INVESTIGATING NOVEL ANALYSIS APPROACHES FOR STRUCTURAL CONDITION ASSESSMENT USING ULTRASOUND AND INFRARED DATA

Abstract

Aging civil infrastructure, particularly reinforced concrete bridges, is experiencing progressive deterioration that threatens safety, serviceability, and long-term performance. Traditional inspection methods such as visual examination and hammer sounding are limited in their ability to detect subsurface defects and are prone to subjectivity. This dissertation develops and validates an integrated, multi-modal structural condition assessment framework that combines rapid Infrared Thermography (IRT), high-resolution Ultrasound Tomography (UT), Artificial Intelligence (AI)-driven anomaly detection, and immersive Digital Twin (DT) visualization to overcome these limitations. The research advances three main areas: (1) a dual-mode IR–UT workflow exploiting the complementary strengths of each modality, enabling rapid surface screening with IRT and in-depth defect characterization with UT; (2) optimized deep learning (DL) models tailored to each modality, with a transformer-based Grounding DINO model applied to raw Infrared (IR) imagery for automated detection of thermal anomalies, and a lightweight You Only Look Once (YOLO)-v8n model applied to UT volumetric slices for detecting internal delaminations, voids, ducts, and rebar, both trained on large, segmentation-assisted, color-standardized datasets to ensure robust performance under diverse field conditions; and (3) integration of Unmanned Aerial Vehicle (UAV)-based Light Detection and Ranging (LiDAR), photogrammetry, and multi-modal non-destructive testing (NDT) data into a geo-referenced Virtual Reality (VR) environment to support real-time, collaborative decision-making. Laboratory testing on engineered specimens with embedded defects and field deployment on multiple in-service bridges, including the NASA Causeway Bridge, achieved high detection accuracy (mAP@0.5 up to 0.93 for UT using YOLOv8n and 0.80 for IRT using Grounding DINO), strong localization (Average IoU ≈ 0.80–0.90), and significant efficiency gains through targeted UT scanning. The VR-based DT enabled inspectors to seamlessly review thermal anomalies, volumetric UT slices, and 3D geometry in a single immersive scene, reducing defect confirmation time from several minutes to approximately one minute per location. By fusing complementary NDT modalities with AI models purpose-built for each data type and immersive visualization, this research delivers a scalable, repeatable, and field-validated methodology for rapid, objective, and data-rich condition assessment of reinforced concrete structures, with potential for broader application to other infrastructure types to enable proactive maintenance strategies and improved lifecycle management.