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Journal ArticleDOI: 10.1080/23789689.2020.1753401

Classification and mathematical modeling of infrastructure interdependencies

04 Mar 2021-Vol. 6, pp 4-25
Abstract: Critical infrastructure and their provision of goods, services and resources to communities determine the well-being and economic prosperity of modern society. Critical infrastructure jointly opera...

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12 results found


Journal ArticleDOI: 10.1016/J.RESS.2021.108042
Neetesh Sharma1, Paolo Gardoni1Institutions (1)
Abstract: Risk and resilience analysis research has favored simpler models such as topological connectivity and maximum flow algorithm to model infrastructure performance. However, modeling of dependencies and interdependencies requires high-fidelity flow analyses. Developing a rigorous mathematical formulation to model interdependent infrastructure encounters three sets of challenges: (1) Identifying and understanding the different types of interactions within and across the infrastructure, (2) Modeling the time-varying performance of each infrastructure accurately, and (3) Modeling the interdependencies among infrastructure. This paper presents a mathematical formulation that models infrastructure as a set of generalized flow network objects. The proposed formulation then models infrastructure interdependencies using dynamic interfaces among the network objects, enabling infrastructure-specific multi-fidelity analyses. This formulation is the first in the literature that can model bilateral and looped interdependencies. The paper also presents guidelines for selecting boundaries and resolutions and introduces novel aggregated performance measures that address the disparity in the performance of spatially distributed infrastructure. Finally, the paper illustrates the proposed formulation using two examples. First, a simple example conceptually illustrates the formulation’s details. Second, a real-world example models the performance of interdependent infrastructure for Shelby County, Tennessee, in a post-earthquake scenario to illustrate scalability.

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Topics: Flow network (52%)

4 Citations


Journal ArticleDOI: 10.1016/J.RESS.2021.108074
Abstract: The spatial and temporal extent of disruptions to services provided by infrastructure following disruptive events is directly related to the instantaneous state of the infrastructure and their post-disruption recovery. This paper develops a novel formulation to model the effects of infrastructure deterioration on their time-varying ability to recover after disruptive events. By unifying available models for deterioration and recovery, the paper proposes a general formulation to model the physical state and functionality of deteriorating infrastructure throughout its service life. The paper further develops resilience measures to quantify the temporal and spatial variations of infrastructure's ability to recover after disruptive events. The proposed formulation has a hierarchical structure that enables exploiting readily available data at the lower level of hierarchy to improve the prediction capability of models at the infrastructure level. Incorporating the governing physical laws in the proposed formulation also enables customizing the models to emulate the reality of infrastructure deterioration and recovery. While the formulation is general, the emphasis is on modeling potable water infrastructure as a case in which the deterioration of pipelines grows mostly undetectable until extensively developed. By the time the deterioration becomes visible, a substantial portion of the infrastructure service life has already been depleted, and costly repair or replacement would be inevitable. To illustrate, the proposed formulation has been implemented to model the time-varying reliability and resilience of the potable water infrastructure of the city of Seaside in Oregon, United States. The example highlights the effects of spatially varying exposure conditions and pipelines’ age on the reliability, functionality, recovery, and resilience of the potable water infrastructure.

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Topics: Resilience (network) (55%)

4 Citations


Journal ArticleDOI: 10.1016/J.RESS.2021.107756
Abstract: While significant modeling advances have unpacked the complexities of interdependent infrastructure, post-disaster reconnaissance consistently demonstrates a wide variability of outcomes and how much is still to be learned. With that in mind, one might expect the treatment of uncertainty to be quite advanced in interdependent infrastructure models, but we find that to not be the case. In this work, we identify, define, and describe two key classes of uncertainty: system uncertainty and modeling uncertainty. System uncertainty is inherent in all complex infrastructure systems and possesses several subclasses (e.g., physical uncertainty and operational uncertainty). Modeling uncertainty occurs when researchers downscale a complex system to a mathematical or other symbolic representation. It too has several subclasses (e.g., parameter uncertainty and completeness uncertainty). We then identify how the literature to date treats uncertainty with respect to each type of uncertainty. While some work has investigated the implications of physical and temporal uncertainty, by and large, most types of uncertainty have had minimal exploration, suggesting significant knowledge gaps. Finally, we suggest a path forward for treatment and discussion of uncertainty, including what can be learned from other fields involving complex interdependent systems.

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3 Citations


Journal ArticleDOI: 10.1016/J.RESS.2021.107899
Wencheng Huang, Linqing Li1, Hongyi Liu1, Rui Zhang1  +1 moreInstitutions (1)
Abstract: A Self-Contained Girvan-Newman Algorithm and Mean Variance Model combined approach is proposed to allocate the defense resource in road dangerous goods transportation network. Firstly, the weighted physical network without direction and its weighted service network with direction are established. The Self-Contained Girvan-Newman Algorithm is applied to separate the whole weighted physical network into several communities. Next, based on the service network, the covariance matrix of each separated community is established, the Mean Variance Model is used to allocate the defense resource for each community, which focuses on selecting the option with the lowest probability of loss caused by the dangerous goods transportation accident/risk. The case study is conducted by using the road network and the dangerous goods transportation volume of Dalian, China as the background. When the whole network is separated into 6 communities, the defense resource capability in the whole network is the best. The Power Function Allocation Model (PFAM) is applied as the comparison approach, the overall rescue capability of the network is defined and applied to evaluate the defense resource allocation schemes. The results show that, the approach proposed in this paper has better effectiveness than PFAM, especially when the whole network is separated into 6 communities.

