Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling
Bridge slabs are critical structural components that directly sustain vehicle loads and generally have the shortest service life among bridge elements, leading to increased maintenance needs and costs. In many countries, damage and repair histories have been systematically recorded for over four dec...
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MDPI AG
2025-06-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/12/1979 |
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| author | Hyunhye Song Kiyeol Kim Jihun Shin Gitae Roh Changsu Shim |
| author_facet | Hyunhye Song Kiyeol Kim Jihun Shin Gitae Roh Changsu Shim |
| author_sort | Hyunhye Song |
| collection | DOAJ |
| description | Bridge slabs are critical structural components that directly sustain vehicle loads and generally have the shortest service life among bridge elements, leading to increased maintenance needs and costs. In many countries, damage and repair histories have been systematically recorded for over four decades. In this study, a digital twin framework for predicting the future performance of bridge slabs by integrating long-term inspection data was proposed. Historical 2D damage drawings were digitized using the YOLOv7 deep-learning model to extract the spatial coordinates of the damaged locations. Based on this data, eight representative damage states were defined to support the prediction of the service life. The damage and repair history was embedded into the 3D bridge models using a unique coding system to enable temporal and spatial tracking. As the corrosion of the reinforcement cannot be directly observed by visual inspection, its development and progression is estimated using empirical models. The digital twin concept is validated using historical inspection records to demonstrate its applicability to existing bridge slabs. The integration of cumulative deterioration data significantly improves the accuracy of the performance predictions and facilitates data-driven maintenance and rehabilitation strategies. |
| format | Article |
| id | doaj-art-3d564bdaf35946a8aa2499d36d935219 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-3d564bdaf35946a8aa2499d36d9352192025-08-20T03:26:51ZengMDPI AGBuildings2075-53092025-06-011512197910.3390/buildings15121979Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance ModelingHyunhye Song0Kiyeol Kim1Jihun Shin2Gitae Roh3Changsu Shim4Department of Civil and Environmental Engineering, Chung-Ang University, Seoul 06974, Republic of KoreaDepartment of Civil and Environmental Engineering, Chung-Ang University, Seoul 06974, Republic of KoreaDepartment of Civil and Environmental Engineering, Chung-Ang University, Seoul 06974, Republic of KoreaDepartment of Civil and Environmental Engineering, Chung-Ang University, Seoul 06974, Republic of KoreaDepartment of Civil and Environmental Engineering, Chung-Ang University, Seoul 06974, Republic of KoreaBridge slabs are critical structural components that directly sustain vehicle loads and generally have the shortest service life among bridge elements, leading to increased maintenance needs and costs. In many countries, damage and repair histories have been systematically recorded for over four decades. In this study, a digital twin framework for predicting the future performance of bridge slabs by integrating long-term inspection data was proposed. Historical 2D damage drawings were digitized using the YOLOv7 deep-learning model to extract the spatial coordinates of the damaged locations. Based on this data, eight representative damage states were defined to support the prediction of the service life. The damage and repair history was embedded into the 3D bridge models using a unique coding system to enable temporal and spatial tracking. As the corrosion of the reinforcement cannot be directly observed by visual inspection, its development and progression is estimated using empirical models. The digital twin concept is validated using historical inspection records to demonstrate its applicability to existing bridge slabs. The integration of cumulative deterioration data significantly improves the accuracy of the performance predictions and facilitates data-driven maintenance and rehabilitation strategies.https://www.mdpi.com/2075-5309/15/12/1979bridge slabservice lifedigital twindamagerepairperformance prediction |
| spellingShingle | Hyunhye Song Kiyeol Kim Jihun Shin Gitae Roh Changsu Shim Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling Buildings bridge slab service life digital twin damage repair performance prediction |
| title | Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling |
| title_full | Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling |
| title_fullStr | Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling |
| title_full_unstemmed | Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling |
| title_short | Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling |
| title_sort | digital twin framework for bridge slab deterioration from 2d inspection data to predictive 3d maintenance modeling |
| topic | bridge slab service life digital twin damage repair performance prediction |
| url | https://www.mdpi.com/2075-5309/15/12/1979 |
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