives. Antonino et al. [5] found that real-time and historical data on usage of a building are precious to building managers. They can improve building maintenance and services as well. The authors employed image recognition to monitor people’s walks in an office building and provide real-time usage statistics. The authors discovered that real-time data on the flow of individuals traveling through a monitored location could be used to create smart contracts. On the other hand, the authors found no problems in integrating real-life data collected with image sensors to the BIM model.

Moreover, in various case studies, the authors did not test the suggested approach to improve their application in facilities management. By creating DTs for bulk silos, Greif et al. [22] looked into possibilities for construction site logistics. They created and deployed a decision-making system for silo replenishment and dispatch. The authors discovered that a successful decision assistance system requires a structural and visual display of insights. According to the study, silos can serve as both transport units and temporary storage facilities, allowing products to be transported to large urban areas for free.

6.4 Deconstruction and Recovery Phase

Researchers usually disregard the inactive phase when the facilities lose their functions [44]. In terms of using DT technology in the construction sector, the deconstruction and recovery phase, identical to the inactive phase, has also been overlooked. Knowledge of an object’s behavior is generally lost during the deconstruction and recovery phase [54]. Grieves and Vickers [24] stated that as the things may share similar qualities, knowledge about the precursor of the next generation of the object might be utilized to tackle comparable issues that would arise. Liu et al. [44] stated that a low cost might be maintained in the virtual environment because the deconstruction and recovery phase provides information on the complete life cycle phases. Barazzetti et al. [9] described how to create detailed Historic Building Information Modeling (HBIM) utilizing augmented reality and virtual reality to increase user interest in cultural tourism. Integrating DT and HBIM procedures helps improve data management efficiency. The combination of DT and HBIM processes can also assist building managers in identifying potential dangers, technical solutions, and possible measures to take, as well as their actual implementation, in terms of asset conservation [33].

7 DT for Construction

By superimposing perceived DT skills and features from neighboring domains onto construction site multidimensional BIM usage, Boje et al. [13] emphasized the significant considerations related to deploying and using a DT during the construction stage.

Table 1 lists some recurring themes in the literature related to DT, which are divided into three categories using the Virtual–Data–Physical paradigm. Each subpart is regarded as a feature or “ability” of the DT that is thought necessary to facilitate various services. These competencies are applicable throughout the building life cycle, but the technologies and techniques employed at each stage vary.

Table 1 Identified DT abilities and their roles within the virtual–data–physical paradigm (adapted from: Boje et al. [13])
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DT is considered to contain all “valuable” information throughout the whole product life cycle in the manufacturing industry [15]. This valuable information holds for building and infrastructure life cycles as well, but on a much bigger scale and with essentially different dynamics, it affects how the built environment is designed and managed through the use of digital assets [46]. The construction DT’s (CDT) capabilities are based on several processes and data layers aimed at facilitating smart construction services and applications [73]. These might benefit from DT integration on numerous levels, assuming that a solid framework is in place to accommodate the different heterogeneous systems and technologies encountered in research [63].

Current construction site detection efforts are restricted to routine laser scanning, manual management updates, and a variety of forms and papers human inputs. This hinders the ability of BIM and ultimate DT to accurately mimic and anticipate in terms of multidimensional modeling, as the information is out of date and out of sync with the physical twin [61].

The researched 4D BIM use cases demonstrated the emergence of trends and technology for capturing site data and automating BIM during construction. These technologies use a variety of site scanning procedures and reflect them in BIM [26, 37]. Several research projects have previously referred to the issue of interpreting the massive volume of data transferring through a building site to its digital model and the difficulties of fitting and retaining construction site sensors [1]. As a result, problems persist in verifying data (completeness and accuracy), appropriately interpreting it (using semantics), and processing it in a way that allows for real-time responses [13]. Automated site monitoring approaches would initially help site logistics [26] and safety [79].

