Next Generation Community-Level Resilience Modeling: The Interdependent Networked Community Resilience Modeling Environment (IN-CORE)
Item Type:Conference Paper
Citation:John van de Lindt, Jamie Kruse, Dan Cox, Paolo Gardoni, Jong Lee, Jamie Padgett, Therese McAllister, Andre Barbosa, Harvey Cutler, Shannon Van Zandt, Next Generation Community-Level Resilience Modeling: The Interdependent Networked Community Resilience Modeling Environment (IN-CORE), 14th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP14), Dublin, Ireland, 2023.
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In 2015, the U.S National Institute of Standards and Technology (NIST) funded the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a 14 university-based consortium of almost 100 people, 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 researchers to set up complex interdependent models of an entire community consisting of buildings, transportation networks, water and electric power networks, and to include social science data-driven household and business models and computable general equilibrium (CGE) models that predict the level and distributional economic effects of a natural hazard on the community economy. In this paper, an overview of both the IN-CORE technology and the scientific implementation is shown for several of the CoEﾒs testbeds with a focus on four key community stability areas (CSA) that encompass an array of community resilience metrics (CRM). Each testbed within IN-CORE has been developed by a team of engineers, planners, and economists and begins with the initial community description, i.e., buildings and other physical and non-physical models as described above, and progresses to the hazard strike, i.e., a tornado, tsunami, hurricane, or earthquake. This process is accomplished through chaining of algorithms, making the technology modular in nature, which is also explained. Following the initial hazard-induced damage is determined this sets the initial conditions for the recovery models, which are, in a way, the least studied area of community resilience, but arguably one of the most important. Two illustrative examples of community testbeds within the center that look at some combination of population, economics, physical services, and social services are presented.
Other Titles:14th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP14)
Type of material:Conference Paper
Series/Report no:14th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP14)
Availability:Full text available