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A peer-reviewed article of this preprint also exists.
Submitted:
02 November 2023
Posted:
03 November 2023
You are already at the latest version
Source Databases | Web of Science (WoS), Scopus, Taylor and Francis, IEEE Xplore, Springer, and ASCE Library |
Search String | (“digital twin” OR “digital twins” OR “virtual twin” OR “digital replica” OR “virtual counterpart” OR “cyber-physical system”) AND (“development” OR “evolution” OR “key technologies” OR “key components” OR “key elements” OR “applications”) AND (“construction engineering” OR “construction” OR “construction sector” OR “AEC industry” OR “construction industry” OR “construction engineering and management”) |
Time-period Restriction | 2010-2023 |
Article Types | Journal, Conference Paper, Book Chapter, Review |
Language Restriction | English |
Included Subject Areas | Engineering, Computer Science, Energy, Mathematics, Environmental Science, Materials Science, Decision Sciences, Business, Management and Accounting |
Excluded Subject Areas | Social Sciences, Earth and Planetary Sciences, Chemical Engineering, Medicine, Economics, Econometrics and Finance, Arts and Humanities, Agricultural and Biological Sciences, Neuroscience, Chemistry, Biochemistry, Genetics and Molecular Biology |
Work Area/Industry | Construction Industry, AEC Sector, Civil Engineering |
No | Key Components | Description | Corresponding Literature |
I | Technologies | The core technologies that help develop the interaction of DT with real-world physical entities are: the Internet of Things (IoTs), Artificial Intelligence (AI), Cloud Computing (CC), Extended Reality (XR) | [1,21,25,45,46,47,48,49,50,51,52,53,54,55,56] |
II | Maturity Levels | The basic levels of DT maturity have specific purposes and scope to help in decision-making throughout the system’s lifecycle: Pre-Digital Twin, Digital Twin, Adaptive-Digital Twin, Intelligent-Digital Twin | [2,22,26,33,34,57,58,59,60,61,62] |
III | Data Layers | Data is the core of DT integration and fusion of the virtual model, and data flows in layers between systems: Data Acquisition Layer, Data Transmission Layer, Digital Modeling Layer Data/model Integration and Fusion Layer, Service Layer | [35,56,63,64,65,66,67,68,69,70,71,72] |
IV | Enablers | Five fundamental entities are responsible for promoting and enabling the DT functioning: Physical Entity, Virtual Model, Data, Smart Service, Connection | [5,22,45,58,73,74,75,76,77] |
V | Functionalities | A variety of functionalities are carried out with DT employment; however, the crucial ones for the AEC sector applications over the product lifecycle are: Simulation, Visualization, Prediction, Optimization, Monitoring | [5,17,20,23,32,35,36,65,66,68,71,76,78,79] |
Corresponding Study | Year | Key Point in the Study | DT-Definition |
[89] | 2010 | Integrated simulation | A digital twin is a comprehensive simulation of a vehicle or system, integrating multi-physics multi-scale aspects and leveraging the best available physical models, sensor updates, and past operational data to mirror the life of its real-world counterpart. |
[78] | 2012 | Ultra-high-fidelity model | A Digital Twin is a simulation of an as-built system that seamlessly mirrors its real-life counterpart by incorporating models, sensors, and other intelligent devices. |
[90] | 2014 | High-fidelity modeling | Digital Twin is a life management and certification paradigm integrating as-built vehicle states, as-experienced loads and environments, and another vehicle-specific history into models and simulations. This approach enables high-fidelity modeling of individual aerospace vehicles throughout their service lives. |
[84] | 2015 | Lightweight virtual model | The Digital Twin comprises a physical entity existing in the real environment, a virtual representation existing in the digital domain, and information connectors bridging the real and virtual counterparts. |
[87] | 2015 | Realistic model | The term “Digital Twin” typically refers to highly realistic models of the current process state and their behaviors as they interact with the real-world environment. |
[91] | 2016 | Functional description of a product | The Digital Twin is a virtual representation of a component, product, or system that benefits the entire lifecycle of the entity. |
[92] | 2016 | Virtual substitutes | Digital twins are virtual substitutes for real-world objects, embodying virtual representations and communication capabilities. These smart objects function as intelligent nodes within the Internet of Things and services. |
[93] | 2016 | Advancement in modeling, simulation, and optimization | Digital twin represents one of the imminent major advancements in modeling, simulation, and optimization technology. |
[94] | 2017 | Multi-disciplinary replica | The Digital Twin serves as a virtual representation of a production system, capable of synchronization with the actual system through real-time data sensed from connected smart devices. |
[95] | 2017 | Virtual equivalent | The Digital Twin is a set of virtual information constructs that fully describe a physical product. |
[86] | 2017 | Digital representation of an asset | A Digital Twin is the digital representation of a distinct asset (such as a product, machine, service, or intangible asset) encompassing its properties, condition, and behavior using models, information, and data. |
[17] | 2018 | Virtual product data | The components of a complete Digital Twin include a physical entity, a virtual counterpart, a connection linking the physical and virtual counterparts, as well as data and services. |
[58] | 2018 | Product mirror and Digital counterpart | The Digital Twin is a digital counterpart of a physical object. |
[96] | 2018 | Multi-level digital layout | The Digital Twin of a physical entity encompasses layers of data, including information about the product itself, the processes involved, and the resources within its operational environment. |
