Why Data Is the Missing Link for Fixing America's Infrastructure Problems

Utility companies call them PSPSs, or public safety power shutoffs. To electricity consumers, they're simply blackouts — dreaded long days without lights, air conditioning, internet, and other modern conveniences. For many small businesses, the impacts are felt in lost revenue. Implemented as a cautionary measure to prevent high winds from yanking down live electricity wires, PSPSs have become a necessary inconvenience in wildfire prone states like California and Colorado.

But what if they didn't have to be? What if there were better, less painful ways of preventing our electrical infrastructure from causing wildfires?

The challenges facing America's infrastructure are vast and well-publicized. In its latest report, the American Society of Civil Engineers gave the nation's infrastructure a depressing C-minus. In addition to the electricity grid's aging poles and transmission wires, and overburdened substations, long backlogs of maintenance and repairs exist for roads, bridges, railways, and water pipes. According to the American Road and Transportation Builders Association, 1 in 3 bridges across the country needs to be fixed or replaced, and 7 percent have been deemed structurally deficient. When initiated, large infrastructure projects often blow through their budgets and take far longer than projected.

The list of reasons for all this is familiar, ranging from insufficient funding and a lack of political will to excessive regulations and bureaucratic red tape. But we also need to start seeing our infrastructure problems through another, less obvious lens: the lack of information we have about our physical world and built systems. Advanced technologies now exist that allow us to collect and analyze vast, superhuman quantities of data — information that can help us plan and implement new projects far more efficiently and do a better job of protecting existing infrastructure.

Consider those hundreds or thousands of miles of wooden poles and transmission wires that represent wildfire risks during wind storms. Instead of killing the power running through the lines, what if we had comprehensive, granular data about these structures that would allow us to address the risks preemptively? Companies like Mach9, for instance, are using AI to create three-dimensional maps from massive sets of mobile lidar scans, which use light in the form of a pulsed laser to measure variable distances. This technology, which stands for light detection and ranging, can now be mounted on trucks or attached to drones and used to build highly accurate and detailed maps at a fraction of the cost and time. Utility companies could use such precision maps to identify a range of vulnerabilities: which poles have moved less than an inch, which wires are showing signs of wear, where vegetation exists within a 30-foot perimeter. Crews could be dispatched to replace the pole, trim the tree, or fix the line before they cause problems, potentially avoiding blackouts and disruption to millions of people's lives.

This is just one example of how the automated collection of big sets of data and the extraction of insights from that data can transform both the maintenance of existing infrastructure and the construction of new structures and systems. Lidar systems, for instance, can be game changing across a wide range of infrastructure projects, enabling the collection of data about physical systems at unimaginable scale. Although this technology has been used for decades, new affordable and lightweight solutions allow for much broader usage. Project surveys that used to take months or years, with engineers spending thousands of hours manually mapping, drawing, designing, comparing, and inspecting, can now be done in days. In addition, new types of advanced machine learning software can help us make sense of these three-dimensional maps by extracting insights from huge datasets far faster than humans could ever process. In one of the least digitized sectors of the economy, we're just beginning to see how these technologies can create a smarter, more efficient future.

Approaching infrastructure projects armed with better, more sophisticated geospatial mapping can solve some of the problems that have long plagued the sector. In addition to saving time and money on surveying, mobile-lidar-enabled maps give project designers and engineers a far more complete and detailed picture of every relevant aspect of the physical environment — existing built structures, topography, and vegetation. With this data, they can avoid many, if not all, of the errors, miscalculations, and redesigns that happen on so many complex or large infrastructure projects. Imagine if project engineers on Boston's Big Dig project had extensive structural and geological survey data ahead of time. Costly delays and redesigns could have been avoided, streamlining construction timelines and potentially saving billions in unexpected costs.

Precision mapping can also help optimize the amounts of material resources infrastructure projects need to consume. For example, being able to make predictions down to the cubic foot about how much concrete or asphalt needs to be poured (and how much recycled material from excavations can be used) can conserve precious resources, trim costs, and lower greenhouse gas emissions.

Major blind spots also exist underground. It's common for construction and excavation crews to hit buried lines they don't realize are there. These so-called utility strikes — of water or sewer pipes, telecom lines, electric cables, gas pipelines — can cause millions of dollars in damage and result in significant delays. Other times, project stakeholders simply lack the right information. At one point, construction workers on Maryland's Purple Line, for instance, a 16-mile rail project that's more than $4 billion over budget and five years late, estimated they could drill for a new sewer line at the rate of a few hundred feet per day. Instead, the hard rock they encountered allowed for only a few inches per day. Had project planners and engineers known this ahead of time, they could have properly accounted for the extra time and cost in the budget or found alternate routes with more favorable conditions. In combination with above ground mapping, geophysical sensors can now accurately assess a wide variety of underground features across large-scale areas, from soil, bedrock, underground utilities, groundwater, undermarked graves, and other anomalies.

As impactful as precision mapping data can be, reaping the benefits means getting it into the hands of the many stakeholders involved in infrastructure projects. Too often robust data sets remain siloed in narrow corners. The three-dimensional models of landscapes and structures that have been crafted from lidar are often only available to the project surveyors who collect it. Planners and engineers are likely to see the maps and reports generated from it, but they don't have the ability to interact with the data in order to create new maps, answer questions, solve problems, or make more informed decisions.

The answer to better collaboration lies in the wider use of advanced construction management platforms that use building information modeling (BIM) tools. These technologies allow project stakeholders to share data across a project and establish a single source of truth so no one has to wonder where information might live.

With the total price tag for fixing America's existing infrastructure pegged at $2.6 trillion, we clearly won't be able to do everything at once. Mapping and monitoring solutions can help decision-makers prioritize what to fix when, channeling money in the right direction and extending the life of an infrastructure asset.

Nothing is a more potent symbol of the urgency of our infrastructure needs than bridge collapses. When the Fern Hollow Bridge in Pittsburgh crumbled into a ravine in January 2022, it happened to coincide with President Biden's scheduled appearance in the city to discuss the recently passed infrastructure bill. Gazing at the wreckage, the president called the years of neglect to the nation's aging infrastructure “mind-boggling.”

A NTSB report later found that extensive corrosion caused by the continual accumulation of water and debris had caused a key plate on one of the bridge's legs to fail. What if we had been using cameras and sensors to consistently monitor these changes in corrosion, then applying machine learning software to detect small deviations across very large data sets to find fault points? There's a world in which these multibillion-dollar disasters don't have to happen. Digital precision mapping technologies can give us insight into where the biggest risks lie, allowing better decisions about how to prioritize, well before disaster strikes.

As the $1.2 trillion earmarked in the 2021 bi-partisan federal infrastructure law starts to make its way into road, bridge, mass transit, electricity grid, and broadband network projects across the country, these and other digital solutions will be needed to help spend taxpayer dollars wisely. In fact, the possibilities for a more efficient use of infrastructure resources are wide open. Consider the implications of being able to build infrastructure like light and heavy rail in clogged urban areas at a fraction of today's costs. A robust network of trains criss-crossing the greater Los Angeles metropolitan area, for instance, could potentially eliminate the city's monstrous traffic snarls. Plenty of political, bureaucratic, and managerial stars would have to align to make this happen, but it can start with the smarter, faster, more automated design and implementation of projects that precision mapping makes possible.