Application of Multidisciplinary Community Resilience Modeling to Reduce Disaster Risk: Building Back Better
Publication: Journal of Performance of Constructed Facilities
Volume 38, Issue 3
Abstract
From December 10 to December 11, 2021, a deadly tornado outbreak struck across several states in the US, including Arkansas, Illinois, Kentucky, and Tennessee. This tornado outbreak resulted in at least $3.9 billion in damage, more than 90 fatalities, and hundreds of injuries. Mayfield, Kentucky, a small city in the eastern United States, was hit by a long-track tornado rated as an Enhanced Fujita 4 (EF4) scale and was one of the communities most heavily damaged during the tornado outbreak. Following the 2021 tornado event, an analysis was performed in the Interdependent Networked Community Resilience Modeling Environment (IN-CORE) for the City of Mayfield to investigate a design code change for residential structures and its effect on communitywide metrics related to functionality and dislocation. Specifically, the IN-CORE modeling environment was used to hindcast the community-level building damage and forecast the community-level building recovery in Mayfield for residential buildings. This required the development of a Mayfield test bed for IN-CORE with a focus on buildings. The generalization of multidisciplinary community resilience modeling from a test bed community to a real community impacted by a recent major tornado event is intended to benchmark that IN-CORE has a strong potential and capability to forecast/hindcast community resilience and provide what-if scenarios for decision makers, city planners, and stakeholders in communities with similar sizes.
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Data Availability Statement
Some data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The Center for Risk-Based Community Resilience Planning is a NIST-funded Center of Excellence; the Center is funded through a cooperative agreement between the US National Institute of Standards and Technology and Colorado State University (NIST Financial Assistance Award Nos. 70NANB15H044 and 70NANB20H008). The authors also thank Mr. Jarrod Loerzel of NIST for his data and GIS expertise supporting this study. The views expressed are those of the presenter and may not represent the official position of the National Institute of Standards and Technology or the US Department of Commerce.
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© 2024 American Society of Civil Engineers.
History
Received: Aug 21, 2023
Accepted: Dec 19, 2023
Published online: Apr 4, 2024
Published in print: Jun 1, 2024
Discussion open until: Sep 4, 2024
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