Automated Indoor Image Localization to Support a Post-Event Building Assessment.

Image data remains an important tool for post-event building assessment and documentation. After each natural hazard event, significant efforts are made by teams of engineers to visit the affected regions and collect useful image data. In general, a global positioning system (GPS) can provide useful spatial information for localizing image data. However, it is challenging to collect such information when images are captured in places where GPS signals are weak or interrupted, such as the indoor spaces of buildings. The inability to document the images' locations hinders the analysis, organization, and documentation of these images as they lack sufficient spatial context. In this work, we develop a methodology to localize images and link them to locations on a structural drawing. A stream of images can readily be gathered along the path taken through a building using a compact camera. These images may be used to compute a relative location of each image in a 3D point cloud model, which is reconstructed using a visual odometry algorithm. The images may also be used to create local 3D textured models for building-components-of-interest using a structure-from-motion algorithm. A parallel set of images that are collected for building assessment is linked to the image stream using time information. By projecting the point cloud model to the structural drawing, the images can be overlaid onto the drawing, providing clear context information necessary to make use of those images. Additionally, components- or damage-of-interest captured in these images can be reconstructed in 3D, enabling detailed assessments having sufficient geospatial context. The technique is demonstrated by emulating post-event building assessment and data collection in a real building.

Computer-Aided Approach for Rapid Post-Event Visual Evaluation of a Building Façade.

After a disaster strikes an urban area, damage to the façades of a building may produce dangerous falling hazards that jeopardize pedestrians and vehicles. Thus, building façades must be rapidly inspected to prevent potential loss of life and property damage. Harnessing the capacity to use new vision sensors and associated sensing platforms, such as unmanned aerial vehicles (UAVs) would expedite this process and alleviate spatial and temporal limitations typically associated with human-based inspection in high-rise buildings. In this paper, we have developed an approach to perform rapid and accurate visual inspection of building façades using images collected from UAVs. An orthophoto corresponding to any reasonably flat region on the building (e.g., a façade or building side) is automatically constructed using a structure-from-motion (SfM) technique, followed by image stitching and blending. Based on the geometric relationship between the collected images and the constructed orthophoto, high-resolution region-of-interest are automatically extracted from the collected images, enabling efficient visual inspection. We successfully demonstrate the capabilities of the technique using an abandoned building of which a façade has damaged building components (e.g., window panes or external drainage pipes).

Reference-free damage detection, localization, and quantification in composites.

In this study, a reference-free damage characterization technique is developed not only to identify but also to locate and quantify damage in composite structures subject to varying temperature conditions. First, damage is characterized in terms of a damage index (m-value) defined as the ratio of damage size to the wavelength of the A0 mode within the damage. Then, a feasible solution space defining all possible combinations of the damage location and size are estimated without using any prior baseline data obtained from the pristine condition of a structure or different paths. When additional information such as the A0 mode group velocity within the pristine region of the structure becomes available, the estimates for the damage location and size are updated with better accuracy. The uniqueness of this study lies in that damage localization and quantification as well as identification are all performed without comparing current Lamb wave signals with the ones obtained from the pristine condition of the target structure, making the proposed technique more attractive for online monitoring. Numerical and experimental tests are presented to demonstrate the effectiveness of the proposed damage detection technique under varying temperature.