[59] | 2019 | Updated virtual instance | A digital twin is a virtual representation of a physical system (twin) that continuously updates its performance, maintenance, and health status data throughout its entire life cycle. |
[97] | 2019 | Data mapping | Digital Twin refers to a virtual object or a collection of virtual entities defined within the digital virtual space, establishing a mapping relationship with real-world objects in the physical space. |
[98] | 2020 | Virtual entity | A cyber-physical system comprises of both a physical entity and a cyber entity in the form of a Digital Twin. |
[99] | 2021 | Twin of physical entity | Digital Twin is an innovative concept that strives to create a virtual counterpart of a physical entity in the digital world. |
[100] | 2021 | Mirror world | Digital Twin is an approach that establishes a bidirectional connection between a physical system and its virtual representation, enabling the utilization of Artificial Intelligence and Big Data Analytics. |
[101] | 2021 | Real-time digital representation | Digital Twin is a real-time digital representation of a physical building or infrastructure. Typically, on-site sensors continuously monitor changes within the building and its environment, providing data to update the BIM model with the latest measurements and information. |
Corresponding Study | Data Acquisition Layer | Data Transmission Layer | Digital Modeling Layer | Data/Model Integration and Fusion Layer | Service Decision-Making Layer |
[107] | Environmental sensor data by direct digital control system | Direct digital control system & BACnet protocol for data communication | Autodesk Revit for 3D modeling | MSSQL, COBie, IFC 4 extension, Autodesk Revit plug-in, ML algorithms | Monitoring and prediction of conditions of the chiller plant |
[108] | Temperature and mechanical sensors data | WSN (wireless sensor network) & MQTT (Message Queuing Telemetry Transport) | 3D FEM (Finite Element Model) | Metadata APIs for calculations of measured values | Real-time monitoring and warning alerts on reaching defined thresholds |
[31] | Cameras and video stream data | LAN (Local Area Network) & Internet | BIM model. Autodesk Revit, Three.js & Draco 3D | MySQL, Cloud service, Deep learning, Three.js program, Trend graphs | Detection and monitoring of pedestrian trends and pedestrian time |
[66] | RFID tags, Positioning data | Smart mobile gateway & MQTT | Unity 3D model | Time numerical models, Unity 3D, Analytic charts | Real-time monitoring of activities and task alerts & ticket visualization |
[109] | Environmental sensors data by Restful API and Wired sensors | URL via API, Internet, and BACnet | BIM models by Autodesk Revit | Machine learning, MSSQL, IFC, COBie | Fault detection and prediction in air handling unit (AHU) |
[65] | RFID tags, Industrial wearables, Positioning data | Mobile Gateway Operating System (MGOS), Light middleware, Wireless network | 3D models by Solidworks and Autodesk 3D Max | Web database and API for Unity 3D | Real-time positioning tracing for smart objects, robots, and instantiation for prefabricated modules |
[70] | Environmental and thermal data by Wind sensors and IoT nodes | HTTP (Hypertext Transfer Protocol) | BIM models by Autodesk Revit | Google cloud platform, Game engines, Thermal comfort charts | Display environmental, thermography, and thermal comfort levels in real- time |
[110] | RFID and GPS tags, Positioning data | Internet, Azure blockchain platform to provide IoT hub, Web server, Blockchain network | Unity 3D | Microsoft Azure cloud, API for Unity, Compliance checking for BIM and Blockchain | Real-time information tracing by blockchain network |
[111] | Environmental and mechanical data by Wind, Speed, and Temperature sensors | - | Autodesk Revit, Laser scanning, 3D point cloud | Machine learning algorithm using Markov chain & Line graphs | Simulation of condition predictions, structural health monitoring, and early warning for maintenance |
[112] | Location and tracking data from the virtual server generating hypothetical IoT sensor data | - | Virtual modelling, Unity 3D | Unity engine, Data analytics, 3D simulations, API into Bing Maps | Monitoring and simulation of different scenarios in real-time |
[106] | Environmental data and component information by BMS sensor network and QR codes | HTTP (Hypertext Transfer Protocol), Ethernet gateways | 3D models by Autodesk Revit and AECOsim building designer, Laser scanning, Photogrammetry | Autodesk forge API, IFC schema, Amazon web services (AWS), DynamoDB, Time series graphs | Real-time anomaly detection in pumps, environmental monitoring, and maintenance prediction of faults of boilers |
[113, 114] | Mechanical data by vibration sensors | - | BIM models by Autodesk Revit | Autodesk forge API, .NET using C# and Javascript, IFC schema, Cumulative sum control charts (CUSUM) | Anomaly detection and monitoring of the working condition of pumps |
[115] | Environmental, energy, and video data by BAS sensors network | HTTP & Building systems communication networks | Laser scanning and Mixed Reality (MR) | MySQL, Private cloud storage, Deep learning, Trend charts, and Real-time animations | Security and monitoring of energy consumption & Visualizations for space management |
[116] | Positioning and location label data by positioning devices, ultrasonic sensors, and 3D gyroscope sensors | HTTP, Bluetooth, and Wi-Fi | - | Algorithm engines for face recognition, personnel positioning and mechanical attitude positioning | Monitoring of operations, worker and component tracking alerts for risks in real-time |
[104] | Image data by Microsoft Kinect cameras | Gazebo_ros_pkg for simulation | VR (Virtual Reality), Unity 3D, Unified Robotics Description Format (UDRF) | Robot Operating Software (ROS), VR headset | Real-time data capturing to control the Robot on site |
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