Determination of Individual Building Performance Targets to Achieve Community-Level Social and Economic Resilience Metrics

• Published in: Journal of structural engineering (New York, N.Y.) N.Y.), 2022-05, Vol.148 (5)
• Main Authors: Wang, Wanting “Lisa”, van de Lindt, John W, Hartman, Brad, Cutler, Harvey, Kruse, Jamie L, McAllister, Therese P, Hamideh, Sara
• Format: Article
• Language: English
• Subjects: Building codes; Damage; Decision support systems; Economic impact; Economic models; Environment models; Residential buildings; Residential communities; Resilience; Retrofitting; Risk analysis; Structural engineering; Technical Papers; Tornadoes; Wood framed
• ISSN: 0733-9445; 1943-541X
• DOI: 10.1061/(ASCE)ST.1943-541X.0003338

AbstractThe retrofit of wood-frame residential buildings is a relatively effective strategy to mitigate damage caused by windstorms. However, little is known about the effect of modifying building performance for intense events such as a tornado and the subsequent social and economic impacts that result at the community level following an event. This paper presents a method that enables a community to select residential building performance levels representative of either retrofitting or adopting a new design code that computes target community metrics for the effects on the economy and population. Although not a full risk analysis, a series of generic tornado scenarios for different Enhanced Fujita (EF) ratings are simulated, and five resilience metrics are assigned to represent community goals based on economic and population stability. To accomplish this, the functionality of the buildings following the simulated tornado is used as input to a computable general equilibrium (CGE) economics model that predicts household income, employment, and domestic supply at the community level. Population dislocation as a function of building damage and detailed sociodemographic US census-based data is also predicted and serves as a core community resilience metric. Finally, this proposed methodology demonstrates how the metrics can help meet community-level resilience objectives for decision support based on a level of design code improvement or retrofit level. The method is demonstrated for Joplin, Missouri. All analyses and data have been developed and made available on the open-source IN-CORE modeling environment. The proposed multidisciplinary methodology requires continued research to characterize the uncertainty in the decision support results.