Integrated AI-driven virtual reality framework for bridge inspection with multimodal NDT and remote sensing

Abstract

This study presents an integrated framework for bridge inspection that combines multiple non-destructive testing (NDT) technologies with artificial intelligence (AI) and immersive visualization. The proposed system integrates, and fuses unmanned aerial vehicles (UAV)-based LiDAR point clouds, photogrammetry, infrared thermography (IRT), and phased-array ultrasonic tomography (UT) to generate a comprehensive, bridge-scale 3D inspection model with specific application to bridge decks. A fine-tuned Grounding DINO object detection model, trained on 10,500 infrared images, is used to automatically identify suspicious thermal patterns. The AI achieved 90% precision, 90% recall, an F1 score of 0.90, and a mean average precision (mAP@0.5) of 0.80 on held-out test data. These detections are exported as geo-referenced waypoints to guide targeted UT scans, which confirm and characterize subsurface defects such as delamination and voids. All sensing outputs are aligned within a unified coordinate system and visualized inside a virtual reality (VR) environment. Users can interact with 3D geometry, thermal overlays, and depth-resolved UT slices, and annotate defects in context. By replacing manual IRT interpretation and full-grid UT scanning with AI-guided anomaly detection and selective validation, the proposed workflow has the potential to reduce inspection time, lowers labor costs, and minimizes subjectivity in data interpretation. This system also provides a centralized, interactive 3D record that supports efficient decision-making and long-term maintenance planning.