Defining the Digital Twin

As we begin to look at a future involving Digital Twins, do we need to define what Digital Twins are?

by Dean Douglas | October 2, 2020 |  8 min read

Digital Twin Research
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There are so many kitsch and jargon terms floating around the industry that we hear all the time – in everyday working and in academic publications – often without ever fully knowing what these terms mean.

We’ve seen it with the likes of Enterprise Asset Management, we’ve seen it with Construction 4.0, and of course, we’ve seen it with BIM. But are we on track to repeat history and to fall into the same trap as those that have gone before us with digital twin? Or do we mean Cyber-Physical Systems? Or maybe even SCADA?

Many in both industry and academia are already investigating how digital twins can be implemented in a variety of scenarios, including BIM Academy. We’re looking to developing a framework that will enable asset managers in linear infrastructure to implement digital twin. But with some already claiming to be implementing digital twin enabled work processes and technologies, first we must understand what digital twin is. Here we look at the parts of digital twin that we can agree upon and the parts that are still up for debate.

So, what are the parts that have been agreed upon and form the basis of a digital twin?

These elements are; the need for a physical entity whether that be machinery, a building, or a railway. Another constituent element is the digital replica of the physical entity. The defining of this digital replica already poses one of the great obstacles for digital twin.

As it requires the identification, collation and curation of numerous data sets that have the potential to provide insights into an asset’s operation and maintenance, the data sets that can be incorporated seem limitless. With data sources such as; BIM, GIS, Asset Management Systems, legacy asset information and sensor data all having the potential to provide insights into the components of an asset, their condition and usage.

In addition to this, there is also the option to incorporate a plethora of contextual data sets e.g. meteorological data, user experience and topographic data to provide greater context to the asset. All of these data sets have the ability to enrich a digital twin1 but their selection should always strive to aid the intended purpose of the twin.

Potentially one of the most crucial components that defines digital twin and sets it apart from the likes of a GIS or BIM model is the relationship between the physical and digital elements.

The bidirectional flow of data, sometimes referred to as a cyclical loop of “physical world-digital world-physical world” 2, is what enables the digital twin to create this great repository of up-to-date data. That can then be leveraged to derive greater understanding about an asset and allow for better decision making; with it providing insights into processes and usage that can then be exploited to optimise and automate where best applicable and possible3.

However, there is still a great deal that needs to be agreed upon. Here we look at a few of the areas that set the various definitions of digital twin apart that have been proposed thus far.

The fidelity

Understanding the rate of data transfer between the physical entity and the digital twin. Frequently there are calls for this feed of data to be in real-time so that a digital twin can inform and react as events occur. Simultaneously, there are calls for this relationship to be entirely based on the function or intended purpose of the digital twin, citing the cost -of both money and time- of establishing a real-time data connection may not be justified4.

The ability  

The advertised possibilities of digital twin seem to be endless, in order for it to be understood where their application will be more beneficial, it is these possibilities that we must fully comprehend. At current, there are four primary abilities for digital twin which are as follows5:

Simulation– Having the ability to understand the impact of a potential intervention before it takes place.

Control– Having oversight of an entire system and being able to make decision-based on this information, whether that be autonomously or with human control.

Monitor– Collecting data from an asset that enables the establishment of trends and interrelationships.

Predict– The ability to anticipate the need for repair or replacement long before it is due, from a better understanding of an asset’s lifecycle.

The Interrelationship of Digital Twins

It is crucial to remember that while the UK might have an agenda for the development of a national digital twin through the Centre for Digitally Built Britain. It must be remembered that this does not strive to create a single model of the entire UK, but instead looks to enable the ability to link various digital twins.

What this entails is developing a common computational framework that can be engrained in the development of all digital twins6.

As a result of this push there has been talk of completely reimagining our perception of what a digital twin’s form could be.

Moving away from the traditional view of a central platform that holds all the data about an asset and instead looking at creating a structured repository of data that applications with clear purpose can utilise to draw greater understanding7.

Levels of Digital Twin

One of the prevailing trends is the need to define levels of digital twin. Often these follow the format set out by the BIM levels, the success of which is also a contentious point of debate8.

However, one of the challenges that defining levels of digital twin encounters is that unlike with BIM development, is that there is not such a clearly defined chronological ordering of digital twin development.

Some research does make concessions to this, offering their levels more as elements or building blocks of development to be defined by the purpose of the digital twin9. Any move to attempt to define levels of digital twin does raise a series of questions that must be addressed if they are to be effective. The first of which being do we even need levels? And if so, how will they be deduced and assessed? Will they be enshrined in a standard?

Who gets to decide?

One of the big questions we still have to address is who should define the digital twin?

In the past, we have negotiated and tried to educate the procuring client to attempt to make use of new work processes and technologies to enhance their new assets10.

However, this has often relied on the client being forward-thinking, well informed or to a certain extent altruistic in their outlook.

Now with digital twin, the benefits could affect so many of the stakeholders throughout an asset’s lifecycle. Posing the question of how do we ensure that this is taken into account when developing a digital twin?

While we may not yet have all the answers to offer on these issues; what we have realised is that regardless of what is agreed upon, what is paramount is that we understand and develop the relationship between the function and the form of a digital twin.

This forms a large part of the research that is being conducted at BIM Academy. Which looks at the application of digital twin in linear infrastructure so that it can enhance the maintenance, upgrade and development of assets.

If you would like to know more or would like to be involved in the research being conducted at BIM Academy, please contact [email protected].

  1. Lu, Q., Xie, X., Parlikad, A. K., & Schooling, J. M. (2020). Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Automation in Construction, 118 (March), 103277.
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