Russian researchers are exploring the use of neural networks to detect hidden road defects that require maintenance. This approach is reported by TASS, quoting Maxim Bartenev, head of Poddorozhny Hardware and Software Complex LLC.
Not every road problem is visible at first glance. Water can erode voids under the surface, or the road foundation might experience significant movement over time. When these issues are identified early, scheduled repairs can address them before they worsen. A key part of the strategy is to use ground-penetrating radar that broadcasts radio waves into the ground and interprets the returning signals to map what lies below the surface.
According to the team, a single data collection cycle will combine neural networks with large databases produced by a deep georadar system that can cover up to 200 kilometers of road per day. By examining depths of 30 to 40 meters, the system aims to pinpoint areas susceptible to water infiltration, voids, and potholes—zones that are currently problematic or may become problematic in the near future. The machine learning models are expected to forecast the evolution of these issues, enabling proactive maintenance planning. A prototype trailer-shaped unit is slated to be ready by November, with data collection and preliminary results anticipated in March of the following year, as disclosed by the company’s director.
For scanning, the radar-equipped trailer will traverse roadways at about 40 kilometers per hour and can be towed by a standard vehicle. The radar emits signals from a detector positioned roughly two centimeters above the road surface, allowing the system to log vast volumes of information for neural network analysis. The resulting algorithm will estimate the timeframe before structural failure occurs, helping authorities prioritize repairs and allocate resources efficiently. The pilot deployment is expected to roll out in the Moscow region in the near term, providing a testbed for refining both the hardware platform and the AI models.
In related observations, scientists continue to advance the capabilities of non-destructive testing and predictive maintenance in transport networks, aiming to extend road life, reduce unexpected outages, and improve safety for drivers across the country and beyond. This research aligns with broader efforts to integrate smart sensing, data analytics, and automated decision-making into infrastructure management, thereby enabling more resilient and cost-effective road systems for communities in Canada and the United States. The collaborative work among technology developers, civil engineers, and municipal planners illustrates how data-driven methods can translate into concrete physical improvements on real-world road networks.
Note: This overview reflects ongoing reporting on neural network applications in road maintenance and does not constitute a final product release or a guaranteed performance claim. Real-world results will depend on field conditions, regulatory approvals, and continued funding for large-scale testing and deployment, with ongoing updates provided by the implementing teams and participating agencies.