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

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 fourteen university-based consortium of almost 100 col-laborators,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,consul-tants,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 house-hold 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,infras-tructure,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.

Multi-disciplinary seismic resilience modeling for developing mitigation policies and recovery planning

The multi-disciplinary data and information available at a community level comprise the foundation of natural hazard resilience modeling.These data enable and inform mitigation and recovery planning decisions prior to and following damaging events such as earthquakes.This paper presents a multi-disciplinary seismic resilience mod-eling methodology to assess the vulnerability of the built environment and economic systems.This methodology can assist decision-makers with developing effective mitigation policies to improve the seismic resilience of com-munities.Two complementary modeling strategies are designed to examine the impacts of scenario earthquakes from a combined engineering and economic perspective.The engineering model is developed using a probabilis-tic fragility-based modeling approach and is analyzed using Monte Carlo(MC)simulations subject to seismic multi-hazard,including simulated ground shaking and resulting liquefaction of the soil,to quantify the physical damage to buildings and electric power substations(EPS).The outcome of the analysis is subsequently used as input to repair and recovery models to quantify repair cost and recovery time metrics for buildings and as input to functionality models to estimate the functionality of individual buildings and substations by accounting for their interdependency.The economic model consists of a spatial computable general equilibrium(SCGE)model that aggregates commercial buildings into sectors for retail,manufacturing,services,etc.,and aggregates residential buildings into a wide range of household groups.The SCGE model employs building functionality estimates to quantify the economic losses.The outcomes of this integrated modeling consist of engineering and economic impact metrics,which are used to investigate mitigation actions to help inform a community on approaches to achieve its resilience goals.An illustrative case study of Salt Lake County(SLC),Utah,developed through an extensive collaborative partnership and engagement with SLC officials,is presented.The results demonstrate the effectiveness of the proposed methodology in quantifying the loss and functional recovery of infrastructure systems,the impacts on capital stock,employment,and household income and the effect of various mitigation strategies in reducing the losses and functional recovery time subject to earthquakes with varying intensities.