Highway and bridge projects have always depended on accurate measurement. Recent innovation has changed how much of a project can be measured, how often and what teams can do with the data. Laser scanners, mobile mapping systems and GNSS-guided machines now capture existing conditions and finished work in far more detail than traditional surveys. Artificial intelligence (AI) tools can help engineers and contractors sort through that data to find problems before crews reach them.

Troy Dahlin, vice president of construction for the U.S. and Canada at Leica Geosystems, has seen that shift from the contractor's side and the supplier's side. Previously, he led the company's heavy construction business in North America. Before that, he held senior leadership roles at construction firms in the Northwest, overseeing budgets, staffing and operations. That background shapes how he talks about technology. He focuses less on what a tool can do in a demonstration and more on whether it keeps a job on schedule, within tolerance and on budget.

Leica Geosystems, based in Switzerland and part of the Hexagon group, makes surveying instruments, reality capture systems and machine control technology. Its equipment is used in road construction, and the company has offered stringless paving guidance for 30 years.

In this Q&A with Roads & Bridges, conducted over email, Dahlin explains how reality capture and AI are changing preconstruction, from comparing roadway alignments to catching conflicts between existing and planned utilities before excavation begins. He also discusses why machine control delivers the most value when it spans entire fleets rather than single machines. He describes how repeated scans can track a retaining wall's movement or a bridge crack's growth over time, and how AI assistants could help newer workers learn the job faster.

This interview has been edited for length and clarity.

R&B: Artificial intelligence is rapidly changing how transportation projects are designed, built and maintained. How is AI transforming the way engineers and contractors use reality capture data from laser scanners, mobile mapping systems and other surveying technologies, and what opportunities do you see emerging over the next five years?

Troy Dahlin: There are two sides to this: let’s start with preconstruction. Working with the engineers, combined with reality capture and AI, makes the entire process attractive. Let’s say you want to build a new section of road connecting point A to point B: you need to determine what’s the shortest path, what’s the path with the least amount of earth work, what path is most conducive to drainage, etc. If you were to do this conventionally, you’d be looking at massive surveys and weeks of calculations and work. With reality capture, in partnership with AI tools, it becomes quick and easy to suggest various alignments that are beneficial for value engineering. You can validate design fit with existing conditions to reduce potential delays and cost overruns in the field.

What we see coming next is even better value engineering. Soon the engineer will be able to consider an even broader spectrum of options and select the one that’s best. Using AI tools in preconstruction to better identify potential issues is game-changing.

On the construction side, there’s the whole idea of using point clouds above and below the ground to identify clashes between preexisting conditions: It’s all about scenario planning, creating what if scenarios. You can look and see that, based on the existing project design that’s been planned out, two pipes might intersect (one existing and one new). Using AI tools and digital twin data long before construction, a contractor can send data identifying these potential clashes and offer the opportunity to redesign a section of the build. This way, when construction starts, there’s no time lost, fewer cost overruns, and fewer delays on the project being delivered.

R&B: High-precision measurement has become the foundation of digital construction. How have advances in GNSS, total stations and 3D laser scanning changed the accuracy, speed and confidence with which highway and bridge projects are delivered, and what does that mean for owners and contractors?

TD: By performing consistently within specification tolerances, contractors have continued to have increases in adoption meaning that more people/assets are increasing efficiency: a rising tide that lifts all boats. Look at the technical opportunities within the specification level of the project: you use GNSS when the specification is plus/minus a tenth of a foot. Then you use total station guidance when the tolerance is much tighter. It’s important to have options that fit within the specifications of the project. 

Whereas not long ago a contractor would have one machine (such as a grader) using machine control, now we’re seeing multiple machines working more efficiently and more accurately and the entire performance level of the job goes up, versus the performance of only one machine going up, as we’ve seen in the past. 

