Key Concepts

Digital Twin

A digital twin is more than a static model; it provides contextual relationships between asset and environment, real-time or periodic telemetry, and bi-directional connectivity with its physical counterpart, enabling continuous monitoring and intervention.

Commons

The “Commons” is the technical core of the Information Management Framework, consisting of:

• Foundation Data Model (FDM): An upper ontology defining general concepts (time, space, processes, causality, etc.) and relationships to describe any digital twin in a machine-interpretable way.

• Reference Data Library (RDL): A federated set of controlled vocabularies and taxonomies specifying classes and properties for consistent description of twins, e.g., standardized definitions of “door” or “beam”.

• Integration Architecture (IA): Protocols and components (discovery, authorization, messaging, transformation, and validation) that enable secure publication, discovery, querying, and integration of distributed twins.

Gemini Principles

Guiding values for NDT development—public good, innovation, competition, security by default, data quality, and trust—that inform the design of the Commons.

Mechanisms

Discovery Protocol

Enables efficient, catalogue-like retrieval of digital twins across providers using a distributed messaging layer, presenting a unified view to authorized users.

Authorisation Layer

Implements role-based access control and security policies, ensuring data owners define which users and services can discover and invoke their twins or datasets.

Data Transformation and Validation Engines

• Transformation: Semi-automated or real-time mapping of legacy or proprietary data into the FDM and RDL structures, using extract-transform-load and AI-driven semantic tools.

• Validation: Continuous compliance testing against axioms derived from the FDM and RDL to ensure integrity and correct use, informing confidence levels.

Query Protocol

Defines interfaces for invoking twins—ranging from simple distributed database-style queries to execution of computational models (e.g., finite element simulations), orchestrated via cloud services and governed by security constraints.

Governance and Extensibility Processes

Formalised workflows for stakeholder engagement, version control, standardisation, and contributions, balancing backward compatibility with the need for evolution through community-driven extensions to the FDM and RDL.

Examples

1. Fire-Risk Retrofit of Tower Blocks: Distributed query across planning databases to identify cladding types, structural models, occupancy telemetry, and socio-economic data to rank fire risk and prioritize retrofit investments.

2. Regional Resilience Simulation: Composite twin of a Cumbrian region integrating transport, utilities, environmental, and socio-economic models to simulate flood impacts on critical bridges and optimize first-responder routing and infrastructure reinforcement.

3. Citizen-Centric Planning: Linking infrastructure, environmental, connectivity, crime, health, and employment datasets to model living conditions, identify underserved communities, and recommend optimal locations for public services such as hospitals.

References

1 Hetherington, J., & West, M. (2020). The pathway towards an Information Management Framework – A ‘Commons’ for Digital Built Britain. DOI:10.17863/CAM.52659 The pathway towards an Information Management Framework - Digital Twin Hub