Rapid Seismic Damage Assessment in Densely Built Wooden Residential Areas Using 3D Point Cloud Measurement
Rapid post-earthquake assessments of residential buildings are essential for preventing secondary disasters but typically require substantial human resources, with challenges related to accuracy and inspector safety. In wooden residential buildings, residual deformation can cause significant interna...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/10/1623 |
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| Summary: | Rapid post-earthquake assessments of residential buildings are essential for preventing secondary disasters but typically require substantial human resources, with challenges related to accuracy and inspector safety. In wooden residential buildings, residual deformation can cause significant internal damage despite minor external indications. Thus, accurate evaluation of secondary components such as exterior walls and window frames is crucial. Although recent studies on digital assessment technologies focus mainly on reinforced concrete structures, limited research addresses wooden structures, especially considering residual deformation. This study proposes a rapid emergency risk assessment method utilizing 3D point cloud measurements obtained by a 3D scanning camera for densely built wooden residential areas. Its practicality was verified through three aspects. First, a comparison with conventional methods showed that the measurement accuracy of the proposed method is sufficient for practical use, with errors significantly lower than the inclination thresholds used in emergency risk assessments (e.g., 1/60 rad ≈ 1°). Second, in detection experiments using a deformed window frame model, the average error between the applied inclination and the measured values was less than 3%, demonstrating that deformation, dislodgement, and inclination of secondary components can be reliably detected from point cloud data. Third, field validation conducted in a commercial district confirmed that multiple buildings can be simultaneously measured and that individual buildings and their secondary components can be efficiently extracted and identified. Thus, this method demonstrates practical applicability and significantly improves the speed and efficiency of emergency assessments in densely built wooden residential areas. |
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| ISSN: | 2075-5309 |