Ensuring the safety and longevity of bridge decks demands effective inspection techniques, yet traditional methods often rely on manual analysis and time-intensive procedures. This paper introduces a streamlined, AI-driven approach that combines vehicle-mounted infrared (IR) imaging, a Transformer-based detection model, and ultrasonic tomography (UT) to identify and assess deck anomalies. First, processed IR scans are used to generate labels for the raw IR data, enabling the AI model to detect suspicious regions directly from minimally processed imagery. Potential defect areas, including delaminations and cracks, are then verified through UT, providing three-dimensional insights into their depth and extent. By leveraging an AI model that demonstrates a 70% accuracy when compared to processed IR ground truth, this integrated workflow reduces the need for extensive manual preprocessing, accelerates inspection, and delivers precise global localization of defects. Overall, the proposed methodology offers a scalable solution that improves both inspection reliability and maintenance planning for critical bridge infrastructure.
Alqurashi et al. (Thu,) studied this question.