Furthermore, visualization is crucial in the construction industry, as it is at the heart of intercommunication and decision-making. The utterance “drowning in data” [71] may be crossed with proven methods of visualization of data for managing projects by using actual, real-time feeds from multiple sources if holistic and enhanced real-time site surveillance is provided.

The dynamics of a construction site revolve around properly organizing work, keeping expenses within budget, and judiciously employing resources. Suppose that a more integrated complicated structure, the DT should dynamically update schedule and cost information in response to rapidly changing site operations, activate the appropriate estimating algorithms, and notify management by delivering timely warnings on interruptions and their likely causes [13].

Construction sites have long been seen as one of the most hazardous places to work. Various studies and professionals understand the benefits of adopting 4D modeling and believe that it has a built-in benefit for improving health and safety. However, given the way data are collected in the field, the process of applying safety management through systematic and relevant procedures with definite indicators is still absent. The existence of workers on-site, their numbers, and positions should be recorded by construction DTs. It could even catch potential anomalous behaviors like immotility, falls, or also monitor their exhaustion and attentiveness during dangerous activities [77], in addition to monitoring compliance with safety requirements.

Building practitioners are typically hesitant to adopt such innovations despite the added value since they cannot rely on the completion or validity of BIM data all through construction. The related personal endeavor required to achieve suchlike BIM utilization is a significant obstacle that automated detection can overcome. As a result, Boje et al. [13] deemed sensing along with semantic enrichment of BIM models as a foundation for n-dimensional clash detection simulations. Therefore, the DT would demonstrate the current situation and allow construction teams to execute alternative planning simulations, such as tasks, logistics operations, or equipment allocation plans.

Moreover, construction productivity is hampered by a lack of integration between on-site and off-site operations and supply-chain actors. While micromanagement may improve on-site daily operation, these tasks are also considered to be carried out if there are prerequisite tasks (including material/equipment delivery from off-site production systems) related to the entire supply chain. Lean construction approaches, such as the Last Planner System [8], typically rely on forms, data collecting from all parties, and planning ahead of time. On all levels, however, there is still a lack of practical information integration. Semantic DT applications promise the capacity to interconnect diverse datasets as well as connect various planning systems [36]. In such negotiation-intensive management approaches, AI may also provide value to human agents by counseling experts on optimal duration, sequencing, and other factors. Construction processes and related off-site and on-site resources should be modeled, tracked, and optimized using the Semantic DT [36].

As-planned BIMs can change drastically during the structure stage; for instance, determining equipment for a specific manufacturer might change during purchase orders, diminishing expenses, or guaranteeing that equipment is accessible inside the project’s timetable. Project supplier information, products, and order modifications can be linked over the web to assist in lessening the impact of such disruptions on production [13]. Additionally, transmitting a broad context of essential suppliers and items for use during future maintenance and prospective upgrades would improve the transition from handover to the operation of the supplied facility. As evidenced by the literature, web-based IoT integration is in demand across the board in the urban environment. As a result, the shift from construction to operation must consider the needs of the larger urban environment [2].

8 Value and Benefits

Boje et al. [13] suggested a development paradigm of the CDT. Other industry perspectives have been considered [14], laying out a five-step approach for developing a DT during the construction stage. However, due to the absence of application and research at specific degrees of complication, there is still a problem on understanding of the prospective technologies for higher levels. They believed that CDT implementation efforts should be gradual but constant throughout the life cycle of a building, considering supply chain integration and the complication of technologies used. Virtual models and sensing might eventually mix to establish a well-formed web platform. Adoption of traditional tools and formats is dependent on the application domains and current models, but it would be a crucial step toward interoperability and further life cycle phases. Implementing advanced types of AI is the last step that is predicted to happen after enough training and verification of AI behavior have been completed, this marks the transition of some tasks from human expert control and guidance to limited DT agency [13].