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Topics: Dangerous goods (56%), Resource allocation (56%), Flow network (54%) ... show more

2 Citations


Journal ArticleDOI: 10.1016/J.RESS.2021.107800
Qiling Zou1, Suren Chen1Institutions (1)
Abstract: Devising effective post-hazard recovery strategies is critical in enhancing the resilience of transportation networks (TNs). However, existing work does not consider the multiclass users’ travel behavior in network functionality quantification and the metaheuristic solution procedures often suffer from extensive computational burden due to the exploration need in large solution space and the expensive functionality quantification. This study develops a bilevel decision-making framework for the resilience-based recovery scheduling of the TN in a mixed traffic environment with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). The lower level quantifies the TN's functionality over time considering different travel behavior of CAV and HDV users arisen from their different levels of information perception. The upper level presents a novel deep-ensemble-assisted active learning approach to balance optimization performance and computational cost. This framework can help decision makers better quantify the TN's functionality to support effective recovery scheduling of TN with different mixed traffic scenarios ranging from HDV-only to future CAV-dominant traffic. The optimization approach bears the potential to be extended to solving general large-scale network recovery scheduling problems effectively and efficiently. The proposed methodology is demonstrated using a real-world traffic network in Southern California under earthquake considering deterministic and stochastic repair durations.

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Topics: Resilience (network) (53%), Scheduling (computing) (51%), Travel behavior (51%) ... show more

2 Citations


References
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50 results found


Journal ArticleDOI: 10.1038/30918
Duncan J. Watts1, Steven H. Strogatz1Institutions (1)
04 Jun 1998-Nature
Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

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Topics: Complex network (67%), Evolving networks (65%), Network motif (62%) ... show more

35,972 Citations


Journal ArticleDOI: 10.1109/37.969131
Abstract: The notion that our nation's critical infrastructures are highly interconnected and mutually dependent in complex ways, both physically and through a host of information and communications technologies (so-called "cyberbased systems"), is more than an abstract, theoretical concept. As shown by the 1998 failure of the Galaxy 4 telecommunications satellite, the prolonged power crisis in California, and many other recent infrastructure disruptions, what happens to one infrastructure can directly and indirectly affect other infrastructures, impact large geographic regions and send ripples throughout the national a global economy. This article presents a conceptual framework for addressing infrastructure interdependencies that could serve as the basis for further understanding and scholarship in this important area. We use this framework to explore the challenges and complexities of interdependency. We set the stage for this discussion by explicitly defining the terms infrastructure, infrastructure dependencies, and infrastructure interdependencies and introducing the fundamental concept of infrastructures as complex adaptive systems. We then focus on the interrelated factors and system conditions that collectively define the six dimensions. Finally, we discuss some of the research challenges involved in developing, applying, and validating modeling and simulation methodologies and tools for infrastructure interdependency analysis.

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2,094 Citations


Journal ArticleDOI: 10.1193/1.2830434
David M. Boore1, Gail M. Atkinson2Institutions (2)
01 Feb 2008-Earthquake Spectra
Abstract: This paper contains ground-motion prediction equations (GMPEs) for average horizontal-component ground motions as a function of earthquake magnitude, distance from source to site, local average shear-wave velocity, and fault type. Our equations are for peak ground acceleration (PGA), peak ground velocity (PGV), and 5%-damped pseudo-absolute-acceleration spectra (PSA) at periods between 0.01 s and 10 s. They were derived by empirical regression of an extensive strong-motion database compiled by the “PEER NGA” (Pacific Earthquake Engineering Research Center’s Next Generation Attenuation) project. For periods less than 1s , the analysis used 1,574 records from 58 mainshocks in the distance range from 0 km to 400 km (the number of available data decreased as period increased). The primary predictor variables are moment magnitude M, closest horizontal distance to the surface projection of the fault plane R JB , and the time-averaged shear-wave velocity from the surface to 30 m VS30. The equations are applicable for M =5–8 , RJB 200 km, and VS30= 180– 1300 m / s. DOI: 10.1193/1.2830434

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1,355 Citations


Open accessJournal ArticleDOI: 10.1103/PHYSREVE.69.025103
26 Feb 2004-Physical Review E
Abstract: The magnitude of the August 2003 blackout affecting the United States has put the challenges of energy transmission and distribution into limelight. Despite all the interest and concerted effort, the complexity and interconnectivity of the electric infrastructure precluded us for a long time from understanding why certain events happened. In this paper we study the power grid from a network perspective and determine its ability to transfer power between generators and consumers when certain nodes are disrupted. We find that the power grid is robust to most perturbations, yet disturbances affecting key transmision substations greatly reduce its ability to function. We emphasize that the global properties of the underlying network must be understood as they greatly affect local behavior.

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Topics: Electric power transmission (54%), Blackout (50%)

1,261 Citations


Open access
01 Jan 2010-
Abstract: In accordance with 44 Code of Federal Regulations (CFR) for FEMA, Subpart B, Agency Implementing Procedures, Part 109, an Environmental Assessment (EA) was prepared Pursuant to Section 102 of the National Environmental Policy Act (NEPA) of 1969, as implemented by the regulations promulgated by the President's Council on Environmental Quality (CEQ; 40 CFR Parts 1500-1508) The purpose of the proposed project is to build a new fire station for the City of Munford, Tennessee Fire Department in Shelby County, Tennessee that provides for enhanced response for community and is compliant with national Occupational Safety and Health Administration and National Fire Prevention Association standards An EA was prepared to determine whether to prepare an Environmental Impact Statement (EIS) or a Finding of No Significant Impact (FONSI)

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1,190 Citations


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