COMPUTATIONAL ENVIRONMENT FOR MODELING AND ENHANCING COMMUNITY RESILIENCE: INTRODUCING THE CENTER FOR RISK-BASED COMMUNITY RESILIENCE PLANNING John W. van de Lindt 1,* , Bruce R. Ellingwood 2 , Therese McAllister 3 , Paolo Gardoni 4 , Daniel T. Cox 5 , Harvey Cutler 6 , and Walter Gillis Peacock 7 1 Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80523-1372, USA. 2 Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80523-1372, USA. 3 Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA 4 Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA 5 School of Civil and Constructional Engineering, Oregon State University, Graf Hall, Corvallis, OR 97331, USA 6 Department of Economics, Colorado State University, Fort Collins, CO 80523-1771, USA 7 Hazard Reduction and Recovery Center and Department of Landscape Architecture and Urban Planning, Texas A & M University, 1372 TAMU, College Station, TX 77843, USA ABSTRACT The resilience of a community is defined as its ability to prepare for, withstand, recover from and adapt to the effects of natural or human-caused disasters, and depends on the performance of the built environment and on supporting social, economic and public institutions that are essential for immediate response and long-term recovery and adaptation. The performance of the built environment generally is governed by codes, standards, and regulations, which are applicable to individual facilities and residences, are based on different performance criteria, and do not account for the interdependence of buildings, transportation, utilities and other infrastructure sectors. The National Institute of Standards and Technology recently awarded a new Center of Excellence (NIST-CoE) for Risk-Based Community Resilience Planning, which is headquartered at Colorado State University and involves nine additional universities. Research in this Center is focusing on three major research thrusts: (1) developing the NIST-Community Resilience Modeling Environment known as NIST-CORE, thereby enabling alternative strategies to enhance community resilience to be measured quantitatively; (2) developing a standardized data ontology, robust data architecture and data management tools in support of NIST-CORE; and (3) performing a comprehensive set of hindcasts on disasters to validate the data architecture and NIST-CORE. KEYWORDS Community resilience, hazards, investment optimization, post-disaster recovery, resilience performance metrics, risk-informed decision. INTRODUCTION AND MOTIVATION Disaster resilience has been defined many ways, but one major commonality exists in virtually all definitions: the ability to rebound following a shock or major disruption. Presidential Policy Directive (PPD) 8: National Preparedness (2011) defines resilience as the ability to “adapt to changing conditions and withstand and rapidly recover from disruption due to emergencies.” This definition was expanded in Presidential Policy Directive (PPD) 21 (2013) which defines resilience as the ability to “prepare for and adapt to changing conditions and withstand and recover rapidly from disruptions.” The ability of a community to recover from a disaster is a function of many factors including pre-event planning, preparation, mitigation, infrastructure type, complexity, condition, individual and collective experiences prior to the disaster and the ability to mobilize resources afterward. Prior to the disaster, a community may contain a heterogeneous mix of populations and institutions of varying degrees of vulnerability. Following the occurrence of an extreme event, there is a rapid drop in community functional capacity, followed by a period of response and recovery, leading to a “new normal” state for the community. For example, the most vulnerable communities may have a diminished “new normal,” in which the community functions at a level that is below where they were prior to the event because they were only able to build back based on pre-impact or diminished capabilities; an example of this is the City of New Orleans, Louisiana, USA shown flooded in Fig 1a following Hurricane Katrina in 2005, particularly in the lower 1154