Assessment of the impact of interdependencies on the resilience of networked critical infrastructure systems

Mao, Quan; Li, Nan

doi:10.1007/s11069-018-3302-3

Cited by 65 publications

(27 citation statements)

References 86 publications

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“…Examples include: connected electrical generation capacity [19], functional cranes at a seaport [20], volume of gas supplied [21], average vehicle speed [22], and travel time in a transportation network [23]. In most cases, measures are normalized relative to a performance target or baseline (e.g., percent of customers with utility service [24], [25] or satisfied demand [26], [27]). A resilience curve is defined by the progression of a specific performance measure over the scenario duration.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Time-Varying Value on Infrastructure Resilience Assessments

Poulin

Kane

2021

IEEE Access

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Infrastructure resilience for a scenario can be assessed quantitatively from resilience curves that plot the evolution of system performance. Summary metrics map these curves to a single value to facilitate comparisons of different systems, scenarios, and policies. Commonly, these metrics are integralbased, e.g., cumulative infrastructure performance. However, since these curves and metrics only examine infrastructure performance, they fail to consider the dynamics of when stakeholders value the performance. For example, a power failure at a hospital during an ordinary day would be of less concern to emergency managers than during a hurricane recovery. This manuscript defines value weighting functions to represent the evolution of stakeholders' value of performance. Temporal correlation between the performance and value weighting functions is described through a stochastic offset. Together, these concepts are used to define a holistic resilience metric: percent value satisfied. Through analytical and numerical approaches, percent value satisfied is treated as a resilience assessment's stochastic output, and its distribution is compared to the naïve metric, cumulative infrastructure performance. The naïve summary metric is shown to be misleading in multiple potential scenarios; this work establishes that resilience assessments must consider the impact of time-varying stakeholder value and its correlation with infrastructure performance. These elements also provide new considerations for resilience assessments: opportunities to improve holistic system resilience without directly affecting infrastructure performance; hazard categories to inform value-weighted resilience analysis; and general insights to guide extensions from performance-based to value-weighted assessment.

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Section: Introductionmentioning

confidence: 99%

The Effect of Time-Varying Value on Infrastructure Resilience Assessments

Poulin

Kane

2021

IEEE Access

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“…The quantitative metric of resilience has fundamental implications in the understanding of the mechanism of resilience [4,5]. Some methods for assessing or quantifying system resilience have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…A disruptive event that directly impacts some infrastructure systems can trigger indirect impacts on other systems. Quantitative assessments have shown that indirect disaster impacts caused by infrastructure interdependencies may be more severe than the direct impacts [4,20]. Therefore, the recovery process of infrastructure systems following a disaster is also affected by the interdependencies among them.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Restoration Resource Allocation Model for Enhancing the Resilience of Interdependent Infrastructure Systems

2019

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Infrastructure systems play a critical role in delivering essential services that are important to the economy and welfare of society. To enhance the resilience of infrastructure systems after a large-scale disruptive event, determining where and when to invest restoration resources is a challenge for decision makers. Comprehensively considering the recovery time of infrastructure systems and the overall losses resulting from a disaster, this study proposes a two-stage restoration resource allocation model for enhancing the resilience of interdependent infrastructure systems. First, to evaluate the effect of resource allocation during the recovery process, dynamic resilience is selected as the criterion for the recovery of infrastructure systems. Second, taking into consideration the decision makers’ point of view, a two-stage resource allocation model is proposed. The objective of the first stage is to quickly recover the infrastructure systems’ dynamic resilience to meet the basic needs of the users. The second stage is aimed at minimizing the overall losses in the following recovery process. The effects of infrastructure interdependencies on resource allocation are incorporated in the model using the dynamic inoperability input–output model. Through a case study, the proposed approach is compared with other resource allocation strategies. The results show that: (1) the restoration resource allocation strategy obtained from the proposed approach balances the recovery time and the overall losses to infrastructure systems; and (2) the value of the usage cost of the unit restoration resource has a significant impact on the recovery time and the overall losses under different strategies. The proposed model is both effective and efficient in solving the post-disaster resource allocation problem and can provide decision makers with scientific decision support.

