Post-Earthquake Recovery of a Community and Its Electrical Power Supply System

Didier, Max; Sun, Li; Ghosh, Siddhartha; Stojadinović, Božidar

doi:10.7712/120115.3478.869

Cited by 15 publications

(7 citation statements)

References 17 publications

(20 reference statements)

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“…Didier et al developed a resilience loss measure using the supply‐demand mechanism

L O R = \int_{t_{0}}^{t_{r}} ([], D false(t false) - S false(t false)) d t,

where

D false(t false)

is the demand over time and

S false(t false)

is the available supply over time. The measure was specifically developed to assess the underlying supply‐demand mechanism of a civil infrastructure system.…”

Section: Literature Reviewmentioning

confidence: 99%

Large‐scale systems resilience: A survey and unifying framework

Shen¹,

Cassottana

Heinimann³

et al. 2020

Quality & Reliability Eng

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A unifying framework in defining and measuring resilience has been an intense research topic. In this paper, resilience is measured as a function of intrinsic capacities of a system, the effectiveness of recovery, and the extrinsic random shock process. Some existing resilience measures are analyzed as special cases of the proposed unifying measure. Then, we develop a framework in which the key constituents in achieving resilience are identified. Resilience is represented by four key dimensions: reliability, robustness, recovery, and reconfigurability. Finally, some practical and specific strategies are proposed to enhance the resilience of critical infrastructure systems under the proposed framework.

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“…Didier et al developed a resilience loss measure using the supply‐demand mechanism

L O R = \int_{t_{0}}^{t_{r}} ([], D false(t false) - S false(t false)) d t,

where

D false(t false)

is the demand over time and

S false(t false)

is the available supply over time. The measure was specifically developed to assess the underlying supply‐demand mechanism of a civil infrastructure system.…”

Section: Literature Reviewmentioning

confidence: 99%

Large‐scale systems resilience: A survey and unifying framework

Shen¹,

Cassottana

Heinimann³

et al. 2020

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…In addition, the Re-CoDeS framework was used to analyze the resilience of the electric power supply system in Nepal and of the water distribution system and the cellular communication system of the Kathmandu Valley (Didier, Baumberger et al, 2017) after the 2015 Gorkha earthquake. Resilience evaluation of more complex, but still virtual systems is presented in Didier, Sun, Ghosh, & Stojadinovic (2015). Systems with agent-based recovery models, still employing with the distribution components of the system unable to execute the CIS service dispatch.…”

Section: Illustrative Examplementioning

confidence: 99%

A compositional demand/supply framework to quantify the resilience of civil infrastructure systems (Re-CoDeS)

Didier

Broccardo

Esposito

et al. 2017

Sustainable and Resilient Infrastructure

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Originally published in:Sustainable and Resilient Infrastructure , http://doi.org/10.1080/23789689.2017.1364560 Rights / License:Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use. ETH LibraryFull Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tsri20 Disaster resilience of civil infrastructure systems is essential to security and economic stability of communities. The novel compositional demand/supply resilience framework named Re-CoDeS (Resilience-Compositional Demand/Supply) generalizes the concept of disaster resilience across the spectrum of civil infrastructure systems by accounting not only for the ability of the civil infrastructure system to supply its service to the community, but also for the community demand for such service in the aftermath of a disaster. A Lack of Resilience is consequently observed when the demand for service cannot be fully supplied. Normalized resilience measures are proposed to allow for direct comparisons between different civil infrastructure systems at component and system levels. A scheme is introduced to classify component and system configurations with respect to their resilience. In addition to quantify the resilience of civil infrastructure systems, Re-CoDeS can be used to evaluate or design community risk mitigation strategies and to optimize post-disaster recovery.

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“…Specifically, one of the most challenging modeling aspects is the prediction of the post-event demand because of the uncertainty in the human behaviors, such as evacuation or the decision to relocate. A promising approach to track the post-event evolution of EPN supply and demand has been proposed by Didier et al (2015), (2017) and Sun et al (2015). Though underlying models of demand changes during recovery from a hazard have not yet been implemented, the proposed procedure can model variation of the baseline demand through a constant demand multiplier to adjust the base demand of every node.…”

Section: Assessing the Resilience Of Dependent Critical Infrastrucmentioning

confidence: 99%

Modeling the resilience of critical infrastructure: the role of network dependencies

Guidotti

Chmielewski

Unnikrishnan

et al. 2016

Sustainable and Resilient Infrastructure

182

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Water and wastewater network, electric power network, transportation network, communication network, and information technology network are among the critical infrastructure in our communities; their disruption during and after hazard events greatly affects communities’ well-being, economic security, social welfare, and public health. In addition, a disruption in one network may cause disruption to other networks and lead to their reduced functionality. This paper presents a unified theoretical methodology for the modeling of dependent/interdependent infrastructure networks and incorporates it in a six-step probabilistic procedure to assess their resilience. Both the methodology and the procedure are general, can be applied to any infrastructure network and hazard, and can model different types of dependencies between networks. As an illustration, the paper models the direct effects of seismic events on the functionality of a potable water distribution network and the cascading effects of the damage of the electric power network (EPN) on the potable water distribution network (WN). The results quantify the loss of functionality and delay in the recovery process due to dependency of the WN on the EPN. The results show the importance of capturing the dependency between networks in modeling the resilience of critical infrastructure.

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Post-Earthquake Recovery of a Community and Its Electrical Power Supply System

Abstract: Abstract. Research from the past ten years shows that the seismic fragilities of different in-

Cited by 15 publications

References 17 publications

Large‐scale systems resilience: A survey and unifying framework

Large‐scale systems resilience: A survey and unifying framework

A compositional demand/supply framework to quantify the resilience of civil infrastructure systems (Re-CoDeS)

Modeling the resilience of critical infrastructure: the role of network dependencies

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