Digital Twin in the Construction Industry: Comparison
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Digital twin (DT) has gained significant recognition among researchers due to its potential across industries. DT presents the opportunity to develop digital models, which can be continually updated using several sources of data to make predictions regarding the current as well as future states and conditions of the physical asset. These models can be simulated for real-time predictions, optimisation, monitoring, and controlling, as well as enhanced decision-making regarding the status of a physical asset. In addition, DT utilises other technologies, including artificial intelligence (AI), machine learning, and data analytics. With the prime goal of solving numerous challenges confronting the construction industry (CI), DT has witnessed several applications in the CI.

  • construction industry
  • digital twin
  • technology

1. Introduction

The advent of Industry 4.0 gave rise to an array of digital technologies, including digital twin (DT) technology. DT presents the opportunity to develop digital models, which can be continually updated using several sources of data to make predictions regarding the current as well as future states and conditions of the physical asset. These models can be simulated for real-time predictions, optimisation, monitoring, and controlling, as well as enhanced decision-making regarding the status of a physical asset. In addition, DT utilises other technologies, including artificial intelligence (AI), machine learning, and data analytics. Due to the prowess of DT, the construction industry (CI), with its numerous challenges, has started DT applications. Technologies such as building information modelling (BIM), wireless sensor networks (WSNs), and machine learning, together with data analytics are presently being used to support the adoption of DT in the CI. Several studies [1,2,3][1][2][3] have studied DT in the CI and established their relevance. For instance, Opoku, Perera, Osei-Kyei, and Rashidi [2] indicated that DT is necessary for facility management since they can be employed in “What-if” analysis in decision making relating to the building’s operation and maintenance activities. Researchers and practitioners are currently discovering the numerous potentials of DT in the CI.

2. Concept and Definition of Digital Twin

The concept of DTs has made several waves in various industries and presents an overwhelming desire to adopt this concept. The National Aeronautics and Space Administration (NASA) first used the term “digital twin” in the public domain [8][4]. The Apollo program of NASA’s conceptualisation of “twins” resulted in the use of the concept in its space exploration missions in the 1960s. During these missions, two matching spaceships were designed to mirror the state of the spaceship that was on a mission [9][5]. Boschert and Rosen [9][5] reported that the spaceship that stayed on Earth was regarded as the twin of the ship in space. The Earth-remained ship could present an idea of the conditions that existed in space during an exploration mission. However, in scientific research, it is well documented that Hernandez and Hernandez [10][6] were the first to use the concept. In 2003, at the University of Michigan, Michael Grieves applied the DT concept in an industry presentation for the formulation of a Product Life Cycle Management (PLM) centre. The PLM led to the digital version of a physical product which was later expanded with the Information Mirroring Model [11][7]. In 2006, Hribernik et al. [12][8] introduced an alternative to the DT known as “product avatar”. The “product avatar” was utilised in the development of an architecture for managing information that supported a bidirectional product-centric flow of information. A white paper was published in 2014 by Michael Grieves to explain the DT concept. Several definitions of DT are available in the literature. However, the definitions are based on its application without a limitation to any specific industry. Opoku, Perera, Osei-Kyei, and Rashidi [2] reported on the ambiguities in the definitions of DT due to its lack of connection to specific fields within the global industry. Notwithstanding, the concept of DT as a technology should possess three (3) distinct components that include a physical object, a virtual entity, and the data that create a linkage between the physical and virtual entities [13,14][9][10]. Fotland et al. [15][11] defined DT as a physical asset’s digital form that collects real-time data from the entity and presents information which is not directly gathered using hardware. Luo et al. [16][12] described DT as a multi-domain as well as ultrahigh fidelity digital model that integrates several domains which include mechanical, electrical, and hydraulic, as well as the subjects of control. Grieves and Vickers [17][13] defined DT as a full description of an actual or potential product that is physically created using a set of virtual information constructs from the micro atomic level to the macro geometrical level. Gabor et al. [18][14] also defined DT as the simulation of the physical entity itself to enable the prediction of system’s state in the future. Moreover, Rosen et al. [19][15] defined DT as very realistic model of the current state of the process as well as their behaviours in communicating with their environments in their real world. These definitions from subsequent years’ publications really indicate the fact that, irrespective of the industry of DT application, there should be a physical entity, virtual entity, and the data that connects them in order to ensure a bidirectional dynamic interaction between the physical object and virtual model [14][10]. It is also worth mentioning that, for the virtual entity to be identified as a DT, the physical component must be in existence. This is significantly different from virtual engineering where geometric models are integrated with their related engineering tools for simulation-based decision making. Furthermore, the sophistication of the physical and virtual entities’ integration identifies the different classifications of DT. Kritzinger et al. [20][16] and Opoku, Perera, Osei-Kyei, and Rashidi [2] reported that there are digital models where there are no interactions between the entities themselves. The studies also stated that there are digital shadows where there is a self-driven one-directional movement of data from the physical object to the digital model. This is normally through the utilisation of the Internet of Things (IoT) and WSN devices including sensors, drones, and the like. Finally, the studies indicated that in terms of a complete DT, there is a fully integrated two-way communication and interactions between the physical object and its virtual counterpart or model. Table 1 below presents an ordered list of definitions relating to the development of the DT concept and the specific domains of their application. This is to give a clearer understanding of how the concept’s development has evolved over the period across different domains.
Table 1.
Sample list of yearly DT definitions in the literature.
S/N Year Domain of DT Application Definition of Digital Twin Reference
1 2010 NASA’s integrated simulations A combined multi-scale, multi-physics, probabilistic simulation of a system that utilises the most readily accessible physical models, updates from sensor, fleet history, and the like to reflect its flying twin’s life. [8][4]
2 2012 Airframes An aircraft structure’s cradle-to-grave model that has the capacity to achieve mission requirements, including sub-models of the electronics, controls of flight, the propulsion system, and other subsystems. [21][17]
3 2013 Predictive manufacturing A coupled model of the real machine that operates in the cloud platform as well as simulates the health conditions with a combined knowledge from both data-driven analytical algorithms and other accessible physical knowledge. [22][18]
4 2014 Structural health management A certification paradigm together with life management whereby models and simulations comprise the state of the as-built vehicle, as-experienced loads and environments, and other vehicle-specific history to allow high-fidelity modelling of individual aerospace vehicles throughout their service lives. [23][19]
5 2015 Industrial manufacturing A very realistic model of the current state of the process as well as their behaviours in communicating with their environments in their real world. [19][15]
6 2016 System design A simulation of the physical entity itself to enable the prediction of system’s state in the future. [18][14]
7 2017 Product lifecycle management A full description of an actual or potential product that is physically created using a set of virtual information constructs from the micro atomic level to the macro geometrical level. [17][13]
8 2018 Smart manufacturing A multi-domain and ultrahigh fidelity digital model incorporating various areas including mechanical, electrical, hydraulic, and various subjects of control. [16][12]
9 2019 Architecture for cyber physical systems A physical entity’s digital version that is connected and synchronised to signify the system’s elements and dynamics relating to its lifecycle operation within the system’s environment. [24][20]
10 2020 Work environment safety DT is a physical asset’s digital form that collects real-time data from the entity and presents information which is not directly gathered using hardware [15][11]
11 2021 Construction DT is similar to Building Information Model (BIM). However, their purposes, technologies, end-users, and data types are different. Digital twin utilises real-time data, whilst BIM works with static data. [2]
12 2022 Construction A fully or partially completed structure or building’s representation in real time to reflect the character and status of the structure or building. [7][21]

