A federated dataset that we might be familiar with in project delivery, generally in the form of a Navisworks model (or similar), is not the same thing as a Digital Twin. Coming from the world of BIM and Digital Engineering to an understanding of Digital Twin can, however, be challenging. A time- and location-based understanding of all assets, both physical and digital, becomes fundamental to better decision-making. This places an even greater degree of reliance on the comprehensive classification of digital assets.

A Digital Twin differs from federated data sets traditionally delivered by BIM, in that it is a database and representation or virtual model of physical assets and linked events. This ‘thing’ can be anything from a building or a power network, to a bridge or even industrial equipment. It can include elements such as water, people and vehicle movement, and is a platform through which to interrogate or exchange data.

Importantly, a Digital Twin can span the entire lifecycle of a physical asset and is enabled with real-time data, allowing machine learning and simulations to facilitate better decision making. Ultimately, this leads to more informed outcomes in the physical world and new ways to deliver services.

It is estimated that by 2022 over 75 per cent of large industrial companies that are implementing Internet of Things solutions will have also adopted Digital Twin technology.

Common Misconceptions

As an emerging market segment, buzzwords and misconceptions about Digital Twins are rife. In particular, delineating the core features of Building Information Modelling (BIM), Digital Engineering (DE), and Digital Twins can be difficult.

We approach this in terms of capabilities:

• Building Information Modelling (BIM); Specification, management, utilisation and authoring of data rich 3D information models that document, capture and reflect the built environment through a federated data set.
• Digital Engineering (DE); Innovation, specification, management and utilisation of digital solutions within the built environment. Examples of common digital solutions encompassed under Digital Engineering include BIM, GIS, Virtual Reality and Augmented Reality.
• Digital Twin; Virtual representation of an object or system that spans its lifecycle utilising real- time data to provide a live reflection of the captured subject(s). A Digital Twin can be utilised for analysis, simulation, planning and business logic. It can be linked to digital assets in a combined platform, with a focus on data analytics and solutions that leverage machine learning.

The ability to agnostically integrate a client’s existing datasets (Data Ontology will be covered in a future instalment) or support the procurement of new elements provides the basis for a true Digital Twin experience and is essential to the development of new services and revenue streams for all data owners.

Applications