skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Community vulnerability perspective on robust protection planning in interdependent infrastructure networks
Critical infrastructure networks, including water, power, communication, and transportation, among others, are necessary to society’s functionality. In recent years, the threat of different types of disruptions to such infrastructure networks has become an increasingly important problem to address. Due to existing interdependencies, damage to a small area of one of the networks could have far-reaching effects on the ability to meet demand across the entire system. Common disruption scenarios include, among others, intentional malevolent attacks, natural disasters, and random failures. Similar works have focused on only one type of scenario, but combining a variety of disruptions may lead to more realistic results. Additionally, the concept of social vulnerability, which describes an area’s ability to prepare for and respond to a disruption, must be included. This should promote not only the protection of the most at-risk components but also ensure that socially vulnerable communities are given adequate resources. This work provides a decision making framework to determine the allocation of defensive resources that accounts for all these factors. Accordingly, we propose a multi-objective mathematical model with the objectives of: (i) minimizing the vulnerability of a system of interdependent infrastructure networks, and (ii) minimizing the total cost of the resource allocation strategy. Moreover, to account for uncertainty in the proposed model, this paper incorporates a means to address robustness in finding the most adaptable network protection plan to reduce the vulnerability of the system of interdependent networks to a variety of disruption scenarios. The proposed work is illustrated with an application to social vulnerability and interdependent power, gas, and water networks in Shelby County, Tennessee.  more » « less
Award ID(s):
1635813
PAR ID:
10304439
Author(s) / Creator(s):
 ;  ;  ;  
Date Published:
Journal Name:
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Volume:
235
Issue:
5
ISSN:
1748-006X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Baraldi, P.; null; Zio, E. (Ed.)
    Critical infrastructure networks are becoming increasingly interdependent which adversely impacts their performance through the cascading effect of initial failures. Failing to account for these complex interactions could lead to an underestimation of the vulnerability of interdependent critical infrastructure (ICI). The goal of this research is to assess how important interdependent links are by evaluating the interdependency strength using a dynamic network flow redistribution model which accounts for the dynamic and uncertain aspects of interdependencies. Specifically, a vulnerability analysis is performed considering two scenarios, one with interdependent links and the other without interdependent links. The initial failure is set to be the same under both scenarios. Cascading failure is modeled through a flow redistribution until the entire system reaches a stable state in which cascading failure no longer occurs. The unmet demand of the networks at the stable state over the initial demand is defined as the vulnerability. The difference between the vulnerability of each network under these two scenarios is used as the metric to quantify interdependency strength. A case study of a real power-water-gas system subject to earthquake risk is conducted to illustrate the proposed method. Uncertainty is incorporated by considering failure probability using Monte Carlo simulation. By varying the location and magnitude of earthquake disruptions, we show that interdependency strength is determined not only by the topology and flow of ICIs but also the characteristics of the disruptions. This compound system-disruption effect on interdependency strength can inform the design, assessment, and restoration of ICIs. 
    more » « less
  2. The recurrence of extreme weather events has led to the development of methods for assessing the vulnerability and interdependencies of physical and human systems. A case example is Hurricane Maria (H-Maria), where Puerto Rico experienced damage to 80% of its electrical power system, leading to massive disruptions of essential services for months. Here we evaluate the effectiveness of various interventions aimed at reducing vulnerability by considering power and water infrastructure and respective water–power dependencies while also considering the social vulnerability of affected communities associated with the physical infrastructure upgrades. On the basis of the current infrastructure configuration, we found that all communities suffered enormously from power and water outages. As one upgrade option, we show that incorporating regional energy grids would reduce outages in an H-Maria scenario. However, a large portion of disadvantaged communities will face service disruption under this option. In contrast, hardening transmission lines, as the second option, would improve service delivery and, most importantly, provide uninterrupted service to the higher portion of the vulnerable population. 
    more » « less
  3. Economic damages of hurricanes and tropical cyclones are increasing faster than the populations and wealth of many coastal areas. There is urgency to update priorities of agencies engaged with risk assessment, risk mitigation, and risk communication across hundreds or thousands of water basins. This paper evaluates hydrology and social vulnerability factors to develop a risk register at a subbasin scale for which the priorities of agencies vary by storm scenario using publicly available satellite-based Earth observations. The novelty and innovation of this approach is the quantification and mapping of risk as a disruption of system order, while using social vulnerability indices and sensor data from disparate sources. The results assist with allocating resources across basins under several scenarios of hydrology and social vulnerability. The approach is in several parts as follows: first, a baseline order of basins is defined using the CDC/ATSDR social vulnerability index (SVI). Next, a set of storm scenarios is defined using Earth Observations and modeled data. Next, a swing-weight technique is used to update factor weights under each scenario. Lastly, the importance order of basins relative to the baseline order is used to compare the risk of scenarios across the study area. The risk is thus quantified (by least squares difference of order) as a disruption to the ordering of basins by social and hydrologic factors (i.e., SVI, precipitation, wind speed, and soil moisture), with attention to the most disruptive scenarios. An application is described with extensive mapping of hydrologic basins for Hurricane Ian to demonstrate a versatile method to address uncertainty of scenarios of storm nature and extent across coastal mega-regions. 
    more » « less
  4. Abstract Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards. 
    more » « less
  5. Abstract Damages in critical infrastructure occur abruptly, and disruptions evolve with time dynamically. Understanding the situation of critical infrastructure disruptions is essential to effective disaster response and recovery of communities. Although the potential of social media data for situation awareness during disasters has been investigated in recent studies, the application of social sensing in detecting disruptions and analyzing evolutions of the situation about critical infrastructure is limited. To address this limitation, this study developed a graph‐based method for detecting credible situation information related to infrastructure disruptions in disasters. The proposed method was composed of data filtering, burst time‐frame detection, content similarity calculation, graph analysis, and situation evolution analysis. The application of the proposed method was demonstrated in a case study of Hurricane Harvey in 2017 in Houston. The findings highlighted the capability of the proposed method in detecting credible situational information and capturing the temporal and spatial patterns of critical infrastructure events that occurred in Harvey, including disruptive events and their adverse impacts on communities. The proposed methodology can improve the ability of community members, volunteer responders, and decision makers to detect and respond to infrastructure disruptions in disasters. 
    more » « less