Interscale Content Hub – It’s often thought that the terms “digital twin” and “static model” are the same thing, which can cause confusion. That’s why we need to define what differentiates a digital twin from a static model.
While they’re both digital copies of physical assets, there are some big differences in what they can do and when you can use them.
A static model is a fixed representation, like a snapshot in time, while a digital twin is a dynamic, living entity that evolves alongside its physical counterpart.
Ok, let’s drill down and define more specifically.
Key Characteristics of Digital Twins
A digital twin is a dynamic and evolving digital replica of a physical asset, process, or system.
Therefore, the digital twins go beyond static models by incorporating real-time data, predictive analytics, and simulation capabilities.
In the context of the AEC industry, as highlighted in “From BIM to Digital Twins: A Systematic Review of the Evolution of Intelligent Building Representations in the AEC-FM Industry,” Min Deng and colleagues argue that digital twins could be a game-changer for the way we design, build, and manage buildings and infrastructure.
Meanwhile, Louise Wright and Stuart Davidson say in their paper, “How to tell the difference between a model and a digital twin,” that a digital twin has three main parts: a model of the object, an evolving set of data, and a way to update the model based on the data.
With those expert definitions, one of the main things about digital twins is how they can bring together data from different sources in real time, like sensors, IoT devices and external databases.
This data is constantly fed into the digital twin, so it can reflect the current state and behaviour of the physical asset.
So, a digital twin of a bridge could get real-time data on things like structural loads, traffic patterns and environmental conditions.
This would help engineers keep an eye on how it’s performing and spot any potential issues before they become bigger problems.
Another key benefit of digital twins is their ability to predict what might happen in the future.
By using machine learning and historical data, digital twins can predict how things will perform in the future, where there might be problems, and what maintenance is needed.
For instance, a digital twin of a building’s HVAC system could tell you when a filter needs replacing or show you where you could save energy based on how the building is used.
On top of that, digital twins let you run simulations and make optimisations in a virtual environment.
With digital twins, engineers and designers can test different scenarios, evaluate design alternatives and optimise operational parameters without affecting the physical asset.
This can mean big savings on costs, a safer environment and better performance.
For example, a digital twin of a manufacturing plant could be used to test out different production scenarios and spot where things might get stuck, so you can make the production process better before you actually start using it.
For insights on the security of digital twins, you can read “How Secure is Your Digital Twin? Is it a Sitting Duck? Define the Perimeter!“
Attributes of Static Models
Static models are different from digital twins because they’re fixed representations of physical assets.
They capture a moment in time and don’t change as the real-world asset does.
As Wright and Davidson say, static models are usually used for things like visualisation, documentation and basic analysis.
A static model can give you some useful insights into the design and geometry of an asset, but it doesn’t capture how it behaves or performs over time.
For instance, a static BIM model of a building can be used to visualise its design and generate construction drawings, but it can’t predict how the building will perform in different weather conditions or with different numbers of people in it.
On top of that, static models can’t integrate real-time data. They need to be updated manually, which can be time-consuming and prone to errors.
This means they’re not suitable for real-time monitoring or predictive analysis.
For example, a static model of a wind turbine can’t provide real-time data on things like wind speed, blade vibrations or power output.
So, static models are a cost-effective solution for projects or scenarios where dynamic data is not really matter.
They give you all the detail you need for lots of applications without the complexity and expense that come with digital twins.
Comparative Analysis: Digital Twins vs. Static Models
First of all, whether you go for digital twins or static models depends on what you need for a particular project.
For instance, digital twins can monitor a building’s structural integrity in real-time, predict when maintenance is needed, and simulate emergency scenarios to improve safety measures.
Meanwhile, static models are often used at the start of a project to show what the design and layout will look like.
Static models are great for showing stakeholders what the finished product will look like before construction starts.
Data Handling
Static models, like those often used in traditional Building Information Modelling (BIM), are basically fixed representations of a physical asset at a specific point in time.
Wright and Davidson (2020) also point out one downside of these models: they don’t automatically update to reflect changes in the real-world asset.
On the other hand, digital twins are designed to change and develop along with the physical asset.
They do this by bringing in data from different sources in real time, like sensors and IoT devices, as Deng and colleagues point out.
This means that digital twins can provide an accurate, up-to-date picture of the asset’s current state.
Predictive Capabilities
One of the best things about digital twins is how well they can predict what’s going to happen.
The combination of machine learning and historical data in digital twins means they can predict future performance, potential failures and maintenance needs.
This predictive capability is a real game-changer for asset management.
It allows for proactive maintenance and optimisation, which ultimately extends the asset’s lifespan and reduces operational costs.
Static models don’t have this predictive capacity, so they’re only really useful for design, visualisation and documentation.
Real-Time Applications
The real-time data integration and predictive capabilities of digital twins open up a whole range of real-time applications in the AEC industry.
Plus, digital twins can help you manage energy performance in real time.
Static models cannot handle these real-time applications because they are limited to representing a fixed point in time.
Cost Implications
You’ll usually have to spend more upfront on digital twins than you would on static models.
This is because you need sensors, data storage and advanced software for real-time data integration and analysis.
But, the long-term benefits of digital twins, like predictive maintenance and optimisation, can really save you money over the lifetime of the asset.
Static models are cheaper to set up and look after, but they don’t save you money in the long run.
For another point of reference, please refer to “How to Tell the Difference Between a Model and a Digital Twin: A Roadmap.”
Now, the Problem of Static Model to Digital Twins
A lot of AEC firms are keen to make the move to digital twins, but getting over the hurdle of moving from static models can be a big ask.
Setting up digital twins, getting all the data to fit together, and keeping them up to date can seem like a lot to handle.
To help you overcome these challenges, we at Interscale have developed specialised solutions for integrating static models with digital twins.
We’re here to make this cutting-edge technology accessible and beneficial for your projects. But how do we do that?
Our team of experts will be with you every step of the way, helping you with your static model and digital twin journey.
We start by doing a full risk assessment and then keep on top of things with management and optimisation, so you can relax knowing your projects are safe, reliable and delivering the best results.
You can get ahead of potential issues before they become costly problems.
Our data-driven insights help you improve how your assets perform, work more efficiently and make your business more sustainable.
We know we have a lot to offer. It’s fair to say it’s a bit confusing.
So we’d really love it if you could do your research and see for yourself what we can do for our many clients out there.
By all means, take a look at our Interscale BIM Management Support Service page.
We’d also be happy to run through a few more tweaks with you if you need to make a few more changes.
When would be a good time for us to grab a coffee and have a meeting? We’re here for you 24/7, whenever you need us.
In Closing
Digital twins are great for getting insights and predictions in real time, while static models are better for simpler tasks because they’re more cost-effective.
Knowing the differences is useful for professionals, as it helps them choose the right tool for their specific needs, which in turn ensures optimal project outcomes.
Interscale, a pro in BIM management and digital twin tech, can show you the ropes when it comes to managing digital twins and BIM.
This will deepen your understanding of what differentiates a digital twin from a static model.
