How AI-Powered Digital Twins Can Help Build Sustainable and Resilient Infrastructure

In “The Art of Taking Action,” author Greg Krech wrote: “We are all depending on each other, as we weave our lives, families and communities together. So, when we do not carry our weight, the fabric gets a buckle in it.” 

I like to think designing, building, and operating infrastructure plays a significant role in weaving our lives, families and communities together, as Krech said. With that, we carry the weight of advancing infrastructure for a more environmentally sustainable future.

As climate change continues to throw the world off balance, numbers show it is the designers, builders and owner-operators of infrastructure who need to lead the transition to low-carbon solutions, waste reduction and energy conservation. 

Infrastructure “is responsible for 79 percent of total greenhouse gas emissions and 88 percent of all adaptation costs,” according to a 2021 report from the U.N. Office for Project Services (UNOPS). 

No one can tackle these sustainability challenges alone. So, how do we work together in the quest to meet and exceed sustainability goals while we design, build, operate and maintain infrastructure? 

Empowering action for sustainable infrastructure

In 2015, all U.N. member states adopted the 2030 Agenda for Sustainable Development with 17 sustainable development goals for humanity and the planet. 

Using an infrastructure lens, we can zoom in on Goal 17 to empower organizations in their effort to design, build, operate and maintain infrastructure sustainably: “Strengthen the means of implementation and revitalize the global partnership for sustainable development.” 

Empowering effective implementation and collaboration through partnerships requires a data-centric approach. Most data, while abundant, is typically siloed in different formats, multiple repositories and various disciplines across the supply chain. Using a data-centric approach removes these boundaries and provides open access to extract value for problem-solving.

Likewise, digital twins — virtual models of real-world objects or infrastructure — enable organizations to visualize and analyze data to make more informed decisions. This decision intelligence is proven to reduce waste and lower carbon emissions in the construction and operation of infrastructure, and even conserve energy and water. 

From decision intelligence to sustainable solutions

In Finland, the smart city of Helsinki uses natural gas to power its central heating system. These systems work to warm individual buildings by transmitting heat from a central source through a network of insulated pipes. However, even with regular care, heating and water systems deteriorate with age, leading to leakage events that can cause service outages, energy inefficiencies and water insecurity. 

Action 1: Optimize data and expertise: One of the largest grid operators in Finland, Savon Sähkö Oy, partnered with an artificial intelligence laboratory, Silo AI, to develop a data-driven asset optimization service for the citywide pipe networks using a data-centric approach. Silo AI, experienced in smart city solutions and networks, unified data from numerous systems into a single view of overall pipeline health. 

Action 2: Let digital twins and AI do the work: The two organizations leaned heavily on digital twins and artificial intelligence to develop a single source of pipeline network data that owner-operators can use to improve operations and eliminate water leakages. 

Silo AI brought together all the data, including information on heating supply, water supply, and pipeline age, type and condition. The organization then developed a method to analyze and visualize the data in an intuitive user interface, leveraging Bentley Systems’ iTwin Platform.  

The AI-driven cooling performance analysis significantly improved energy efficiency and decreased fuel consumption, lowering the city’s heating network temperature by 3 degrees Celsius. 

By prioritizing maintenance activities where leaks are likely to occur, the pipeline owner-operator also restricts water loss and prevents blockages and outages while reducing maintenance costs.

Action 3: Reuse and recycle: For most practical purposes, any data can be duplicated in a single digital twin, which becomes a kind of centralized data repository that is open to all and easily consumed in its native format.

Sustainability realized

Resolving Helsinki’s central heating system challenges started with a data-centric approach, weaving AI and digital twin technology into its infrastructure operations to bring energy savings and water security to residents. An open, accessible digital twin platform where the disparate data is collected in one place empowers third-party companies like Silo AI and its customers to achieve sustainable development goals for infrastructure. 

This is a true example of the collaborative ecosystem needed to accelerate the Global Goals implementation.  And one of many implementations effectively changing how we design, build and operate infrastructure to reduce our impact on the planet.

Intelligent change agents

Infrastructure professionals face the enormous task of sustainably adapting the fabric of our world. The good news is we can rely on the intelligence of digital twins to help carry the weight. 

By embracing a data-centric and collaborative approach to problem-solving, we will enable an ecosystem of organizations that can advance infrastructure for a sustainable future. 

The road ahead may be complicated, but experience shows us the most effective changes start with small steps. Using intelligent digital twins, we can work smarter and faster together to achieve these goals. 

Image credit: Tapio Haaja/Unsplash