A QUANTITATIVE APPROACH TO MODELING AND IMPROVING COMMUNITY RESILIENCE TO NATURAL HAZARDS

Community resilience is the ability of a city or community to plan for, withstand, and recover from a natural hazard such as an earthquake or flood. Advancing community (or urban) resilience requires: (1) an understanding of how the physical infrastructure, economy, and social institutions within a community will perform when disrupted by a natural hazard event, and (2) the ability to examine an array of strategies for improving resilience, such as design code improvements or post-event recovery policies, using decision science. To enhance the ability of communities to make decisions that increase their resilience, a robust quantitative model is needed that can accurately model physical and social systems interactions during simulations of a damaging event and later in the periods of systems repair, restoration, and recovery period. In 2015, the U.S. NIST-funded the Center for Risk-Based Community Resilience Planning (the “NIST Center”) with the main objective of developing a computational environment capable of modeling communities, both large and small, to support resilience planning and evaluation. In this paper, the multidisciplinary scientific approach behind the Center’s open source Interdependent Networked Community Resilience Modeling Environment (IN-CORE) is highlighted. Modeling of buildings and networks, hazards and physical damage and impacts to functionality, local economy, social institutions, alternative resilience strategies, and data and metrics for decision support are encompassed within IN-CORE. This multidisciplinary approach is essential in addressing the interdependent nature of physical, social, and economic systems that contribute to community resilience. Two illustrative examples are presented to highlight the capabilities of IN-CORE.