A wide range of industries and projects are deploying digital twin technology, from manufacturing to city infrastructure. The goal is to create a virtual representation of physical objects, processes, and systems. These digital models can be used to monitor performance, predict future behavior, optimize resource allocation, and automate processes to increase efficiency.

The digital twin is a blend on multiple technologies, including simulation, AI, and virtual reality. The appeal of mixed reality goes beyond novelty and aesthetics. Companies are using digital twins to redefine the way they work, create, design, construct, and communicate. In the US, GHD Digital built an immersive digital Firefighter Incident Response Simulation Tool to help the National Fire Protection Association educate firefighters and enhance NFPA’s firefighter training, preparedness, and safety.

We caught up with GHD Digital – a company that helps with digital transformation – to dig deeper into the use and value of digital twins. GDH Digital offered comments drafted by James Barrow, the mixed reality and digital engineering solutions lead at GHD Digital, and Richard Leslie, the mixed reality and digital engineering solutions delivery lead for North America at GHD Digital.

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What are the benefits of using digital twins? What are some applications for digital twins across various industries?

James Barrow and Richard Leslie: The terminology “digital twin” can mean many different things to an asset owner across the lifecycle of their asset. Rather than focusing on digital twin as a singular product, it is better to think of it as a digital twin system or ecosystem. Being able to define and understand what a digital twin represents to that asset owner and its various users is one of the critical early steps on a digital twin journey.

It can be beneficial to go through a road map process to identify where you are as an organization in this process and what steps are needed by your organization to have a successful and productive experience, learning to or understanding how you are walking before you try to run. Taking the important time to assess needs, define use cases, understand data maturity and organizational readiness are a few of the important steps to explore on a journey towards successful digital twin implementation. This will additionally help to identify any redundant steps or inefficient duplication in process, systems or data, and help you better realize the targeted benefits from your investment.

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Through this process it will often be identified that some of the building blocks and critical components to achieve a digital twin system implementation may already be in place or being used.

Getting data standards right

Like many digital environments, to truly realize outcomes and benefits, you need to ensure that you have appropriate data standards in place including Asset Information Requirements and Exchange Information Requirements, defined by the operational requirements of the organization. This helps ensure success is achieved through establishing an integrated, information-rich environment, that is symbiotically linked to the physical asset. It also requires a coordinated and transformative process within the organization to allow digital processes, tools, and technological innovations to be effectively deployed to support the ongoing lifecycle needs of the asset.

The benefits that can be derived from a digital twin are broad and change based on the maturity and nature of the project or asset, but a critical foundational benefit is improving the way information is accessed, visualized and reported, and the insights that can be gained through this process. We understand information better when presented visually and spatially, and through immersive 3D environments, we can better interpret information.

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At this early stage of digital twinning, a key benefit for clients is to be able to see their information within a 3D environment, mapped to the current state of their assets. For most clients, their assets vary in age, style, fit-out, etc., and are often located across multiple sites. Being able to virtualize their assets improves their planning programs, enables the ability to test through digital rehearsals, or improves the management and consistency of inspection programs more easily from the desktop, which can bring substantial cost benefits.

Connecting your delivery and operational ecosystems through digital twin systems helps find efficiency in operational readiness for asset commissioning and handover, predictive and scheduled maintenance in operations, and reduced downtime and safety incidents through remote asset inductions, briefings and inspections.

Additionally, all of the data collected through these processes, compliant with your defined organizational standards, will help achieve broader operational maturity and tracked digital legacy for your assets.   

Are there environmental benefits from using digital twins?

Barrow and Leslie: Sustainable design is one area where digital twins are providing significant progress. Creating a sustainable and resilient built environment starts with the design stage, which can leverage digital twins and virtual simulations to optimize the design in as early as the conceptual stages. This not only balances the aesthetic appeal, but also makes it energy-efficient and more purpose-built with less waste.

