AI-Driven Bridge Inspection & Risk Assessment
Automated visual defect detection, standards-compliant classification, and risk scoring for bridge infrastructure — from raw inspection images to actionable reports.
The Challenge
Bridge inspections generate large volumes of imagery that must be analyzed against detailed national standards. Inspectors manually identify and classify defects — cracking, corrosion, spalling, delamination, settlement, vegetation ingress, and more — then assess severity, write descriptions, and compile everything into standardized reports. This process is labor-intensive, inconsistent across inspectors, and slow to turn around. Risk scoring based on RAMS or RAMSSHEEP criteria adds another layer of complexity that is difficult to apply uniformly at scale.
Our Approach
We built a visual intelligence system that processes inspection imagery and automatically detects, classifies, and grades defects according to NEN 2767-4-2 and NPR 4768 standards. The system produces structured defect descriptions with severity scores, then generates standardized inspection reports that incorporate inspector input where needed. A risk assessment module computes composite Risk scores based on configurable RAMS and RAMSSHEEP criteria, giving asset managers a clear, auditable basis for maintenance prioritization.
Key Results
Technologies Used
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