Optimization-Based Probabilistic Consequence Scenario Construction for Lifeline Systems

Gearhart, Jared Lee; Brown, Nathanael J. K.; Jones, Dean A.; Nozick, Linda K.; Romero, Natalia; Xu, Ningxiong

doi:10.1193/092711eqs237m

Cited by 10 publications

(9 citation statements)

References 14 publications

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“…It also differs from most recent work in , because although the objective function in that work also contains a contribution factor and two terms, it considers marginal distributions of bridge damage state and the covariance in the bridge damage between pairs of bridges. In this work, we instead explore the use of the ground‐motion intensity distributions as one part of the objective function.…”

Section: Selecting a Subset Of Mapsmentioning

confidence: 98%

“…This qualitative verification is corroborated by our overall error metric MHCE value , which averaged over all sites, and 50 return periods is 30.3%. For determining the size of a randomly chosen set that, on average, obtains the same error, readers can randomly select different size sets of ground‐motion intensity maps and damage maps, normalize the

w_{j^{'}}

, compute the error metrics, and compare . Joint distributions of ground‐motion intensities . The optimization has not explicitly considered the consistency of multivariate ground‐motion distributions when selecting the subset of maps.…”

Section: Case Studymentioning

confidence: 99%

“…Han and Davidson combined an event‐based framework with optimization to select ground‐motion intensity maps. Most recently, an optimization‐based technique has been proposed to select damage maps, but the work is limited to a single ground‐motion intensity scenario; the optimization formulation does not explicitly consider consistency with the input distributions of the ground‐motion intensity or network performance .…”

Section: Introductionmentioning

confidence: 99%

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Ground‐motion intensity and damage map selection for probabilistic infrastructure network risk assessment using optimization

Miller

Baker

2014

Earthq Engng Struct Dyn

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SummaryIn many parts of the world, earthquakes threaten regional infrastructure systems. For modeling risk using stochastic earthquake catalogs, random variables include rupture location and the damage state of different components. Thus, there is an infinite set of possible damage maps that a risk modeler could evaluate in an event‐based probabilistic loss model. Even a finite but large number of damage maps may not be practical, because many network performance measures are computationally expensive. Here, we show a computationally efficient method for selecting a subset of damage maps, corresponding ground‐motion intensity maps, and associated occurrence rates that reasonably estimates the full distribution of the ground‐motion intensity and a target performance measure using optimization. The method chooses a subset of maps and associated annual rates of occurrence that minimizes the error in estimating the distribution of a network performance measure as well as the marginal distributions of ground‐motion intensity exceedance. The joint distribution of the ground‐motion intensity is implicitly included in the objective function of the optimization problem via the network performance measure. We then show how to tune the optimization parameters based on consistency checks related to the network performance measure and the ground‐motion hazard. We illustrate the proposed method with a case study of the San Francisco Bay Area road network to estimate the exceedance curve of the average percentage change in morning commute trip time. This work facilitates expanded and risk‐consistent studies of the impacts of infrastructure networks on regional seismic risk and resiliency. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Selecting a Subset Of Mapsmentioning

confidence: 98%

w_{j^{'}}

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ground‐motion intensity and damage map selection for probabilistic infrastructure network risk assessment using optimization

Miller

Baker

2014

Earthq Engng Struct Dyn

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“…However, this technique could also be applied to the effects of hurricanes and could include other infrastructures. A full description of this optimization-based scenario generation is described in Brown, et al (2011), and extended in Gearhart, et al (2013).…”

Section: Modeling Of Consequence Scenarios For Natural Disastersmentioning

confidence: 99%

“…As such, we proposed a non-linear optimization procedure, as described in Gearhart, et al (2013), to create a much smaller number of scenarios (20 per earthquake event) by using FEMA's Hazus loss estimation tool in conjunction with a non-linear optimization.…”

Section: Identify Hazard-consistent Consequence Scenarios For Each Eamentioning

confidence: 99%

A modeling framework for investment planning in interdependent infrastructures in multi-hazard environments.

Brown¹,

Gearhart²,

Jones³

et al. 2013

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Currently, much of protection planning is conducted separately for each infrastructure and hazard. Limited funding requires a balance of expenditures between terrorism and natural hazards based on potential impacts. This report documents the results of a Laboratory Directed Research & Development (LDRD) project that created a modeling framework for investment planning in interdependent infrastructures focused on multiple hazards, including terrorism. To develop this framework, three modeling elements were integrated: natural hazards, terrorism, and interdependent infrastructures. For natural hazards, a methodology was created for specifying events consistent with regional hazards. For terrorism, we modeled the terrorist's actions based on assumptions regarding their knowledge, goals, and target identification strategy. For infrastructures, we focused on predicting post-event performance due to specific terrorist attacks and natural hazard events, tempered by appropriate infrastructure investments. We demonstrate the utility of this framework with various examples, including protection of electric power, roadway, and hospital networks. 4

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Optimal Resource Allocation Model to Prevent, Prepare, and Respond to Multiple Disruptions, with Application to theDeepwater HorizonOil Spill and Hurricane Katrina

MacKenzie

Kazimi

2021

Applied Risk Analysis for Guiding Homeland Security Policy

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Determining how to allocate resources in order to prevent and prepare for disruptions is a challenging task for homeland security officials. Disruptions are uncertain events with uncertain consequences. Resources that could be used to prepare for unlikely disruptions may be better used for other priorities. This chapter presents an optimization model to help homeland security officials determine how to allocate resources to prevent and prepare for multiple disruptions and how to allocate resources to respond to and recover from a disruption. In the resource allocation model, prevention reduces the probability of a disruption, and preparation and response both reduce the consequences of a disruption. The model is applied to the US Gulf Coast region and considers a Deepwater Horizon-type oil spill and a hurricane similar to Hurricane Katrina.

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Optimization-Based Probabilistic Consequence Scenario Construction for Lifeline Systems

Cited by 10 publications

References 14 publications

Ground‐motion intensity and damage map selection for probabilistic infrastructure network risk assessment using optimization

Ground‐motion intensity and damage map selection for probabilistic infrastructure network risk assessment using optimization

A modeling framework for investment planning in interdependent infrastructures in multi-hazard environments.

Optimal Resource Allocation Model to Prevent, Prepare, and Respond to Multiple Disruptions, with Application to theDeepwater HorizonOil Spill and Hurricane Katrina

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