When looking into the capabilities of the Digital Twin for supporting Facilities Management (FM), we need to consider the multiple layers of system architecture which are specifically designed for the FM sector.
But first we need to understand what exactly a Digital Twin is and how should it be applied to FM.
A Digital Twin is a realistic digital representation of assets, processes and systems within the built environment providing a two-way connection between the physical world and the digital world. Data from the physical world informs the digital twin which enables asset owners and operators to make better operational, maintenance, investment and planning decisions to create value, increase resilience and secure sustainability.
The information exchange between virtual and physical entities has to be a bi-directional flow which is necessary for handling static and dynamic data of the building assets (Peng et al., 2020).
The main three Digital Twin components (as shown in Fig. 1) considered here are:
1) The Physical components
2) The Virtual models
3) The Data that connects them
The connection loop between the “Virtual-Physical” duality of the system is provided by the “Data” in its various forms.
Figure 1: Digital Twin Paradigm showing bidirectional flow of data (Boje et al., 2020)
Due to a lack of research, there are few case studies developed to showcase the conceptual Digital Twin framework for managing the built assets through generic systematic architecture. However, one such case study that does endeavour to develop an integrated platform to collect the data from various sources and databases is discussed in the West Cambridge Campus study (Lu et al., 2020a).
In this case study they strive to understand the advantage of using Digital Twin in operation and maintenance (O&M) phase to increase the operational efficiency and performance of an asset throughout its life cycle. This has the potential to reduce the O&M cost through increasing the value of the data within an organisation.
The system architecture which is used on the West Cambridge study – which can be seen in figure 2 – integrates various data sources and multiple applications to explore the opportunities and challenges when developing a Digital Twin enabled building FM. So, let’s take a look at the various layers.
Figure 2: The system architecture of Digital Twin (Lu et al., 2020a)
Data acquisition layer
Data acquisition layer is the foundation of each Digital Twin. Due to the heterogeneity and large volume of data, the acquisition mechanism and approach includes several challenges, especially when considering the type, format, source and content of data. It can be collected from a multitude of sources such as wireless sensor network system (WSNS), Internet of Things (IoT) devices, QR codes, image-based techniques, Mobiles and Radio-frequency identification (RFID) techniques which can be used for data mining and filtration (Johnson et al., 2020).
Data Transmission Layer
The transmission layer transfers the acquired data to the higher layers for modelling and analysis. Various communication technologies could be used in this layer, such as short-range coverage access network technologies, e.g., Wi-Fi, Zigbee, near field communication (NFC), M2M, and Zwave, and wider coverage, i.e., 3G, 4G long-term evolution (LTE), 5G, and low-power wide-area networks.
Considering energy efficiency of networks and speed of transmission, light fidelity (Li-Fi) and LP-WAN are promising alternatives for wide-range coverage for developing Digital Twins in building and city levels.
Data/Model Integration layer
This layer mainly works on predictive maintenance of the assets in the building by using real time data analysis and processing of different functions such as energy consumption, thermal condition monitoring, MEP system monitoring, fault detection, future maintenance work orders and status of the FM equipment’s. In this case study the heterogenous data from various sources are gathered and stored in a large-scale cloud storage system which is necessary for managing the performance of the Digital Twin through cloud computing, data visualisation and information management.
Application/Service layer
Interactive dashboard and web applications can be used for showcasing the information such as energy management, space management, thermal comfort management etc. to the end users for visualisation. From the studies it is said that this application layer acts as a user interface for the dynamic Digital Twin users (Zheng and Sivabalan, 2020). It also indicates the performance of the designed Digital Twin for the buildings is responsible for creating a sustainable environment.
The system architecture in this case study enabled the integration of heterogeneous data sources, supported effective data querying and analysing, and informed the decision-making processes in O&M management.
Some of the benefits realised with this approach on West Cambridge are:
- By using cloud computing and IoT based services, this gave many of the protocols and environments the ability to manage real time sensors and distribute data in numerous formats
- Reduced energy consumption across the West Cambridge site
- Improved asset maintenance through predictive data analytics
It is evident that a Digital Twin can bring together a number of datasets, tools and services with advanced monitoring and predictive capabilities for FM if we get the system architecture and its components setup right at the start of the building lifecycle.
However, the FM field is still in its infancy in terms of the Digital Twin and one case study/application does not make a comprehensive framework. But we can enhance the existing architecture and build the growing knowledge of Digital Twin developments using this case study as a prime example.
The AEC industry needs to continue to develop and trial frameworks such as this to advance Digital Twin development and enhance the building FM to achieve maximum value within an organisation.
The Digital Twin will ultimately enable remote management of facilities, and there is a growing appetite across industry to increase the use of Digital Twin mapped to FM services. After all, technology has always been one of the most promising aspects whenever circumstances warrant a radical shift in the way we function, and that shift is needed right now.
If you would like to learn more about the work BIM Academy is doing with FM and Digital Twins, please contact us at [email protected].
References
- Boje, C., Guerriero, A., Kubicki, S. and Rezgui, Y. (2020). Towards a semantic Construction Digital Twin: Directions for future research. Automation in Construction, 114, p.103179.
- Johnson, A., Heaton, J., Yule, S., Luke, S., Pocock, D., Parlikad, A.K. and Schooling, J. (2020). Informing the Information Requirements of a Digital Twin: A Rail Industry Case Study. Proceedings of the Institution of Civil Engineers – Smart Infrastructure and Construction, pp.1–12.
- Lu, Q., Parlikad, A.K., Woodall, P., Don Ranasinghe, G., Xie, X., Liang, Z., Konstantinou, E., Heaton, J. and Schooling, J. (2020a). Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus. Journal of Management in Engineering, 36(3), p.05020004.
- Peng, Y., Zhang, M., Yu, F., Xu, J. and Gao, S. (2020). Digital Twin Hospital Buildings: An Exemplary Case Study through Continuous Lifecycle Integration. Advances in Civil Engineering, 2020, pp.1–13.
- Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L. and Nee, A.Y.C. (2019). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems.
- Zheng, P. and Sivabalan, A.S. (2020). A generic tri-model-based approach for product-level digital twin development in a smart manufacturing environment. Robotics and Computer-Integrated Manufacturing, 64, p.101958.
