Pavement Management AI
AI analysis for pavement condition assessment and maintenance optimization.
Definition
Pavement Management AI analyzes pavement condition data to optimize maintenance and rehabilitation decisions. It uses pavement condition indices, distress data, and traffic information to develop cost-effective pavement management strategies for roads and parking lots.
In Depth
Pavement management systems track the condition of road and parking surfaces over time, predicting when maintenance or reconstruction is needed based on current condition data and deterioration models. AI improves the prediction accuracy and optimizes the maintenance strategy across a pavement network.
Condition assessment uses AI to analyze pavement surface imagery (from vehicle-mounted cameras or drones), identifying and classifying distress types — cracking (fatigue, thermal, reflective), rutting, potholes, raveling, and patching. The AI-identified distresses are scored against the pavement condition index (PCI) methodology, producing objective condition ratings that are more consistent than visual inspections by different inspectors.
Examples
Analyzing pavement condition
Optimizing maintenance timing
Planning rehabilitation
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
Pavement Management AI analyzes pavement condition data to optimize maintenance and rehabilitation decisions. It uses pavement condition indices, distress data, and traffic information to develop cost-effective pavement management strategies for roads and parking lots.
Analyzing pavement condition. Optimizing maintenance timing. Planning rehabilitation.
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