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This study describes a hybrid framework for post-hazard building performance assessments. The framework relies upon rapid imaging data collected by regional scout teams being integrated into broader data platforms that are parsed by virtual teams of hazards engineers to efficiently create robust performance assessment datasets. The study also pilots a machine-in-the-loop approach whereby deep learning and computer vision-based models are used to automatically define common building attributes, enabling hazard engineers to focus more of their efforts on precise damage quantification and other more nuanced elements of performance assessments. The framework shows promise, but to achieve optimal accuracy of the automated methods requires regional tuning.
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Hybrid Framework for Post-Hazard Building Performance Assessments with Application to Hurricanes
This study describes a hybrid framework for post-hazard building performance assessments. The framework relies upon rapid imaging data collected by regional scout teams being integrated into broader data platforms that are parsed by virtual teams of hazards engineers to efficiently create robust performance assessment datasets. The study also pilots a machine-in-the-loop approach whereby deep learning and computer vision-based models are used to automatically define common building attributes, enabling hazard engineers to focus more of their efforts on precise damage quantification and other more nuanced elements of performance assessments. The framework shows promise, but to achieve optimal accuracy of the automated methods requires regional tuning.
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- PAR ID:
- 10436529
- Date Published:
- Journal Name:
- Proceedings of the 14th Americas Conference on Wind Engineering
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Disasters provide an invaluable opportunity to evaluate contemporary design standards and construction practices; these evaluations have historically relied upon experts, which inherently limited the speed, scope and coverage of post-disaster reconnaissance. However, hybrid assessments that localize data collection and engage remote expertise offer a promising alternative, particularly in challenging contexts. This paper describes a multi-phase hybrid assessment conducting rapid assessments with wide coverage followed by detailed assessments of specific building subclasses following the 2021 M7.2 earthquake in Haiti, where security issues limited international participation. The rapid assessment classified and assigned global damage ratings to over 12,500 buildings using over 40 non-expert local data collectors to feed imagery to dozens of remote engineers. A detailed assessment protocol then conducted component-level evaluations of over 200 homes employing enhanced vernacular construction, identified via machine learning from nearly 40,000 acquired images. A second mobile application guided local data collectors through a systematic forensic documentation of 30 of these homes, providing remote engineers with essential implementation details. In total, this hybrid assessment underscored that performance in the 2021 earthquake fundamentally depended upon the type and consistency of the bracing scheme. The developed assessment tools and mobile apps have been shared as a demonstration of how a hybrid approach can be used for rapid and detailed assessments following major earthquakes in challenging contexts. More importantly, the open datasets generated continue to inform efforts to promote greater use of enhanced vernacular architecture as a multi-hazard resilient typology that can deliver life-safety in low-income countries.more » « less
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null (Ed.)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.more » « less
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Interest in earthquake resilience has increased in recent years, and the use of building cluster performance objectives has been shown to be an effective method for evaluating the resilience of built environment. A building cluster is a portfolio of buildings that share the same role in a community; its performance objectives are defined by considering earthquake scenarios, hazard levels, and individual building performance. The methodology presented in this paper employs performance-based assessments to estimate the probability of achieving building cluster performance objectives immediately following a seismic event. It can be used to assess the immediate post-earthquake community resilience in five steps: 1) hazard analysis, 2) conditional assessment of individual building performance, 3) conditional assessment of building cluster performance, 4) building cluster performance assessment by aggregation, and 5) earthquake resilience assessment of building clusters considering all hazard levels of interest. The design and extreme hazard levels are formulated using ground motion records selected based on the conditional spectra considering characteristics of earthquake scenarios and spatial correlation. Three performance objectives are defined for both individual buildings and building clusters: functionality, safe and usable during repair, and collapse prevention. Two engineering demand parameters – the maximum transient and the permanent interstory drift indices – are used to estimate individual building performance. The probability of achieving building cluster performance objective is calculated using the total probability theorem. The application of the proposed methodology is demonstrated using two clusters of reinforced concrete buildings, corresponding to ASCE 7 Risk Category II and IV structures, in San Francisco, CA.more » « less
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Since its founding in 2018, the Structural Extreme Events Reconnaissance (StEER) Network has worked to deepen the capacity of the Natural Hazards Engineering (NHE) community for coordinated and standardized assessments of the performance of the built environment following natural hazard events. This paper positions StEER within the field of engineering reconnaissance and the Natural Hazards Engineering Research Infrastructure (NHERI), outlining its organizational model for coordinated community-led responses to wind, seismic, and coastal hazard events. The paper’s examination of StEER’s event response workflow, engaging a range of hardware and delivering a suite of products, demonstrates StEER’s contributions in the areas of: workflow and data standardization, data reliability to enable field-observation-driven research & development, efficiency in data collection and dissemination to speed knowledge sharing, near-real- time open data access for enhanced coordination and transparency, and flexibility in collaboration modes to reduce the “overhead” associated with reconnaissance and foster broad NHE community engagement in event responses as part of field and virtual assessment structural teams (FAST/VAST). StEER’s creation of efficient systems to deliver well-documented, reliable data suitable for diverse re-uses as well as rapidly disseminated synopses of the impact of natural hazard events on the built environment provide a distinctive complement to existing post-event reconnaissance initiatives. The implementation of these policies, protocols and workflows is then demonstrated with case studies from five events illustrating StEER’s different field response strategies: the Nashville, Tennessee Tornadoes (2020) – a Hazard Gradient Survey; the Palu Earthquake and Tsunami in Indonesia (2018) – a Representative Performance Study; the Puerto Rico Earthquakes (2019/2020) – using Targeted Case Studies; Hurricane Laura (2020) – leveraging Rapid Surveys to enable virtual assessments; and Hurricane Dorian (2019) in the Bahamas – a Phased Multi-Hazard Investigation. The use of these strategies has enabled StEER to respond to 36 natural hazard events, involving over 150 different individuals to produce 45 published reports/briefings, over 5000 publicly available app-based structural assessments, and over 1600 km (1000 mi) of street-level panoramic imagery in its first 2years of operation.more » « less
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