By optimizing multiple objectives, like maximizing natural light while minimizing energy consumption, generative AI can assist the design optioneering and multi-criteria analysis processes led by design professionals, yielding more designs and options, that are sustainable, cost-effective and visually striking, serving as a springboard for the use of novel materials, structural forms and construction techniques that meet sustainability, aesthetic, and functional goals.

Digital twins in housing

GHD Digital worked with Wellington City Council (WCC) a pioneer in smart cities and digital twins, to help gain deeper insight into how digital twin capabilities could be leveraged for insights into housing development planning, yields and feasibility. We helped WCC realize the different density scenarios and their impact and explore questions like how expansion plans might impact water requirements, energy use, CO2 emissions, and the impact of density on the broader area, such as transport and green space through the implementation of a geographic information system and data driven digital twin solution.

Virtual simulations can help quickly generate numerous design alternatives based on specific criteria such as cost, space utilization, and environmental impact, helping architects and engineers find the most efficient and sustainable solutions. These models provide the ability to have real-time impact and provide visualization into water resource management, energy optimization, pollution control, and waste management in a way that improves engagement and understanding. With many geographic regions under increasing density and growth pressures, linking your planning, design and operational environments, mapped with you your sustainability targets, can assist in better understanding and forecasting your demands, and defining land use yields and approaches to achieving those targets.  

How are digital twins impacting design in AEC industries?

Barrow and Leslie: The pressure on asset owners to build and operate in a smarter, more sustainable, and resilient manner is continuing to ramp up in the built environment. As the architecture, engineering, construction (AEC) industry is expanding its use of data, technologies, and advanced digital platforms to develop, design and deliver assets, opportunities are increasing for the smarter operational management of buildings, major and critical infrastructure assets. Using digital copies of assets is a way to future-proof your asset as the virtual model not only provides an evolving or static representation of the physical asset, it can also be integrated with real-time data.

Like many asset-intensive industries that are adopting digital transformation, the built environment is transitioning towards a computing platform. This in turn, will help asset owners improve their customer experience by being able to better understand their customer’s needs and optimizing enhancements to existing facilities, operations, and services which help drive the innovation of new business models.

Visualize and simulate

For example, you can visualize and simulate the design and delivery process for a building or infrastructure asset: what goes into the structural design, how it is built, operations management, track embedded carbon, and optimize other aspects of the design that can make that structure more energy efficient. Through strong definition and implementation of appropriate data standards, you can plan, forecast, and simulate various aspects of the value chain, through the use of virtual models and relevant data to optimize process through design, delivery, and in daily operations. AI and machine learning roles are also becoming more valuable in prefabrication and modular construction, streamlining the design of interlocking modules and reducing on-site adjustments, all contributing to better defined and built outcomes.

This advanced planning and execution insight can ensure more efficient resource allocation and increase the likelihood of project success and sustainability, and ideally a reduction in time and cost overruns. The ability to simulate and visualize complex datasets can also enhance communication among stakeholders, promoting a more collaborative and consensus-based approach to decision-making. The result is a more agile process that swiftly adapts to changes, shortens project durations and allows AEC firms to take on more projects or focus on higher-value tasks with greater certainty.

How do AI and digital twins interact?

Barrow and Leslie: For AI to have any benefit, it needs data. The more data, and in particular well managed and curated data, we have available to support an asset, the greater the opportunity to derive the use of AI. A digital twin is about the linking of data to assets in a push-pull live environment. Therefore, this naturally establishes an environment that allows AI to become an effective tool within the digital twin functionality.

Generative AI could also enhance decision-making by providing data-driven insights and simulations that predict the outcomes of various design and construction scenarios with precision. Through predictive analytics and scenario simulations, AEC professionals would be able to evaluate project feasibility, foresee potential challenges, and make well-informed decisions to mitigate risks.

GHD Digital released a report on the transformative capabilities of AI for the AEC industry: Generative AI: Transforming the AEC Industry. 

Can you provide some case studies/real-world examples of digital twins?

Barrow and Leslie: Outside of some specific industries which have experienced digital maturity for some time, digital twin is in its infancy more broadly. Some projects have used various capabilities that digital twin systems provide to enhance the way they design and deliver projects, prepare for handover to operations, or coordinate daily operations and asset management.

