Ames Pioneers AI for Infrastructure Management

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  Published in Science and Technology on Thursday, February 5th 2026 at 12:37 GMT by KCCI Des Moines
      Locales: Iowa, UNITED STATES

Ames Leads the Way: AI-Powered Infrastructure Management Expands Beyond Street Repairs

Ames, Iowa - What began as a pilot program focused on optimizing street repairs is rapidly evolving into a comprehensive, AI-driven infrastructure management system for the City of Ames. The initial success of the AI-powered pavement assessment tool has spurred city officials to explore and implement similar technologies across a broader range of public works departments, signalling a potential revolution in how municipalities approach infrastructure challenges nationwide.

Just two years ago, the Ames Public Works Department relied heavily on manual inspections and limited pavement scans. This reactive methodology, while functional, proved susceptible to human error, subjective assessments, and ultimately, inefficient resource allocation. Roads would often fall into disrepair before being flagged for attention, leading to more extensive - and expensive - fixes down the line. The introduction of the AI system, initially focused solely on street assessment, changed this paradigm.

The core of the system lies in its ability to analyze data from high-resolution pavement scans, employing a sophisticated algorithm that identifies and quantifies key indicators of road deterioration. These include not just visible cracks, but also subtle changes in surface roughness, depth of potholes, and even the types of cracks, which can indicate underlying structural issues. The AI assigns a priority score to each street segment, creating a dynamic and objective ranking that informs repair schedules. This data-driven approach ensures that limited budgets are deployed to address the most critical needs first, maximizing the lifespan of the city's road network.

However, the impact extends far beyond simply patching potholes. The success of the pavement assessment AI has encouraged the City of Ames to expand its application. Currently, the Public Works department is integrating the technology with data from underground utility inspections. Using robotic crawlers equipped with sensors and cameras, the system can now assess the condition of sewer pipes, water mains, and storm drains, identifying leaks, corrosion, and structural weaknesses before they lead to service disruptions or catastrophic failures. The AI is learning to correlate surface anomalies - such as sinkholes or persistent soggy patches - with underground infrastructure issues, predicting potential problems before they manifest above ground.

"We initially saw the AI as a tool to improve our road repair process," explains Sarah Chen, Director of Ames Public Works. "But it quickly became apparent that the underlying technology had much broader applications. We're now looking at a fully integrated system where all our infrastructure data - roads, utilities, bridges, even city-owned buildings - is fed into a central AI platform."

The next phase of the project involves incorporating predictive maintenance capabilities. By analyzing historical data, environmental factors (like freeze-thaw cycles and rainfall), and real-time sensor readings, the AI can forecast when infrastructure components are likely to fail, allowing the city to schedule preventative maintenance and avoid costly emergency repairs. This proactive approach is expected to significantly reduce long-term maintenance costs and improve the reliability of critical infrastructure services.

The city is also exploring the use of drone technology equipped with thermal imaging and LiDAR sensors to assess the condition of bridges and other elevated structures. This data will be fed into the AI platform, providing a comprehensive overview of the city's infrastructure health. Furthermore, Ames is collaborating with Iowa State University researchers to develop AI algorithms capable of optimizing traffic flow and reducing congestion, contributing to a more sustainable and efficient transportation system.

The Ames initiative is attracting attention from cities across the Midwest, many of whom are sending delegations to observe the system in action. The city is actively sharing its experiences and lessons learned, hoping to foster wider adoption of AI-powered infrastructure management practices. Ames is demonstrating that smart infrastructure isn't just about implementing new technologies, but about transforming the way cities plan, maintain, and invest in their future.