TIGPR: A multi-view ground penetrating radar detection data for damage assessment of transportation infrastructure(Mendeley Data).
The TIGPR dataset is a high-quality collection of ground-penetrating radar (GPR) images designed for the detection and assessment of transportation infrastructure damage, including roads, bridges, tunnels, and airports. It captures various structural damages, such as cracks, interlayer debonding, lo...
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Elsevier
2025-06-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925003956 |
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| author | Zhen Liu Bingyan Cui Xingyu Gu |
| author_facet | Zhen Liu Bingyan Cui Xingyu Gu |
| author_sort | Zhen Liu |
| collection | DOAJ |
| description | The TIGPR dataset is a high-quality collection of ground-penetrating radar (GPR) images designed for the detection and assessment of transportation infrastructure damage, including roads, bridges, tunnels, and airports. It captures various structural damages, such as cracks, interlayer debonding, looseness, and voids, providing valuable data for infrastructure condition monitoring. The dataset was collected from laboratory or field investigations conducted in Guizhou, Jinhua, and Nanjing, covering a diverse range of highways, municipal roads, and bridge structures under different environmental conditions. Data acquisition was performed using 2D and 3D GPR systems, including IDS-FastWave, MALA GX750, and GeoScope 3D-Radar. The 2D GPR systems generated B-scan images with a resolution of 200 × 200 pixels, while the 3D GPR system provided both B-scan and C-scan images at 320 × 320 pixels. Each image corresponds to a real-world coverage area of 10 m in length and 1m in depth, enabling precise damage localization and quantification. The dataset is structured to facilitate deep learning applications in damage classification, object detection, and semantic segmentation, offering a benchmark for non-destructive testing (NDT) and automated infrastructure assessment. By providing a comprehensive and diverse dataset, TIGPR contributes to advancing intelligent damage detection and supporting the development of machine learning models for transportation infrastructure monitoring. |
| format | Article |
| id | doaj-art-e205ed000e284dc9baf10928e40e88bb |
| institution | OA Journals |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-e205ed000e284dc9baf10928e40e88bb2025-08-20T02:05:12ZengElsevierData in Brief2352-34092025-06-016011166510.1016/j.dib.2025.111665TIGPR: A multi-view ground penetrating radar detection data for damage assessment of transportation infrastructure(Mendeley Data).Zhen Liu0Bingyan Cui1Xingyu Gu2Department of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, ChinaDepartment of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USADepartment of Roadway Engineering, School of Transportation, Southeast University, Nanjing 211189, China; Corresponding author.The TIGPR dataset is a high-quality collection of ground-penetrating radar (GPR) images designed for the detection and assessment of transportation infrastructure damage, including roads, bridges, tunnels, and airports. It captures various structural damages, such as cracks, interlayer debonding, looseness, and voids, providing valuable data for infrastructure condition monitoring. The dataset was collected from laboratory or field investigations conducted in Guizhou, Jinhua, and Nanjing, covering a diverse range of highways, municipal roads, and bridge structures under different environmental conditions. Data acquisition was performed using 2D and 3D GPR systems, including IDS-FastWave, MALA GX750, and GeoScope 3D-Radar. The 2D GPR systems generated B-scan images with a resolution of 200 × 200 pixels, while the 3D GPR system provided both B-scan and C-scan images at 320 × 320 pixels. Each image corresponds to a real-world coverage area of 10 m in length and 1m in depth, enabling precise damage localization and quantification. The dataset is structured to facilitate deep learning applications in damage classification, object detection, and semantic segmentation, offering a benchmark for non-destructive testing (NDT) and automated infrastructure assessment. By providing a comprehensive and diverse dataset, TIGPR contributes to advancing intelligent damage detection and supporting the development of machine learning models for transportation infrastructure monitoring.http://www.sciencedirect.com/science/article/pii/S2352340925003956Transportation infrastructureGround-penetrating radarDamage detectionNon-destructive detectionDeep learning |
| spellingShingle | Zhen Liu Bingyan Cui Xingyu Gu TIGPR: A multi-view ground penetrating radar detection data for damage assessment of transportation infrastructure(Mendeley Data). Data in Brief Transportation infrastructure Ground-penetrating radar Damage detection Non-destructive detection Deep learning |
| title | TIGPR: A multi-view ground penetrating radar detection data for damage assessment of transportation infrastructure(Mendeley Data). |
| title_full | TIGPR: A multi-view ground penetrating radar detection data for damage assessment of transportation infrastructure(Mendeley Data). |
| title_fullStr | TIGPR: A multi-view ground penetrating radar detection data for damage assessment of transportation infrastructure(Mendeley Data). |
| title_full_unstemmed | TIGPR: A multi-view ground penetrating radar detection data for damage assessment of transportation infrastructure(Mendeley Data). |
| title_short | TIGPR: A multi-view ground penetrating radar detection data for damage assessment of transportation infrastructure(Mendeley Data). |
| title_sort | tigpr a multi view ground penetrating radar detection data for damage assessment of transportation infrastructure mendeley data |
| topic | Transportation infrastructure Ground-penetrating radar Damage detection Non-destructive detection Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925003956 |
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