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“…WDNs must adapt to uncertainties at different evaluation levels and in various scenarios. Proposed improvement strategies for this capability include: (1) Optimizing the components to adapt to natural disasters caused by climate change, such as earthquakes and floods [97][98][99][100]; (2) increasing the absorptive capacity of crucial components to resist targeted attacks and cascading failures in WDNs [24]; (3) strengthening the connection points of the interdependent infrastructure to reduce the number of large-scale cascading failures [99,101,102]; and (4) periodically identifying and updating the service status of aged WDNs components.…”

mentioning

confidence: 99%

“…(3) strengthening the connection points of the interdependent infrastructure to reduce the number of large-scale cascading failures [99,101,102]; and (4) periodically identifying and updating the service status of aged WDNs components.…”

mentioning

confidence: 99%

Review of the Quantitative Resilience Methods in Water Distribution Networks

Shuang

Liu

Porse

2019

Water

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Water distribution networks (WDNs) are critical contributors to the social welfare, economic growth, and public health in cities. Under the uncertainties that are introduced owing to climate change, urban development, aging components, and interdependent infrastructure, the WDN performance must be evaluated using continuously innovative methods and data acquisition. Quantitative resilience assessments provide useful information for WDN operators and planners, enabling support systems that can withstand disasters, recover quickly from outages, and adapt to uncertain environments. This study reviews contemporary approaches for quantifying the resilience of WDNs. 1508 journal articles published from 1950 to 2018 are identified under systematic review guidelines. 137 references that focus on the quantitative resilience methods of WDN are classified as surrogate measures, simulation methods, network theory approaches, and fault detection and isolation approaches. This study identifies the resilience capability of the WDNs and describes the related terms of absorptive, restorative, and adaptive capabilities. It also discusses the metrics, research progresses, and limitations associated with each method. Finally, this study indicates the challenges associated with the quantification of WDNs that should be overcome for achieving improved resilience assessments in the future. earthquake in 1976 [3] stopped the water supply for more than one week, whereas the Northridge earthquake in 1994 [4] destroyed 74 water main pipes, resulting in approximately 1200 leaks and stoppage of water supply for several weeks. The Kobe earthquake in 1995 [5] damaged more than 4000 incidents of damage to water pipes and cut off 1.2 million end users. Only one-third of the WDN was repaired after one week, and the complete period required for repair was two and a half months. The Wenchuan Earthquake in 2008 [6] affected 181 cities in the Sichuan Province and damaged 47,642.5 km of water pipes. Some cities (such as Dujiangyan and Mianzhu) remained deprived of their normal water supply for a year after the occurrence of this earthquake.The emerging uncertainties have introduced new risks with respect to WDN operation and planning. The impacts of climate change, which are already being felt in the water systems in various communities, will become increasingly severe and intense in the future [7,8]. The WDN planners must re-evaluate the balance between water supply and demand under climate-change events such as increase in sea level, earthquakes, storms, and heatwaves [9]. Meanwhile, the WDNs in several industrialized counties and existing systems exhibit endogenous problems such as aging components, increasing their vulnerability to extreme events. Furthermore, the rapid development of cities has led to the high concentration of population in cities as well as the emergence of cities as financial hubs. The cascading failures caused by the closely associated interactive infrastructures can exacerbate the impact and destructiveness of disasters [10,...

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Assessment of the impact of interdependencies on the resilience of networked critical infrastructure systems

Cited by 65 publications

References 86 publications

The Effect of Time-Varying Value on Infrastructure Resilience Assessments

The Effect of Time-Varying Value on Infrastructure Resilience Assessments

A Two-Stage Restoration Resource Allocation Model for Enhancing the Resilience of Interdependent Infrastructure Systems

Review of the Quantitative Resilience Methods in Water Distribution Networks

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