Abstract
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.
Highlights
Publisher’s Note: MDPI stays neutralIn the aftermath of large earthquakes, buildings in the affected region must be evaluated for structural integrity and other life safety hazards before occupants can safely return to their homes and places of work
The Building Information Model (BIM) is used to sort through the unmanned aerial vehicle (UAV) images and select an optimal set of images containing Turner Hall’s damage-sensitive components
The UAV images are input to GrabCut for component identification
Summary
Publisher’s Note: MDPI stays neutralIn the aftermath of large earthquakes, buildings in the affected region must be evaluated for structural integrity and other life safety hazards before occupants can safely return to their homes and places of work. Earthquake-affected buildings are assigned placards classifying them into three categories: (1) inspected (green), no apparent hazards or loss of load carrying capacity; (2) restricted use (yellow), building specific restrictions are indicated on the placard and are to be enforced by the owner; and (3) unsafe (red), extreme life safety hazard or imminent collapse danger, no entry permitted. This effort requires a team of experienced inspectors, comprised of structural engineers and building officials, to classify the safety of every building in the affected region. A team of inspectors classifies the building based on obvious signs such as partial collapse, with regard to jurisdictional claims in published maps and institutional affiliations
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