Objective - To develop computer-based tools that facilitate exploration of decision alternatives for community resilience and that specifically address key challenges related to the scale and interdependencies of the system and the complexities of the planning process.
What is the new technical idea? Communities are places that function under the jurisdiction of geographical boundaries and a governance structure, such as a town, city, or county. All communities have social institutions to meet the needs of individuals and households, including family, economic, government, health, education, community service, religious, cultural, and media organizations. These social institutions rely in many ways on the built environment to function. NIST is developing community-centric guidance and tools to enhance the resilience of communities to hazard events. The approach recognizes the role that buildings and infrastructure systems play in supporting essential community functions over an extended time that includes disruptive events.
In this project, for accessibility by planners, a model will be developed to support community resilience decision-making. The model’s name will be the NIST Alternatives for Resilient Communities (NIST ARC) model. Its underlying methods and models will first be tested in a research version of the model (NIST ARC-R). Data collected during field studies for past hazard events will be used to both inform and validate NIST ARC. The development of NIST ARC will be coordinated with the NIST-funded Center for Risk-Based Community Resilience Planning (CoE), led by Colorado State University. The CoE simulation models and research findings will inform the models and tools developed in this project.
What is the research Plan? A first-generation integrated systems-level model of physical, social, and economic systems at the community scale will be developed to support resilience analysis and decision-making. In the development of plans, the formidable challenges posed by both the analysis and design of the resilience system must be addressed. The analysis must account for or include: a) stochastic phenomena (e.g., timing & severity of hazards, component failure); b) dependencies between and among the resilience subsystems; c) time aspects of resilience (e.g., preparedness through long-term recovery, aging infrastructure); d) connection of the built environment to the social services they support; d) significant uncertainties in knowledge (e.g., in recovery); e) metrics to quantify community resilience; f) large amount and diversity of data needed to characterize a community’s resilience system. The main challenge confronting design, or planning, for community resilience is the need to address the large scale of the resilience system and need to address social, political, budgetary and other constraints.
To address these significant challenges, the research plan adopts an operations research (OR) approach to the problem of community resilience planning. OR is a quantitative approach that is uniquely suited to large scale problems. A main tool of operations research, mathematical programming, is applied here. Formulations (“math programs”) that describe community resilience problems are developed and then are solved using available optimization solvers. The interactive formulation and solution of these models effectively provides decision-makers the ability to identify cost-effective, resilience-improving alternatives that can be the basis of the solutions ultimately adopted.
The research project plan calls for gradually increasing the complexity of the math programming models as the project progresses. Early formulations will deal with only a subset of the resilience system and focus on a single hazard (e.g., floods, levee, single-family homes, population displacement, water system). From here, the formulation will be extended to account for a broader resilience system (e.g., transportation, power, business interruption) and more realism (e.g., probabilistic failure, use of fragilities). Also, math programming techniques will be applied, including multi-objective programming to explore tradeoffs, and modeling to generate alternatives to explore the flexibility in meeting objectives.
Software development is a key part of the research plan. The software development will be aligned with the NIST Planning Guide and Economic Decision Guide and coordinated with related software (e.g., EDGe$). The software development will parallel that of the math programming model. Each model will be tested in NIST ARC-R and then, once tested, incorporated into the web-based tool, NIST ARC. Input from stakeholders from CoE testbeds and from communities using the guide will be solicited.
Certain collaborations are key to success of this project. A key challenge in this project is to make reasonable assumptions within the limitations of the mathematical programming framework, assumptions that capture the essential features of the resilient community systems while acknowledging data and other limitations. This project will rely on insights drawn from the work of the CoE. In addition, collaborations with the CoE on their testbeds (e.g., Galveston, Shelby MSA) and field studies (e.g., Lumberton) are key to informing and testing the models and software. Finally, the CoE simulation models can play a role in evaluating solutions found with the mathematical programming-based software tool.
In addition, as improving resilience is the driver of the math programming models, it is critical that the resilience metrics that are applied have a sound basis. For this reason, there is close collaboration with the Community Assessment Methodology project (7316004) that is focused on developing community resilience metrics and a corresponding assessment methodology.
The timeline of the project reflects a gradual development of the decision support tool. Whereas FY17 focused on assessing the validity of the math programming approach (“proof-of-concept”), FY18 focused on the development of a matlab-based software prototype. In FY19, the web-based prototype version will be built and testing of new formulations will continue in the matlab environment. In FY20 and beyond, the tool will be further developed and tested, engaging an active stakeholder community for input on the tool and for validation purposes. Initial stakeholder involvement will be limited to the groups the Community Resilience team currently works with, which includes users of the Community Resilience Planning Guide and communities involved in the CoE testbeds.