Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

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.

Mapping Network States Using Connectivity Queries

“…The failure model leverages 'geographically correlated' failures, which have been extensively used in prior work both in the computer science [10] and civil engineering communities [1]. At an abstract level, given a disaster scenario o ∈ O, some set of edges I ⊆ E fail.…”

“…Inferring network states from partial observations is an important problem in many applications, such as in epidemics, social networks and Internet/AS graphs. Consider critical infrastructure networks like water distribution system or electric grids -their rapid functional restoration is a priority right after a disaster such as an earthquake [1]. The sparse installation of real time monitoring systems installed in these networks (e.g.…”

Topological clustering of multilayer networks

“…M any modern human-made systems, e.g. critical infrastructures integrating operations of vital societal physical and cyber services such as power systems, telecommunication, and transportation, as well as a broad range of natural phenomena from human brain functionality to socioenvironmental ecosystems and virus-host interactomes, exhibit a sophisticated, highly interdependent structure (1)(2)(3)(4)(5)(6)(7). Modeling such interdependency can be addressed with multilayer graphs, resulting in a recent surge of interest in the interdisciplinary analysis of complex multilayer networks.…”

Generating Synthetic Systems of Interdependent Critical Infrastructure Networks

“…Infrastructure networks interact with each other through interdependencies which are bidirectional interactions across individual networks that influence the overall system operations. Accurate assessment of the system-level performance of interdependent infrastructure networks is essential to the strategic allocation of resources that maintain and protect these systems as well as accelerate their recovery after disruptions [5], [6]. Ideally, the systemlevel performance would be evaluated using real networks where complete information is available on the network topology, spatial characteristics, and operational parameters, such as capacity and flow within individual networks and across networks through interdependent links [7], [8].…”

“…However, data on the topology and flow of critical infrastructure are typically not publicly available due to privacy and security concerns [9]. Additionally, information on the presence and importance of interdependent links is usually not available due to the decentralized management and lack of coordination across different infrastructure sectors [5]. Ex-post disaster studies have revealed that interdependencies across infrastructure sectors do not behave as expected [10].…”