Search for: All records

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. 

Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace. 

FAST deployed from 8-12 January 2020, documenting the performance of 61 structures located in six different cities along the southwestern coast of Puerto Rico between the cities of Ponce and Mayagüez. A variety of structure types are surveyed, including residential building structures and bridge infrastructures. FAST collected perishable data through the Fulcrum app by completing damage assessment forms, recording high-resolution overall and detailed images of observed damages, and notes summarizing key observations. Fieldwork data collection is conducted according to the StEER’s FAST handbook.This project encompasses the products of StEER's Level 2 response to the Puerto Rico Earthquake from December 2019 to January 2020. The main event, a Mw 6.4 quake, occurred on January 7th, accompanied by numerous aftershocks. The governor reported one casualty and eight injuries, declaring a State of Emergency. The earthquakes damaged 10,000+ structures, collapsing 80, predominantly residential units. Infrastructure, bridges, and roads were also affected, leaving two-thirds of the island without power. Over 8,000 people were displaced to shelters, while 63,000 received assistance, with FEMA handling over 13,852 aid requests. In response, the StEER conducted a post-earthquake performance assessment from 8-12 January 2020, documenting the performance of 60 structures located in six different cities along the southwestern coast of Puerto Rico. The field data collection focused on acquiring high-resolution photographs and notes on structural performance necessary to construct detailed case studies of each structure. 

Observing damage and documenting successful performance of buildings and other structures. Classes include residential, commercial, and power infrastructure. Methodologies include detailed damage assessments in Fulcrum, deployment of UAS for high-resolution aerial imagery, and deployment of surface-level panoramic imaging devices. Hazard indicators were also captured.In the early morning hours of March 3, 2020, a strong tornado struck the City of Nashville and the surrounding metropolitan region with estimated maximum wind speeds of 165 mph. The tornado passed through Nashville and continued east for 53 miles, impacting the communities of Donelson, Mt. Juliet and Lebanon before lifting. The same storm system then produced a second tornado that struck Cookeville, TN with estimated wind speeds of 175 mph. The Nashville tornado was the third tornado that passed through the Five Points area of Nashville. Damage was reported across a diverse cross-section of buildings spanning a number of communities: Camden, Germantown/North Nashville, East Nashville/Five Points, Donelson, Mt. Juliet, Lebanon and Cookeville. Exposure of an urban metro area to this series of tornadoes resulted in significant impacts to power infrastructure and building performance ranging from loss of roof cover and broken windows to complete destruction. Affected typologies and building classes include single and multi-family wood framed homes, commercial construction (ranging from big box stores down to smaller restaurants/retail shops), airport and industrial buildings, and a number of schools. More gravely, these nocturnal tornadoes claimed two dozen lives and injured hundreds more. Given the loss of life and property in this event and the fact that the Nashville tornado sequence impacted an urban area with diverse building classes and typologies, this event offers an opportunity to advance our knowledge of structural resistance to strong winds, particularly given that new construction was among the inventory significantly damaged. This project encompasses the products of StEER's response to this event: Preliminary Virtual Reconnaissance Report (PVRR), Early Access Reconnaissance Report (EARR) and Curated Dataset. 

Observing damage and documenting successful performance of buildings and other structures. Classes include residential, commercial, and power infrastructure. Methodologies include detailed damage assessments in Fulcrum, deployment of UAS for high-resolution aerial imagery, and deployment of surface-level panoramic imaging devices. Hazard indicators were also captured. 

The response leveraged small, self-contained, regional FASTs deploying in phases to collect rapid assessment data using vehicle-mounted street-level panoramic imaging platforms, with select use of UAS. Routes were selected to ensure longitudinal data capture of areas previously documented for Hurricane Laura, as well as new clusters exposed to some of the Delta’s highest wind speeds to the east of landfall. 

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.