When examining the importance of a construction company’s value chain management, the gains of implementing a CDT must be thoroughly evaluated for each type of project (basis of scope, customer needs, procurement methods, etc.). In comparison, increasing its earnings and adding value to its clients also reduce its costs of implementation [13]. While BIM is a part of the initial procurement and demolition phases, CDT’s focus should be on building the “Physical Twin” during pre-construction and construction phases [48]. While BIM procedures and models can enhance cooperation with the application of uniform standards and size, the BIM paradigm for IoT and dynamic site data is restricted. A CDT presupposes cohesive, synchronicity-enhanced integration of models, sensors, and services. The implicit advantage would be gained in enhanced constructional services, which would enable the building processes to better assigning resources for activities that are completed better using robots, drones, and sensors and for those requiring human resources [13].

The value of DTs is quantified in the extra benefit to society they provide by supporting lower carbon emission and clean energy objectives [49]. The ability to adapt to the various systems that dwelling around the man-made environment is now a research challenge. Many intelligent building management systems, which must better adapt and react to inhabitant requirements while also optimizing resource consumption, are limited by the dynamics of human interaction with constructed assets [55]. Applying this to the construction site, a CDT should access all of the project’s data, understand the overall context, and provide relevant insights [39]. In addition, the use of the CDT, which varies according to the applications’ domain over the life cycle [65], should be possible for users of different social and educational backgrounds.

9 Conclusion and Limitations

Interoperability, inefficient integration, inadequate information management, operational issues, a lack of data in facility management, and hurdles to knowledge usage and management all afflict the deployment of DTs throughout the life cycle of a building project. This chapter provides an overview of the implementation of the DT in construction projects through their life cycle. The framework’s examination revealed that while the construction industry has made tremendous progress by moving beyond the digital model, the implementation of DT is still not fully realized in the industry. However, it may be stated that the study focus is currently shifting to DT. Since the inception of BIM, the construction industry has made tremendous progress, gaining enough awareness and momentum to permit a change from a static, closed information environment to a dynamic, web-based one that embraces IoT connectivity, and a higher degree of AI application. Increased automation and cohesion of information would help provide better construction services.

The impact of DTs on the construction sector has been demonstrated in this review through the many life cycle phases of the object. DTs offer the possibility of proactively addressing problems before they arise. The use of DTs during the design and engineering stages can aid in determining elements and information that should be acquired or wiped out during the object’s redesign and re-engineering. Further studies will necessitate in-depth investigation into the use of DTs during the design and engineering phases of a construction project. In the construction phase, the job of the DT is to minimize construction costs efficiently and effectively while also improving quality, which is something that the old method cannot deliver. The structural system completeness of the entity during the implementation phase has been the topic of some extant literature. More research into stakeholder management, quality management, cost management, and value management is required during the project’s building phase. In the long term, the building project stakeholders will benefit from DT’s application to an intelligent project life cycle management by innovative and lean building processes. Furthermore, the chapter specifies the number of DT capabilities or characteristics that allow for real-time, web-based, intelligent CDTs.

This study has some limitations despite its contributions. Only the databases Scopus, Web of Science, and ScienceDirect have been used for organizing the research. Despite the rigorous selection of relevant publications, it can be possible that not all keywords were captured in the literature search. Subjective judgments may have influenced the selection of relevant articles and the identification of applications in the various life cycle phases during the literature study. The abovementioned limitations provide fertile ground for future study and should be addressed when evaluating the research findings. This study suggests using a wider range of datasets and conducting more comprehensive literature research.

Future studies should analyze the construction industry’s readiness to integrate digital twins into its operations completely. This will increase practitioners’ grasp of the idea of digital twin. Additionally, future studies should look at the crucial success factors and challenges to effective digital twin adoption in the construction sector. This will increase the urge to use digital twin to solve the construction sector’s difficulties. Furthermore, this study investigated the use of digital twins in the various life cycle phases of a construction project. This can help practitioners grasp and accept the notion of digital twins. However, further study is required to investigate the potential uses of digital twins.