A hybrid model for post-earthquake performance assessments in challenging contexts

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.