8/12/2023 0 Comments Joplin tornado path![]() ![]() The percentage is two times more than the percent of building in damage is because three substations fall within the tornado path. For example, the mean percentage of building nonfunctional is around 52%, and the standard deviation percentage shown as an error bar. The percent shown in the horizontal bar below refers to the number of nonfunctional/damaged buildings divided by all buildings in Joplin. Each component generates 500 samples randomly with their failure status determined, and the percentage of building functional and nonfunctional could be calculated using the updated status of the same sample (such as sample #1) for all the buildings, illustrated herein as an example. Then the failure status of all the buildings is updated by considering their dependencies with the corresponding electric power facilities. Physical Service Resilience Metrics #Īfter finding the damage level for each component (buildings, substations, and poles) based on the components’ fragility curves, their intrinsic failure status is expressed as a binary format with either failed (0) or not-failed (1). The tornado path represents the wind speed within the vortex (multi-vortex in the case of Joplin) that was estimated to have EF5 wind speeds of more than 200 mph, reducing to EF4 wind speeds as the areas move outward from the vortex, and eventually reaching EF1 zone. This figure shows the 2011 Joplin tornado path with EF zones provided in the legend. This section introduces the input for the infrastructure damage analysis including the tornado path, building dataset, and building fragility curves for tornado. If you have a trouble to run CGE model later, please see the instruction at CGE section in this document. The ipopt solver is installed with pyIncore, so the environment should have the solver in it. Used for visualization of geometric objects as matplotlib pathsĮconomic Computable General Equilibrium (CGE) Model uses the ipopt solver. To ensure dependencies are correct, install all modules through conda. The following modules are necessary to run this notebook. The population disclocation model was developed by Nathanael Rosenheim and the CGE model portion provided by Brad Hartman under the supervision of Professor Harvey Cutler. van de Lindt, with the help of the NCSA team (Jong Sung Lee, Chris Navarro, Diego Calderon, Chen Wang, Michal Ondrejcek, Gowtham Naraharisetty, and Yong Wook Kim). *This notebook was created by Lisa Wang, supervised by Professor John W. This example demonstrates how users interact with the IN-CORE computational environment. A population dislocation model provides resilience metrics related to socio-demographics such as population dislocation as a function of income or race/ethnicity. The functionality of the infrastructure was linked with a computable general equilibrium (CGE) economics model that computes specific community resilience metrics in economic terms. Generic tornado paths are also available in IN-CORE, or a user defined tornado path is possible. The initial damage prediction utilized the tornado path, tornado fragility curves representative of a 19- archetype building dataset, and EPN datasets. This Juypter Notebook provides an example of how to use IN-CORE. The Center for Risk-Based Community Resilience Planning simulated this event for buildings and the electrical power network of Joplin in IN-CORE. ![]() The National Institute of Standards and Technology (NIST) conducted a technical investigation of this devastating event which can be found at. The city of Joplin, Missouri, USA, was hit by an EF-5 tornado on May 22, 2011. Even a single high-intensity tornado can result in high casualty rates and catastrophic economic losses and social consequences, particularly for small to medium communities. Tornadoes occur at a high frequency in the United States compared with other natural hazards such as earthquakes and tsunamis but have a substantially smaller footprint. Please note that you might need additional dependencies to run this notebook, such as geopandas and contextily Factor demand percent reduction before and after disaster Percent Reduction of Domestic Supplyĥ.2.2. Computable General Equilibrium (CGE) Modelĥ.2.1. Building Functionality Spatial Distribution Resultsĥ.1. Building Damage Spatial Distribution ResultsĤ.2. MCS chaining with Joplin EPF poles damageģ.4. MCS chaining with Joplin EPF substations damageģ.3. ![]() MCS chaining with Joplin building damageģ.2. Electrical Power Facility (EPF) - Poles Damageģ.1. Electrical Power Facility (EPF) - Substations DamageĢ.3. ![]()
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