As adoption rises, we can expect projects to become more efficient, built closer to specification, to come in on budget. Adoption is driving the accuracy and confidence with which highway and bridge projects are delivered. It’s been a long adoption curve that’s taken construction technology from novelty to necessity and then made it a necessity across all machines.

R&B: Transportation agencies and contractors are collecting more field data than ever before but turning that information into actionable insights remains a challenge. How are reality capture, surveying and AI helping project teams move beyond simply collecting data to making faster, better decisions throughout a project's lifecycle?

TD: Let’s revisit preconstruction and how today’s technology can identify challenges before work begins, helping prevent costly downtime. This is huge. There are things you don’t want to find, don’t want to have happen on your jobsite: like having an excavator operator hit solid rock four feet from where he needs to be. Getting the engineers involved early to help solve the problem will be consequential in the best way.

Looking at QA/QC ability through the lens of reality capture reveals its importance. Consider a standard retention wall: there’s a very specific place that this wall must be installed, the footing of it must be at a precise elevation, etc. Monitoring this process with traditional methods won’t tell the whole story: using reality capture, the entire surface can be scanned to ensure the wall is where it needs to be. Two years later, the wall can be scanned again to reveal even minor movement the eye wouldn’t catch and solve. This allows a growing issue to be addressed early and at less cost than when the change is visible and failure is imminent.  Reality capture is taking the job beyond the as-built and bringing successful follow-up maintenance into the picture. 

This is a pretty big deal, because when bridge inspectors go out to do their jobs, they visually inspect and attempt to document cracks in a bridge. Vertical and horizontal cracks aren’t great, but it’s the diagonal ones that are most concerning. Using reality capture tools to document characteristics of the bridge and create a precise measurement – and then track the progress of the defect – is the answer to head off problems before they become major. Making maintenance decisions more intelligently and earlier means the fix can be deployed at a fraction of the future cost.

R&B: Looking ahead, what technologies do you believe will have the greatest impact on surveying and measurement for roads and bridges over the next decade? How do you see AI, automation, digital twins and high-precision positioning working together to reshape the way transportation infrastructure is planned, constructed and managed?

TD: Prioritizing inspection and repair means using good reality capture tools. Those places that are hard to measure, like the underside of a bridge deck, are easily revealed via reality capture.

On the design/bid aspect, reality capture with AI interface gives the ability to check off all of these tasks – like clash detection - pre-construction, versus stopping the project to figure out a solution.

During the construction phase, AI assistance components are changing the game. Look at how some companies are incorporating AI assistance into their products: from Siri-like in-cab commands to setting a height limiter, OEMs are moving quickly into the future.

On the technology side, companies like us are starting to incorporate AI into job functions like safety, of course, but it’s also within the searching feature of total robotic station to show the most logical path of the prism by identifying the most logical target areas to reacquire connection with the prism if it’s lost. 

I’m excited to see how AI assistants will allow machines to complete their tasks in a much more efficient and repeatable manner while also bridging the knowledge gap. Imagine a new grade checker on a road project, getting various tasks assigned to them in a short period of time. Staking the edge of pavement, base layer, bottom of ditch, or catch point of the slope.  Soon, I imagine that they will be able to ask their AI assistant to help show each aspect and how they relate to each other.  This is not only efficient, but increases learning, accelerates experience, and raises the knowledge bar for all the new people who join the industry.

On the QA/QC side, something else to watch for is mobile mapping. Take a 25-mile section of a freeway and map it with a mobile mapper. Then do it again at an interval of time. What’s revealed are any deviations to the road grade, where vegetation might be crowding the road or covering signage. Combined, this information builds an accurate inventory of what needs to be maintained, while simultaneously contributing to liability prevention for municipalities. 

Another example: On mountain highways, snowplow usage can lead to significant amounts of damage caused by restricted visibility. How best can a road crew identify the damage, where signs might be missing, etc.? Mobile mapping. It’s the perfect solution for major highways and bridges; even smaller, more rural areas can take advantage via the use of a service provider.