3. Application of DT in the CI

The growing interest in the DT concept and technology has seen a gradual implementation within the CI. Although the industry is recognised as being slow in terms of innovation and advancements in technology, over the past years, the technology has witnessed a slow adoption to tackle the wide array of challenges in the CI. Researchers in the CI have undertaken several studies relating to DT in the industry. Opoku, Perera, Osei-Kyei, and Rashidi [2] reviewed and reported on the applications of DT technology in the CI. The authors focused on the technology’s applications across the various life cycle stages of a construction project. They reported that at a project’s design and engineering stage, the utilisation of DT has been geared towards the use of BIM models. This aids in decision making regarding the inheritance or discarding of various components and information during the project’s redesign as well as re-engineering activities. In the construction phase of a project, Opoku, Perera, Osei-Kyei, and Rashidi [2] reported that digital twins have been focused on cost reduction and the structural integrity of the project’s system. Further, the authors indicated that during the project’s operation and maintenance stage, the applications of the technology have been focused on the management and maintenance of facilities, monitoring, processing of logistics, and energy simulations of projects. This enables the facility managers to take vital decisions that relate to operating and maintaining the building project. Finally, the authors reported that there have been limited studies focusing on DT applications in the project’s demolition and recovery phase. Notwithstanding, the authors mentioned that DT could be employed in the conservation as well as safeguarding heritage assets that may have to be demolished soon. These applications indicate that the CI is keen on utilising DT technology to provide solutions to most of the challenges confronting the industry.

4. DT Applications in Other Domains

DT technology has witnessed several applications in technology-advanced industries or domains including manufacturing [20[16][22],25], aeronautics and aviation, healthcare [26][23], automotives [27][24], the energy sector, education, and meteorology [28][25], among others. In manufacturing, DT has been utilised for real-time monitoring, control of production, production planning, predictive maintenance, and detecting faults, together with the monitoring of the state of various systems [29][26]. Tao et al. [30][27] mentioned that DT ensures the healthcare management of products and provides their digital footprint through their geometry, structure, behaviour, and functional properties. In the healthcare domain, Kamel Boulos and Zhang [31][28] indicated that DT is employed in enhancing the diagnostics, prognostics, and treatment of patients. Further, Bruynseels et al. [32][29] reported that DT is used for disease prediction, well-being management, and the provision of precise medication. In the aviation and aeronautics domain, DT is employed in aerospace vehicle maintenance, flight model simulation, and fatigue life and aerothermal model prediction [33,34][30][31]. Francisco et al. [35][32] also deliberated on the application of DT in the energy sector and highlighted that DT is used for energy usage analysis, predictive maintenance, life cycle management, and fault diagnosis. Finally, in the meteorological domain, DT is used in weather prediction, geospatial asset management, and ageing infrastructure [36][33], whilst in the education sector, DT is applied in skills enhancement and effective delivery of knowledge using online platforms [37,38][34][35]. There is also a potential application of DT in medical training. These applications of DT in different domains show the potential of the technology to provide solutions to most of the global challenges and enhance productivity across industries.

References

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