Using high-resolution, multi-disciplinary simulations to investigate the effect of community and structural vulnerabilities on neighborhood recovery
Abstract
A quantitative approach to evaluate community vulnerability and resilience to earthquakes uses a multi-disciplinary model to consider the effects of social vulnerability of residents and the structural vulnerability of their environment on community recovery time. The use of high-resolution building and population data is applicable at a regional scale. A case study illustrates the approach in the city of Berkeley, CA for a hypothetical Mw7.0 earthquake along the Hayward Fault. For each building, the closest site-specific ground motion acceleration time histories are used in nonlinear dynamic simulations to estimate the peak accelerations and inter-story drift ratios in the structure. Twenty-thousand realizations of structural damage are simulated using the building's accelerations, drift ratios, probability distributions of structural, nonstructural, and repair state variables in fragility models from HAZUS. One hundred potential neighborhood damage outcomes are generated from a probabilistic description of damage for every building. Social vulnerability of households is characterized by Community Vulnerability Indicators (CVIs). Parcel-level socioeconomic information is used to enhance the census-block resolution CVI scores. Neighborhood recovery is characterized by the time it takes to have the majority of buildings functional in the area. Experts have found that if over 20% of buildings are in an extensive or complete damage state, neighborhood blight encourages outmigration and reduces the capacity to recover. Although households with higher income sustain more severe damages close to the Hayward Fault, their recovery is facilitated by their lower social vulnerability. The recovery of other neighborhoods is affected by rebuilding delays and other impeding factors that low-income and more vulnerable households experience. Our results suggest that all neighborhoods in Berkeley could be affected by blight for months to years. This high-resolution approach allows for evaluation of the benefits from mitigation policies that target specific structure types or population groups. The results show the potential in this approach and draw attention to the uncertainty in community recovery predictions in lieu of site-specific, high-resolution structural and socioeconomic data and models.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMNH040..05V
- Keywords:
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- 4319 Spatial modeling;
- NATURAL HAZARDS;
- 4327 Resilience;
- NATURAL HAZARDS;
- 4328 Risk;
- NATURAL HAZARDS;
- 4334 Disaster risk communication;
- NATURAL HAZARDS