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It’s Not a Digital Twin If It Can’t Pass These Two Tests 

<strong>It’s Not a Digital Twin If It Can’t Pass These Two Tests</strong> 

By Chris Harman

There is growing buzz about digital twins, and it’s easy to see why. While architects, engineers, and construction professionals have almost always relied on models to plan and deliver projects, static visualizations have real limitations. That’s particularly true for large-scale infrastructure that must operate seamlessly in a complex and dynamic environment, particularly one that could look and feel very different from the world we live in today.  

In recent years, we’ve seen a move from the use of static data to smart systems that use active data inputs. That is a meaningful advancement, and I’ve seen many call that a digital twin. In reality, a true digital twin is far more complex and must pass two key litmus tests.  

First, a true digital twin provides a technological representation or digital counterpart of current and/or planned real-world objects, creating a model that aims to be effectively indistinguishable from the source. The creation of dynamic simulations of the way structures interact with their environments goes beyond isolated infrastructure to offer interactive models of complex systems, grounded in real-world and often real-time data. This involves breaking down information silos to develop an integrated visualization of data that is typically spread out across departments and agencies.  

Consider the strides being made in water management. From Canada to Sweden, utilities are making use of detailed digital twins of water networks, linked to real-time data and controls, enabling operators and engineers to consider the entire water supply system when they plan repairs, upgrades, or respond to unusual situations. It’s not an easy problem.  

Networks of pipes have “folds” due to topography, “cuts” due to rail or highway corridors, and “strings” that link distant parts via tunnels or transmission mains. Disturbing any point of this surface affects the entire system in ways that are hard to predict. A digital twin can pinpoint the area where water is exiting the system, tell operators the fastest safe speed or sequence to close water valves to isolate an impacted area so they might only have to shut off supply to three buildings instead of 300 – and even generate a list of the impacted customers.   

That brings us to the second essential criterion for calling something a digital twin: it must offer analytics that help predict outcomes under various scenarios. At their heart, digital twins are “systems thinkers,” enabling teams to stress test the ways that different parts of the world interact and predict probable outcomes before infrastructure investments are made. 

For example, a true digital twin can model various approaches to reducing carbon emissions and project the cost and health impacts of each over the project’s total lifecycle. Or, it can promote more equitable access to transportation by applying the results of site-specific economic, social, and environmental impact studies to a real-time view of a transportation system, using AI to identify barriers and model likely outcomes based on projected demographic changes to various neighborhoods. 

If a model doesn’t include simulations and analytics, it shouldn’t be called a digital twin. Anything less can create a cloud of confusion in an industry that should thrive on clarity. Just as engineers don’t take a loose approach to engineering calculations, our field should use greater precision in the language used to categorize these new tools or risk diminished credibility for how we handle the hype.  

That’s particularly important when engaging a broader range of stakeholders and proposing uses that go far beyond project design. Digital twins enable efficiencies in ongoing testing, monitoring and maintenance of vital infrastructure. In very simple terms, the data rich model shows what has happened, real-time remote sensing shows what is happening, and predictive modeling and simulation assesses what will happen. For example, to facilitate land use and planning, WSP and Giraffe developed LandiQ, a digital twin that spans all of New South Wales in Australia.  

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That’s the critical point behind simulations and analytics: they should be used to address pain-points and drive strategic outcomes. Designers don’t need to add everything to a simulation simply because it’s technically possible. It’s more helpful to focus on data that helps solve problems, focusing on the use case and the three key dimensions of the size of the asset(s) to be twinned; the intent and life cycle stage of the asset, and maturity of systems and available data, whether static, dynamic, or predictive. 

Done well, digital twins can save time, cut costs, spur innovation, and enhance the quality of designs and decision-making. But only if we hold ourselves accountable to ensuring digital twins meet the twin features that define it. 

Chris Harman is the Director of Digital Delivery and Innovation at WSP, one of the world’s largest engineering and sustainability consultancies and the creator of a no-cost guide to digital twins