The interdependent networked community resilience modeling environment (IN-CORE)

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dc.citation.firstpage57en_US
dc.citation.issueNumber2en_US
dc.citation.journalTitleResilient Cities and Structuresen_US
dc.citation.lastpage66en_US
dc.citation.volumeNumber2en_US
dc.contributor.authorvan de Lindt, John W.en_US
dc.contributor.authorKruse, Jamieen_US
dc.contributor.authorCox, Daniel T.en_US
dc.contributor.authorGardoni, Paoloen_US
dc.contributor.authorLee, Jong Sungen_US
dc.contributor.authorPadgett, Jamieen_US
dc.contributor.authorMcAllister, Therese P.en_US
dc.contributor.authorBarbosa, Andreen_US
dc.contributor.authorCutler, Harveyen_US
dc.contributor.authorVan Zandt, Shannonen_US
dc.contributor.authorRosenheim, Nathanaelen_US
dc.contributor.authorNavarro, Christopher M.en_US
dc.contributor.authorSutley, Elainaen_US
dc.contributor.authorHamideh, Saraen_US
dc.date.accessioned2024-05-08T18:56:09Zen_US
dc.date.available2024-05-08T18:56:09Zen_US
dc.date.issued2023en_US
dc.description.abstractIn 2015, the U.S National Institute of Standards and Technology (NIST) funded the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a fourteen university-based consortium of almost 100 collaborators, including faculty, students, post-doctoral scholars, and NIST researchers. This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE - the Interdisciplinary Networked Community Resilience Modeling Environment (IN-CORE). IN-CORE enables communities, consultants, and researchers to set up complex interdependent models of an entire community consisting of people, businesses, social institutions, buildings, transportation networks, water networks, and electric power networks and to predict their performance and recovery to hazard scenario events, including uncertainty propagation through the chained models. The modeling environment includes a detailed building inventory, hazard scenario models, building and infrastructure damage (fragility) and recovery functions, social science data-driven household and business models, and computable general equilibrium (CGE) models of local economies. An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform. Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population, economics, physical services, and social services. An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas (CSA) that encompass an array of community resilience metrics (CRM) and support community resilience informed decision-making. Each testbed within IN-CORE has been developed by a team of engineers, social scientists, urban planners, and economists. Community models, begin with a community description, i.e., people, businesses, buildings, infrastructure, and progresses to the damage and loss of functions caused by a hazard scenario, i.e., a flood, tornado, hurricane, or earthquake. This process is accomplished through chaining of modular algorithms, as described. The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models, which have been the least studied area of community resilience but arguably one of the most important. Communities can then test the effect of mitigation and/or policies and compare the effects of “what if” scenarios on physical, social, and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.en_US
dc.identifier.citationvan de Lindt, J. W., Kruse, J., Cox, D. T., Gardoni, P., Lee, J. S., Padgett, J., McAllister, T. P., Barbosa, A., Cutler, H., Van Zandt, S., Rosenheim, N., Navarro, C. M., Sutley, E., & Hamideh, S. (2023). The interdependent networked community resilience modeling environment (IN-CORE). Resilient Cities and Structures, 2(2), 57–66. https://doi.org/10.1016/j.rcns.2023.07.004en_US
dc.identifier.digital1-s20-S277274162300039X-mainen_US
dc.identifier.doihttps://doi.org/10.1016/j.rcns.2023.07.004en_US
dc.identifier.urihttps://hdl.handle.net/1911/115661en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titleThe interdependent networked community resilience modeling environment (IN-CORE)en_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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