GHD has been actively involved with several clients in their journey to transform their business and manage data better along their digital twin journeys. For example, we are working with several municipal water agencies to digitalize their sites. They are doing this to improve their understanding of the condition of their current assets, improve assessment of how their assets are functioning, and help them prepare for future demands – both in preparation for water security and increased demands, along with legislative changes, such as PFAS requirements.

Effective operational management

Many agencies are undergoing digital and operational maturity assessments and transformations as part of their evolution and through the introduction of contractual obligation of ISO19650 Information Management Standards and defined asset information requirements (AIR) through asset delivery, they are positioning themselves for effective operational management of new assets. Transit agencies are also looking at how digital twins can help them manage their assets better, either by unlocking greater outcomes under reduced funding, or helping to mitigate risks of failure from aging assets.

For example, in the UK we are supporting Network Rail in the shift to digital railways. The initial work is targeting improved collection, integration, and use of data to support earthwork inspections and minimize risks of slope failures. This solution is enhancing inspection activities by streamlining and automating processes along with providing greater coverage of site studies. The team has developed a spatial online visualization tool that allows remote full-site inspection and instantaneous comparison of the changing conditions along the track, ensuring effective mitigation measures to be implemented quickly.

Can you explain the different types of digital twins?

Barrow and Leslie: Yes, here are the types:

Pre-digital twin: Created during the upfront engineering, it is a virtual prototype of the envisioned physical asset. The elements, their respective relationships, and actual data is created as early as the planning and design stage, then carried over through to the end of the construction phase. This helps designers and engineers to plan and mitigate risks in the design and construction stage. Currently, the industry refers to these models as Building Information Modelling (BIM) or digital engineering models.

Informative digital twin: A virtual replica of the completed physical asset that captures similar characteristics of the pre-digital twin and incorporates maintenance, asset health, finance, resources, and performance data of the physical asset.

Performance digital twin: Connects digital and physical versions of the asset using sensors or Internet of Things (IoT) devices. This allows for real-time data of the physical assets to be reflected in the digital model. This allows information like status data, performance, and operational health from the physical asset to be constantly monitored.

Autonomous digital twin: Automates discovery of new knowledge and insights through data mining and machine learning which enables it to continuously learn and improve over time. This generates predictive insights from the asset which lead to reduced downtime, optimized energy consumption, and the development of new business models that foster continuous optimization processes and deliver true value.

What are common challenges with digital twins?

Barrow and Leslie: Firstly, and foremost, understand what a digital twin means for you and your organization. This will help you understand better what your need or readiness for a digital twin is. Where are you on this journey, maybe you have not yet started or by way of early adoption or ways of working, may already be on your way there. This is often overlooked with the need to keep up with latest market trends. Substantial investment can often be wasted because the core question of ‘why’ is not considered nor understood and the steps to mapping out your needs, engaging with your stakeholders, and defining your roadmap and requirements are not taken or defined.

Data is a critical input. Some of the biggest challenges come from poor data management of a lack of data standards. Know your data, what you need from your data, and where data gaps exist. Currency, accuracy, and multi-use formats are critical to a successful digital twin implementation and realizing benefits.

Interlinking or the connection of legacy platforms between new, current, and aging assets is critical. A lot of historical information does not exist in a form suitable for use in a modern digitally interconnected environment and it will take time and investment to implement new systems or translate this information into a more suitable form.

Adapting to digital twin technology

Internal capabilities are also important. The success of digital twin technology requires a transformation to occur. The organization’s processes, resourcing, structure, and investment model will need to adapt to support a digital twin future. The focus is often too heavy on the technology.

A number of digital twin initiatives over recent years have failed because there has been little appreciation of the time and effort required to maintain these environments past their initial standup. For example, a digital twin developed in 2021 is not relevant to the asset in 2024 unless an established process is in place to ensure the data is kept up to date and the digital and physical states match. Maintaining user confidence in the information the digital twin provides is crucial to long-term success.