Reply To: What is the difference between Twins & BIMs

I stole a lot of this content and wrote the following:

Our definition of a Digital Twin is as follows:

A Digital Twin is a digital representation of a physical thing (and its operation) that one can query.

This definition helps us to distinguish between the concept of a Digital Twin, and the more established practice of BIM. The key differences are:

1. Digital Twins can and should be part of the construction phase, but the focus of their use is on the operation of existing physical assets (e.g. the 99%+ of assets that are not currently under construction). Within our organisation (and the wider industry), there is often a loss of data capability as projects move from construction to operations as operators have typically been unable to exploit BIM products. By designing our construction models as nascent Digital Twins we have the opportunity to define the data and logic required to operate an asset at the start of the lifecycle, and ensure that the models we create during construction have operational value.
2. The emphasis on being able to query Digital Twins is important. A Digital Twin should not be a static representation of an asset, it should reflect the logic of that asset in operation. This means that Digital Twins need to expose not just the material properties of an asset (e.g. location, dimensions, materials, etc.) but also the business logic governing that asset (e.g. how we as the infrastructure owner can intervene on that asset to change how it performs). This allows Digital Twins to enable better organisational decision-making through simulation and ‘what if’ scenarios.
3. In order to realise the two points above, the data schema underpinning Digital Twins is necessarily more complex, and more focused on relationships rather than properties. BIM data standards, such as COBie or Uniclass focus on the hierarchies of assets, and their properties (e.g. “span belongs to bridge and is made of steel”). Emergent Digital Twin data models (including our own Highways England Ontology) capture not just the properties of assets but how the relate to their wider environmental and operational context (e.g. “span is corroded by road salts, damaged by vehicle incursions, is maintained when the flange has 20%+ corrosion, and supports a flow of 50,000 vehicles per day travelling on the M25 (as well as a broadband internet cable) causing significant safety and KPI impact in the event of failure”). Creating these data models demands the creation and maintenance of a deep ‘knowledge graph’ of the organisation.