How cloud and AI are de-carbonizing road construction

By and Tilo Hahn | 4 minute read | June 22, 2021

aerial view of roads

When most people hear the term “smart roads,” they associate it with the use of various digital technologies to monitor and optimize the movement of traffic on them. These initiatives, which include the use of embedded devices to communicate with connected and ultimately driverless vehicles, is both ultra-important and fast growing. But for today’s road planners, this dimension is only half the story.

The other, more esoteric side of smart road development relates to the virtual DNA of the road itself: what the raw materials are, where they come from and how they all come together to build a road that lasts. In this context, smarter roads are defined as those that are designed, built and maintained to maximize environmental sustainability and minimize costs.

Smart roads start with smart decisions

Building smart roads is really about decision optimization. Priorities are established, data is gathered and variables are weighed against each other in a rigorous and consistent way. Equally important is the process itself, which brings all the participants in road building into a kind of ecosystem for collaborative, collective decision making. The key players include the governmental infrastructure authorities that “own” the projects, financial institutions that fund them, and the design and engineering firms that guide the technical end.

Then, of course, there are the material suppliers themselves. As with any collective decision-making process, one of the inherent barriers to road-building optimization might be called the Tower of Babel effect, in which key data inputs are incomplete, inconsistent or otherwise fragmented. The fact that materials are as a rule provided by local contractors—often affiliated with larger firms—has long made the vision of smart road decision optimization hard to realize.

Toward new level of decision optimization

As a longtime innovator in the building materials space, LafargeHolcim saw the opportunity to drive a new level of smart road optimization by bringing our advanced AI-based decision technology to market as a service. Drawing from our expertise, we had assembled a set of capabilities that had the potential to transform the way road-building ecosystems accessed, shared and acted on the key data inputs they relied on.

We recognized that to succeed with this game-changing digital service offering, we needed to formulate a comprehensive go-to-market strategy that was aligned to the unique needs of decision makers at all levels of the road-planning ecosystem. We saw IBM Services as having the uniquely diverse competencies needed to get the job done.

Flexibility through Kubernetes cloud containers

In creating the service, known as ORIS, our go-to-market efforts followed two parallel, complementary and ultimately convergent paths. The first was focused on creating a digital platform—a technical foundation—for delivering the service. The second was to transform a set of powerful capabilities into an intuitive and satisfying user experience.

From a platforming perspective, our chief priority was that customers could run ORIS applications on any cloud platform, with the same functionality and level of performance. To achieve that goal, a team from IBM GBS Cloud Application Integration—following the IBM Garage methodology—adapted each of the component applications to make them container-based microservices, with Kubernetes used to run and manage their container-based workloads. On the customer-facing side, the best way to illustrate the work of the GBS iX team that designed the end-user experience is to show it in action.

Whatever their specific role, people working in the road-building ecosystem use ORIS to optimize decisions—such as which materials to use for a stretch of highway and which material supplier to source it from—based on their priorities they establish. A government road planner may, for example, want to minimize the overall carbon footprint of a project, while at the same time minimizing road maintenance costs over the next five years. After setting those priorities through the system’s dashboard interface, the planner receives detailed options along with supporting metrics. For the same project, a road engineer may approach the decision with a different set of criteria—say, a higher priority on using locally recycled material—and receive a different set of suggestions.

How cloud and AI add up to greener road building

What’s most critical is that both conclusions are driven from a common set of data, metrics and frameworks. Put simply, ORIS enables everyone in a particular road planning ecosystem to work with the same set of facts—however complex and detailed—and provides the AI-powered analytics to translate them into concerted action.

Any good go-to-market strategy has a strong customer value proposition at its core, and for the ORIS solution it’s powerful one. By optimizing the earliest stages of road design decision, ORIS has shown the potential to reduce overall road project costs by up to 33% and overall carbon emissions by up to 50%, while at the same time tripling road durability and lifespan.

We also recognize that the ability to deliver this value to customers is ultimately predicated on having the right technology platform, one that is flexible enough to conform to each customer’s specific cloud preferences. Our joint decision with IBM to build ORIS as a series of containerized applications—orchestrated by Kubernetes—ensures not only that it can be deployed on any cloud, but that it can also minimize the cost of maintaining and expanding the service over time.