Although the ample benefits of Industry 4.0 have been proven, and industries such as automotive manufacturing and maintenance are focusing on the interaction between industry elements and IoT devices, the construction industry is lagging in the implementation of Industry 4.0 technologies such as Digital Twin (DT) [1,2]. In the embracing of Industry 4.0 concepts, Construction 4.0 initially relied on the extensive application of Building Information Modeling (BIM) in different stages of the product/asset lifecycle, while subsequently focusing on different areas of innovation such as industrial modular production, cyber-physical systems (CPS), supply chain and construction site works monitoring and data analytics including big data, Artificial Intelligence (AI), cloud computing and blockchain [3,4]. In addition to BIM, Digital Twin (DT), as one of the main concepts of Industry 4.0 and a subset of a CPS, bespeaks a new paradigm in the construction industry as a real-time virtual replica of a physical asset.

Construction companies are seeking to adopt new technologies to increase their profits and add value for their customers. Particularly in a context where construction project complexity is growing and coupled with the need for higher productivity, innovative solutions for tackling such challenges, such as Industry 4.0 technologies, are in demand [4]. Hence, a general contractor can benefit from a Construction Digital Twin (CDT) during its construction activities to increase its profits, decrease risks and also increase its customer satisfaction at the delivery and hand-over stage.

Industry 4.0 is a collective term for a number of building blocks consisting of Internet of Things (IoT), Big Data, Cloud Computing, the Internet of Services, Cyber-Physical System (CPS), Smart Factories, Advanced Manufacturing, Digital Twin, etc. [5,6,7]. The essential building blocks of Industry 4.0 are cyber-physical systems (CPS) that link the digital and physical worlds through networks and computational resources [2,7].

As a specific form of a CPS, Digital Twin aims to provide a digital replica of the physical product or process in real time or near real time and capture all useful information throughout the product or process’s lifecycle [6,8]. Serving as the virtual and computerized counterpart of a physical system, DT enables the simulation and real-time synchronization of the sensed data from the field acquired via enabling technologies of Industry 4.0 such as IoT [5].

Grieves and NASA provided the first definitions of Digital Twin. Although there is no universally accepted definition of Digital Twin due to different viewpoints, researchers and institutions have provided broader definitions of Digital Twin [6,9]. Table 1 provides various definitions provided in the literature with respect to their associated industry.

A close look at the provided definitions leads to the understanding that, regardless of the industry type, a DT is basically composed of a physical entity, a virtual entity that represents its respective physical peer and a data link to couple these two to capture the status of the physical entity. Moreover, the various definitions provided in Table 1 imply the spread of the DT in different industries, including construction and the built environment.

Increased interest in the Digital Twin has encouraged the construction industry to follow this concept. However, in the AEC/FM industry, the DT has not been defined comprehensively by both academia and practitioners, and it is often confused with BIM models for design, construction and operation/maintenance [9,12,24]. Unlike BIM, which reflects the states of a project in a static manner, Digital Twin is dynamic and holds a bi-directional connection with the physical asset and captures its status in real time or near real time.

In the construction industry, improving productivity, sustainability, safety and achieving other organization or project goals are the key purposes of a DT [25]. Infrastructures, the built environment and city assets can benefit from the applications of DT in monitoring, managing and predicting an asset’s current and future status. For instance, Pan and Zhang [18] proposed a Digital Twin framework integrating BIM, IoT and data mining techniques for a more efficient project management. Lu et al. [1] presented a system architecture to implement the DT at building and city levels, focusing on Facility Management (FM). Ham and Kim [26] worked on the DT at the city level by proposing a method for leveraging unstructured crowdsourced visual data for locating objects in urban areas that are vulnerable and have potential risks for citizens. A number of researchers conducted studies in the area of the DT in the construction industry [2,4,27,28,29,30,31,32] and, as Boje et al. [21] and Wanasinghe et al. [9] revealed, using DT in various industries, especially the construction industry, has gained momentum in the recent years.

In the product manufacturing stage, DT can realize real-time product monitoring and accurately predict performance. In addition, with the utilization of DT, the consistency of the final product with the required specifications can be evaluated at this stage [33]. In this sense, the building products constructed, fabricated and installed by the general contractor can be monitored and checked against the required performance level by using a DT. Having said that, the DT can verify or predict the effective performance of the building component in real time, which can be the basis for quality check assessments and, subsequently, payments to the general contractor. Monitoring the building components, workers and construction equipment is a challenge during the construction phase. Therefore, the DT approach for the construction stage can often be highly desirable since it enables actionable knowledge and effective decision making in the construction phase based on real-time data and measured productivity and performing “what-if” scenarios, which in turn reduces the construction waste (in the broad sense of time, effort, materials) to a great extent [3,9,12,34].

Although the design and construction phases of a project account for a considerable portion of the total project’s cost (up to 40 percent), most construction industry studies and practices have focused on the implementation of the DT in the operation and maintenance phase of facilities [35] and the level of DT development during construction is still very low [21]. Considering the substantial impact of the design and construction processes of an asset on its operation costs [21], any benefit from the DT during construction will have added value. In addition, the gathered data from the current monitoring technologies (e.g., range finders, laser scanning, GPS, RFID, Wi-Fi, UWB, smart sensors, etc.) in the construction industry are generally used in an isolated fashion with a single-subject focus where there are very few cases of the integrated use of more than one technology [3]. There is also a lack of clarity on the potential technologies for higher levels of CDT in terms of integration with socio-technical platforms and using simulation, optimization, learning and end-user engagement, mainly due to a lack of implementation and research at such levels of sophistication [21].

Sacks et al. [3] focus on developing a Digital Twin Construction workflow for the design and construction processes of a product. Depending on the nature of the contract and the project delivery system (Design Bid Build, Design Build, etc.), a general contractor might or might not be engaged with the design process. In addition, in many processes, such as construction supply management and construction equipment management, the general contractor intends to control and improve its internal processes rather than having a comparison between the as-design and as-built statuses. They present a conceptual workflow for the planning and control of the design and construction processes using Digital Twin information systems while leaving the researchers/practitioners without solid practices in the design or construction process. Their emphasis on the impossibility of having a closed loop model of construction control prior to the new enabling technologies is not valid in the real construction practice. They define Plan, Do, Check and Act cycles as the structure to achieve a closed loop production control. Either manually or automatically, by any technology, if not all for most construction processes, monitoring, comparing current and designed/planned status and acting accordingly is a routine project control practice.

Essential to the execution of a process or producing a product in construction, a flow of information including material, labor and equipment information is needed. Capturing the information flow about the product and process and considering their constraints on a specific time basis, which is determined by the project specifications, nature of the product/process, etc., would enable actionable decisions. Despite the existing literature that focuses on a single stream of information (material, or labor, or equipment), as depicted in the case study, the CDT framework developed in this research requires the acquisition of all the necessary information of the product or process through the deployment and integration of the enabling technologies.