More sophisticated data management is required to ensure common data environments (CDE) can enable city-level digital twins, according to new international research.
“Digital twin technology is pivotal for advancing sustainable, liveable and resilient smart cities. As digital twins scale from building to infrastructure and city levels, data management remains a key challenge due to increasing data heterogeneity. This paper addresses this gap by defining a CDE that connects physical and virtual spaces with three enablers: data sources, data management with functional components, and data consumers,” the researchers state.
First, recognising the volume of different data sources a city-level digital twin should draw from and the varying quality of those databases, the researchers say: “Adopting master data management, metadata management, data dictionary and data governance techniques and tools could provide collaborative solutions for defining and organising data sources dynamically.”
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Second, the researchers highlight the solutions to tackle key data management challenges, including connectivity, interoperability, computational capability and cybersecurity.
The connectivity issue is not to be underestimated, say the researchers. “Improving connectivity in CDEs is vital for timely data collection, processing, and exchange to support decision-making. Manual data collection complicates this effort, as does coordinating hardware devices providing data sources.
Connectivity challenge
“Additionally, the challenges brought by coordinating hardware devices providing the data sources are inevitable. The data transmission gap between systems or devices, such as traditional building management systems, IoT devices, monitoring cameras, and the digital twin CDE, are intricate but critical to address.”
They continue: “The primary challenge lies in coordinating diverse protocols and vendor-specific platforms, a task that becomes particularly complex in city-level digital twins due to the highly varied and intricate data availability conditions. Additionally, hardware upgrades and replacements create further complications in maintaining connectivity. Beyond technical issues, organisational dependencies, such as data access permissions and supplier collaboration, remain critical barriers to connectivity in complex CDEs.”
To address this, the researchers suggest:
- the development of automated tools to address data transmission challenges and enable efficient, real-time data collection and ingestion;
- the employment of blockchain technologies to enhance data integrity and collaboration among stakeholders; and
- the establishment of organisational agreements to regulate access permissions and improve system-wide connectivity.
Combatting interoperability issues
Focusing on interoperability, the researchers state: “Current studies reveal that the process of transforming raw data sources into a state suitable for exchange or integration is often time-consuming and complex. The diversity of data types and heavy data loads challenge integration, particularly in web-based systems reliant on low-performance hardware.
“While a universal data format is neither practical nor feasible, existing tools such as Cesium, Forge, ArcGIS, open-sourced and lightweighted map services, and game engines, offer potential solutions for geometric data preprocessing and integration. These tools could also play a critical role in maintaining integrated geometric data models, facilitating updates to specific elements dynamically and effectively.”
They further suggest:
- methods to address the dynamic update of geometric digital assets in digital twins should be developed;
- data requirements for common data models should be decveloped and managed in data dictionary platforms, incorporating user requirements;
- the development of federated BIM models based on standards such as 19650 and COBie to “seamlessly enrich physical assets' geometric and semantic information”; and
- the development and implementation of standardised workflows and tools for data quality assurance.
Computational capability
The researchers detail the computational capability issue: “The computational capability of digital twin CDEs is often underdeveloped, with many studies relying on external software for modelling and simulation. Digital twins are primarily used to visualise analysis results rather than to conduct real-time simulations. Real-time simulation is also challenging due to the difficulties of integrating dynamic data sources, such as the timeliness challenge in integrating BMS (low bandwidth and granularity data) and IoT (high bandwidth and granularity data) with BIM (geometric and semantic data).
“Additionally, the concurrency of numerous users interacting in web-based digital twins simultaneously is very challenging, requiring high-performance data management solutions. Improving computational capability might be highly dependent on the upgrade of hardware in the industry. [Other] researchers have indicated the potential of game engines such as Unreal Engine for model simulation.”
The researchers suggest:
- the development of advanced data integration methods improving real-time simulation and addressing the concurrency issue to enable user collaboration in digital twins;
- advanced system architecture should be developed and applied that can reduce the requirements of high hardware performance; and
- simulation results should be used and demonstrated with more interactions of 3D elements.
The research paper is published in the June issue of Automation in Construction. It was written by researchers at the Bartlett School of Sustainable Construction at University College London, the City University of Hong Kong and Tsinghua University in China.
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