A scientist at the University of Houston’s Cullen College of Engineering is helping reveal the world’s weakest bridges — and how to fix them before it’s too late.
In a study of 744 bridges across the globe published in Nature Communications, Pietro Milillo and an international team found that structures in North America are in the poorest condition, followed by those in Africa. Their solution could change how infrastructure is protected worldwide: monitoring bridge stability from space to detect problems before they become disasters.
The grim bridge news correlates with the age of the bridges, as there was a peak in North American bridge construction in the 1960s, meaning many of these bridges are near or beyond their design lives. The solution — to use spaceborne monitoring of bridges via Synthetic Aperture Radar — offers frequently acquired, high-resolution imagery with global coverage and extensive historical archives.
“Our research shows that spaceborne radar monitoring could provide regular oversight for more than 60 percent of the world’s long-span bridges,” said Milillo, co-author of the study and an associate professor of civil and environmental engineering at UH.
“By integrating satellite data into risk frameworks, we can significantly lower the number of bridges classified as high-risk, especially in regions where installing traditional sensors is too costly,” he said.
The international team, including Dominika Malinowska, Delft University of Technology (TU Delft) and the University of Bath; Cormac Reale and Chris Blenkinsopp (University of Bath), and Giorgia Giardina (TU Delft), used a remote sensing technique called Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR). The researchers revealed it can complement traditional inspections by detecting millimetre-scale displacement caused by slow-moving phenomena like landslides or subsidence or detecting anomalies across spatially extensive regions.
Bridges are among the most vulnerable parts of the transportation networks, yet traditional monitoring has limitations. In-person visual inspections can be subjective and expensive, while inspectors may miss signs of early deterioration between typical bi-yearly inspection cycles. Structural Health Monitoring (SHM) sensors offer a more cost-effective solution, but their implementation remains limited primarily to newer bridges and specific concern cases, with the study confirming that they are installed on fewer than 20% of the world’s long-span bridges.
This leaves a significant gap in the understanding of the structural condition of bridges.
A solution from the skies
“Remote sensing offers a complement to SHM sensors, can reduce maintenance costs, and can support visual inspections, particularly when direct access to a structure is challenging,” Millilo said. “For bridges specifically, MT-InSAR allows for more frequent deformation measurements across the entire infrastructure network, unlike traditional inspections, which typically occur only a few times per year and require personnel on the ground.”
Malinowska added, “While using MT-InSAR to monitor bridges is well-established in academic circles, it has yet to be routinely adopted by the authorities and engineers responsible for them. Our work provides the global-scale evidence showing this is a viable and effective tool that can be deployed now.”
Researchers found that incorporating data from MT-InSAR, particularly pixels with stable scattering properties known as persistent scatterers (PS), into risk assessments provides more accurate risk registers through uncertainty reduction, enabling better risk prioritization and maintenance planning.
The method proposed by this international research team integrates the availability of monitoring from both SHM sensors and satellites like the European Space Agency’s Sentinel-1 or the recently launched NASA NISAR into a bridge’s structural vulnerability score.
By providing more frequent updates than typical visual inspections, this combined monitoring approach reduces uncertainty about a bridge’s current condition, leading to more accurate risk classification.