Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework
Computer vision has shown potential for assisting post-earthquake inspection of buildings through automatic damage detection in images. However, assessing the safety of an earthquake-damaged building requires considering this damage in the context of its global impact on the structural system. Thus,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/3/873 |
| _version_ | 1797484836065116160 |
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| author | Nathaniel M. Levine Billie F. Spencer |
| author_facet | Nathaniel M. Levine Billie F. Spencer |
| author_sort | Nathaniel M. Levine |
| collection | DOAJ |
| description | Computer vision has shown potential for assisting post-earthquake inspection of buildings through automatic damage detection in images. However, assessing the safety of an earthquake-damaged building requires considering this damage in the context of its global impact on the structural system. Thus, an inspection must consider the expected damage progression of the associated component and the component’s contribution to structural system performance. To address this issue, a digital twin framework is proposed for post-earthquake building evaluation that integrates unmanned aerial vehicle (UAV) imagery, component identification, and damage evaluation using a Building Information Model (BIM) as a reference platform. The BIM guides selection of optimal sets of images for each building component. Then, if damage is identified, each image pixel is assigned to a specific BIM component, using a GrabCut-based segmentation method. In addition, 3D point cloud change detection is employed to identify nonstructural damage and associate that damage with specific BIM components. Two example applications are presented. The first develops a digital twin for an existing reinforced concrete moment frame building and demonstrates BIM-guided image selection and component identification. The second uses a synthetic graphics environment to demonstrate 3D point cloud change detection for identifying damaged nonstructural masonry walls. In both examples, observed damage is tied to BIM components, enabling damage to be considered in the context of each component’s known design and expected earthquake performance. The goal of this framework is to combine component-wise damage estimates with a pre-earthquake structural analysis of the building to predict a building’s post-earthquake safety based on an external UAV survey. |
| first_indexed | 2024-03-09T23:10:12Z |
| format | Article |
| id | doaj.art-d3ac1729d7c14212bde4ba067b797958 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:10:12Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d3ac1729d7c14212bde4ba067b7979582023-11-23T17:46:48ZengMDPI AGSensors1424-82202022-01-0122387310.3390/s22030873Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin FrameworkNathaniel M. Levine0Billie F. Spencer1Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USADepartment of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USAComputer vision has shown potential for assisting post-earthquake inspection of buildings through automatic damage detection in images. However, assessing the safety of an earthquake-damaged building requires considering this damage in the context of its global impact on the structural system. Thus, an inspection must consider the expected damage progression of the associated component and the component’s contribution to structural system performance. To address this issue, a digital twin framework is proposed for post-earthquake building evaluation that integrates unmanned aerial vehicle (UAV) imagery, component identification, and damage evaluation using a Building Information Model (BIM) as a reference platform. The BIM guides selection of optimal sets of images for each building component. Then, if damage is identified, each image pixel is assigned to a specific BIM component, using a GrabCut-based segmentation method. In addition, 3D point cloud change detection is employed to identify nonstructural damage and associate that damage with specific BIM components. Two example applications are presented. The first develops a digital twin for an existing reinforced concrete moment frame building and demonstrates BIM-guided image selection and component identification. The second uses a synthetic graphics environment to demonstrate 3D point cloud change detection for identifying damaged nonstructural masonry walls. In both examples, observed damage is tied to BIM components, enabling damage to be considered in the context of each component’s known design and expected earthquake performance. The goal of this framework is to combine component-wise damage estimates with a pre-earthquake structural analysis of the building to predict a building’s post-earthquake safety based on an external UAV survey.https://www.mdpi.com/1424-8220/22/3/873unmanned aerial vehiclesbuilding information modelingdigital twincomputer visionpost-earthquake evaluationautomated inspection |
| spellingShingle | Nathaniel M. Levine Billie F. Spencer Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework Sensors unmanned aerial vehicles building information modeling digital twin computer vision post-earthquake evaluation automated inspection |
| title | Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework |
| title_full | Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework |
| title_fullStr | Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework |
| title_full_unstemmed | Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework |
| title_short | Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework |
| title_sort | post earthquake building evaluation using uavs a bim based digital twin framework |
| topic | unmanned aerial vehicles building information modeling digital twin computer vision post-earthquake evaluation automated inspection |
| url | https://www.mdpi.com/1424-8220/22/3/873 |
| work_keys_str_mv | AT nathanielmlevine postearthquakebuildingevaluationusinguavsabimbaseddigitaltwinframework AT billiefspencer postearthquakebuildingevaluationusinguavsabimbaseddigitaltwinframework |