Mapping damages from inspection images to 3D digital twins of large‐scale structures
Abstract This study develops a methodology to create detailed visual Digital Twins of large‐scale structures with their realistic damages detected from visual inspection or nondestructive testing. The methodology is demonstrated with a transition piece of an offshore wind turbine and a composite rot...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.12837 |
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| author | Hans‐Henrik von Benzon Xiao Chen |
| author_facet | Hans‐Henrik von Benzon Xiao Chen |
| author_sort | Hans‐Henrik von Benzon |
| collection | DOAJ |
| description | Abstract This study develops a methodology to create detailed visual Digital Twins of large‐scale structures with their realistic damages detected from visual inspection or nondestructive testing. The methodology is demonstrated with a transition piece of an offshore wind turbine and a composite rotor blade, with surface paint damage and subsurface delamination damage, respectively. Artificial Intelligence and color threshold segmentation are used to classify and localize damages from optical images taken by drones. These damages are digitalized and mapped to a 3D geometry reconstruction of the large‐scale structure or a CAD model of the structure. To map the images from 2D to 3D, metadata information is combined with the geo placement of the large‐scale structure's 3D model. The 3D model can here both be a CAD model of the structure or a 3D reconstruction based on photogrammetry. After mapping the damage, the Digital Twin gives an accurate representation of the structure. The location, shape, and size of the damage are visible on the Digital Twin. The demonstrated methodology can be applied to industrial sectors such as wind energy, the oil and gas industry, marine and aerospace to facilitate asset management. |
| format | Article |
| id | doaj-art-dff750718cd942a88a1115bace2ee03f |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-dff750718cd942a88a1115bace2ee03f2025-01-31T00:22:48ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.12837Mapping damages from inspection images to 3D digital twins of large‐scale structuresHans‐Henrik von Benzon0Xiao Chen1Department of Wind and Energy Systems Technical University of Denmark Roskilde DenmarkDepartment of Wind and Energy Systems Technical University of Denmark Roskilde DenmarkAbstract This study develops a methodology to create detailed visual Digital Twins of large‐scale structures with their realistic damages detected from visual inspection or nondestructive testing. The methodology is demonstrated with a transition piece of an offshore wind turbine and a composite rotor blade, with surface paint damage and subsurface delamination damage, respectively. Artificial Intelligence and color threshold segmentation are used to classify and localize damages from optical images taken by drones. These damages are digitalized and mapped to a 3D geometry reconstruction of the large‐scale structure or a CAD model of the structure. To map the images from 2D to 3D, metadata information is combined with the geo placement of the large‐scale structure's 3D model. The 3D model can here both be a CAD model of the structure or a 3D reconstruction based on photogrammetry. After mapping the damage, the Digital Twin gives an accurate representation of the structure. The location, shape, and size of the damage are visible on the Digital Twin. The demonstrated methodology can be applied to industrial sectors such as wind energy, the oil and gas industry, marine and aerospace to facilitate asset management.https://doi.org/10.1002/eng2.128373D photogrammetryartificial intelligencedamage inspectiondeep learningdigital twinimage segmentation |
| spellingShingle | Hans‐Henrik von Benzon Xiao Chen Mapping damages from inspection images to 3D digital twins of large‐scale structures Engineering Reports 3D photogrammetry artificial intelligence damage inspection deep learning digital twin image segmentation |
| title | Mapping damages from inspection images to 3D digital twins of large‐scale structures |
| title_full | Mapping damages from inspection images to 3D digital twins of large‐scale structures |
| title_fullStr | Mapping damages from inspection images to 3D digital twins of large‐scale structures |
| title_full_unstemmed | Mapping damages from inspection images to 3D digital twins of large‐scale structures |
| title_short | Mapping damages from inspection images to 3D digital twins of large‐scale structures |
| title_sort | mapping damages from inspection images to 3d digital twins of large scale structures |
| topic | 3D photogrammetry artificial intelligence damage inspection deep learning digital twin image segmentation |
| url | https://doi.org/10.1002/eng2.12837 |
| work_keys_str_mv | AT hanshenrikvonbenzon mappingdamagesfrominspectionimagesto3ddigitaltwinsoflargescalestructures AT xiaochen mappingdamagesfrominspectionimagesto3ddigitaltwinsoflargescalestructures |