Global ETD Search
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Showing 1 to 5 of 5 for “"Crack Segmentation"”.
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LLM-as-Judge for Reliable Crack Segmentation in Edge-Based Structural Inspection
<p>Crack detection and segmentation are fundamental tasks in structural health monitoring, enabling early identification of damage in critical infrastructure such as roads, bridges, and buildings. While deep learning models—particularly convolutional encoder--decoder architectures—have achieved …
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Crack identification through computer vision: from non-learning-based to learning-based methodologies, and from patch-level to pixel-level detections
… one of the most common types of defects is cracking, which evolves rapidly under the impacts of heavy traffic, aging of materials, and drastic environmental changes. In recent decades, image-based automated crack detection methodologies have been developed and extensively applied by …
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Multi-Class 3D Segmentation of Progressive Damage in Advanced Composites using Deep Learning
… three categories, each of them manifesting as a crack of some sort: (i) delamination (interlaminar cracking), (ii) matrix cracking, and (iii) fiber breakage (crack through fiber). At present, X-ray computed tomography (CT) performed to observe these complex damage mechanisms in 3D and 4D (3D …
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Computer Vision Applications in Structural Engineering
… on tall industrial chimneys. 4. RTK-UAV based 3D Crack Identification, Visualisation and Quantification for Structural Health Monitoring: This study proposed enhancements for 3D Crack Identification, Visualisation, and Quantification in Structural Health Monitoring using a Real-Time-Kinematic …
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3D damage mapping and segmentation using neural radiance fields and advanced deep learning techniques
… Time Agumentation (TTA) significantly enhances crack detection capabilities. This approach allows for precise mapping of segmented cracks onto a 3D model of a bridge, offering a detailed and quantifiable assessment of structural damage. By combining these innovative technologies, the research …