AI-Based Quantitative Concrete Crack Assessment and Structural Health Monitoring Framework

Visual inspection of reinforced concrete infrastructure is a fundamental pillar of structural asset management and lifecycle integrity assessment. Traditional inspection methods rely predominantly on manual visual surveys, handheld crack comparators, and subjective field notes, introducing substantial human error and safety hazards. This technical report presents an automated, quantitative computer vision framework designed for end-to-end structural crack characterization. The system integrates deep learning segmentation with morphological skeletonization (Zhang-Suen thinning) and Euclidean Distance Transformation to extract continuous centerline path lengths, maximum/average crack width profiles, and surface damaged areas. Furthermore, an expert decision engine benchmarks extracted metrics against international standards (ACI 224R-01 and Eurocode 2) to evaluate structural durability risk and automatically synthesize formal inspection reports. Experimental benchmarks on standard crack datasets demonstrate high fidelity, low error bounds, and strong operational feasibility for bridge decks, highway pavements, and tunnel linings.