Imagen de Portada

Time-Series 3D Modeling of Tunnel Damage Through Fusion of Image and Point Cloud Data

Guardado en:
Publicado en:Remote Sensing vol. 17, no. 18 (2025), p. 3173-3211
Autor principal: Lee, Chulhee
Otros Autores: Kim Donggyou, Kim Dongku, Kang Joonoh
Publicado:
MDPI AG
Materias:
Accuracy
Defects
Concrete
Lidar
Data processing
Damage detection
Unmanned aerial vehicles
Measurement techniques
Data integration
Registration
Density
Leakage
Efflorescence
Visualization
Preventive maintenance
Geometric accuracy
Inspections
Artificial intelligence
Digital transformation
Lasers
Freeze-thawing
Root-mean-square errors
Sensors
Time series
Three dimensional models
Digital twins
Freeze thaw cycles
Image acquisition
Spalling
Acceso en línea:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen:Precise maintenance is vital for ensuring the safety of tunnel structures; however, traditional visual inspections are subjective and hazardous. Digital technologies such as LiDAR and imaging offer promising alternatives, but each has complementary limitations in geometric precision and visual representation. This study addresses these limitations by developing a three-dimensional modeling framework that integrates image and point cloud data and evaluates its effectiveness. Terrestrial LiDAR and UAV images were acquired three times over a freeze–thaw cycle at an aging, abandoned tunnel. Based on the data obtained, three types of 3D models were constructed: TLS-based, image-based, and fusion-based. A comparative evaluation results showed that the TLS-based model had excellent geometric accuracy but low resolution due to low point density. The image-based model had high density and excellent resolution but low geometric accuracy. In contrast, the fusion-based model achieved the lowest root mean squared error (RMSE), the highest geometric accuracy, and the highest resolution. Time-series analysis further demonstrated that only the fusion-based model could identify the complex damage progression mechanism in which leakage and icicle formation (visual changes) increased the damaged area by 55.8% (as measured by geometric changes). This also enabled quantitative distinction between active damage (leakage, structural damage) and stable-state damage (spalling, efflorescence, cracks). In conclusion, this study empirically demonstrates the necessity of data fusion for comprehensive tunnel condition diagnosis. It provides a benchmark for evaluating 3D modeling techniques in real-world environments and lays the foundation for digital twin development in data-driven preventive maintenance.
ISSN:2072-4292
DOI:10.3390/rs17183173
Fuente:Advanced Technologies & Aerospace Database