Application of Multidisciplinary Community Resilience Modeling to Reduce Disaster Risk: Building Back BetterSource: Journal of Performance of Constructed Facilities:;2024:;Volume ( 038 ):;issue: 003::page 04024012-1Author:Wanting “Lisa” Wang
,
John W. van de Lindt
,
Blythe Johnston
,
P. Shane Crawford
,
Guirong Yan
,
Thang Dao
,
Trung Do
,
Katie Skakel
,
Mojtaba Harati
,
Tu Nguyen
,
Robinson Umeike
,
Silvana Croope
,
Andre R. Barbosa
DOI: 10.1061/JPCFEV.CFENG-4650Publisher: ASCE
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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contributor author | Wanting “Lisa” Wang | |
contributor author | John W. van de Lindt | |
contributor author | Blythe Johnston | |
contributor author | P. Shane Crawford | |
contributor author | Guirong Yan | |
contributor author | Thang Dao | |
contributor author | Trung Do | |
contributor author | Katie Skakel | |
contributor author | Mojtaba Harati | |
contributor author | Tu Nguyen | |
contributor author | Robinson Umeike | |
contributor author | Silvana Croope | |
contributor author | Andre R. Barbosa | |
date accessioned | 2024-04-27T22:26:15Z | |
date available | 2024-04-27T22:26:15Z | |
date issued | 2024/06/01 | |
identifier other | 10.1061-JPCFEV.CFENG-4650.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4296651 | |
description 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. | |
publisher | ASCE | |
title | Application of Multidisciplinary Community Resilience Modeling to Reduce Disaster Risk: Building Back Better | |
type | Journal Article | |
journal volume | 38 | |
journal issue | 3 | |
journal title | Journal of Performance of Constructed Facilities | |
identifier doi | 10.1061/JPCFEV.CFENG-4650 | |
journal fristpage | 04024012-1 | |
journal lastpage | 04024012-12 | |
page | 12 | |
tree | Journal of Performance of Constructed Facilities:;2024:;Volume ( 038 ):;issue: 003 | |
contenttype | Fulltext |