Classification and mathematical modeling of infrastructure interdependencies

Sharma, Neetesh; Nocera, Fabrizio; Gardoni, Paolo

doi:10.1080/23789689.2020.1753401

Cited by 31 publications

(17 citation statements)

References 46 publications

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“…Therefore, interdependencies among networks can only exist at the interfaces. Sharma et al (2021) proposed a novel classification of infrastructure interdependencies. Such classification enables the definition and formulation of the mathematical models needed to account for the various types of interdependencies.…”

Section: Interdependent Networkmentioning

confidence: 99%

“…Also, 𝐺 [1] has structural capacity 𝐂 [1] (𝑡), and demand 𝐃 [1] (𝑡) where the elements of 𝐂 [1] (𝑡) and 𝐃 [1] (𝑡) are the capacity and demand models associate with the network components. In the case of 𝐺 [1] , the supply 𝐒 [1] (𝑡) is defined as 𝐒 [1] (𝑡) = 𝐃 [1] (𝑡) ⊙ 𝟏 {𝐃 [1] (𝑡)≼𝐂 [1] (𝑡)} , and 𝐐 [1] (𝑡) is written as the instantaneous reliability of each of the components (Sharma et al, 2021). Similarly, the water flow network 𝐺 [2] has state variables 𝐱 [2] (𝑡) that include flow pressure, the roughness coefficient of the pipelines, and their geometry, pump curves, and tank characteristics.…”

Section: Estimating the Modeling Resolution Of An Example Water Infra...mentioning

confidence: 99%

“…Also, past research (e.g., Adey et al, 2016;Guidotti et al, 2019;Sharma et al, 2021) has shown that it is important to select the proper level of modeling resolution of infrastructure to adequately capture the impact of damaging events. Sharma et al (2021) defined three types of resolution of infrastructure: the temporal, hierarchical, and spatial resolution. The temporal resolution defines how often the model estimates the time-varying performance of infrastructure.…”

Section: Introductionmentioning

confidence: 99%

“…Sharma et al. (2021) defined three types of resolution of infrastructure: the temporal, hierarchical, and spatial resolution. The temporal resolution defines how often the model estimates the time‐varying performance of infrastructure.…”

Section: Introductionmentioning

confidence: 99%

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Selection of the modeling resolution of infrastructure

Nocera

Gardoni

2022

Computer aided Civil Eng

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Accurate risk analysis requires representative mathematical models of infrastructure. One of the main challenges in developing mathematical models of infrastructure is selecting the modeling resolution from multiple candidates for the required analyses, each candidate having different computational cost, accuracy, and possibly ability to provide information. The goal of selecting the model resolution is to allocate computational resources to the model that best delivers the desired information with the desired accuracy level. This paper proposes a mathematical formulation to select the appropriate resolution for the modeling of infrastructure. We formulate the model selection as an iterative process until a tradeoff is achieved among accuracy, simplicity, and computational efficiency.To select the appropriate level of resolution, we propose novel metrics that measure the level of agreement between estimates of the quantities of interest computed using different levels of resolution. Specifically, we propose global metrics of accuracy that inform if a model is sufficiently detailed or oversimplified. Likewise, we introduce local metrics of accuracy to inform which parts of the model (e.g., spatial portions of the infrastructure model) need refinements. We illustrate the proposed mathematical formulation by selecting the level of resolution of the water infrastructure of Seaside, Oregon, for the purpose of assessing its functionality after a seismic hazard.

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Section: Interdependent Networkmentioning

confidence: 99%

Section: Estimating the Modeling Resolution Of An Example Water Infra...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selection of the modeling resolution of infrastructure

Nocera

Gardoni

2022

Computer aided Civil Eng

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“…Study [48] tried to develop better decisions in the recovering process after a failure in a coupled system by studying its cascading effects when a small disturbance affects them. In [49], researchers aimed to analyse the network robustness under different failure scenarios by characterizing the impact on a network's interdependency. To consider the different classes of infrastructure dependencies, researchers in study [50] proposed a general mathematical formulation to account for these different classes by modelling the post-disaster recovery infrastructure while accounting for its dependencies.…”

Section: Systematic Literature Surveymentioning

confidence: 99%

Disaster Management in Smart Cities

et al. 2021

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The smart city concept, in which data from different systems are available, contains a multitude of critical infrastructures. This data availability opens new research opportunities in the study of the interdependency between those critical infrastructures and cascading effects solutions and focuses on the smart city as a network of critical infrastructures. This paper proposes an integrated resilience system linking interconnected critical infrastructures in a smart city to improve disaster resilience. A data-driven approach is considered, using artificial intelligence and methods to minimize cascading effects and the destruction of failing critical infrastructures and their components (at a city level). The proposed approach allows rapid recovery of infrastructures’ service performance levels after disasters while keeping the coverage of the assessment of risks, prevention, detection, response, and mitigation of consequences. The proposed approach has the originality and the practical implication of providing a decision support system that handles the infrastructures that will support the city disaster management system—make the city prepare, adapt, absorb, respond, and recover from disasters by taking advantage of the interconnections between its various critical infrastructures to increase the overall resilience capacity. The city of Lisbon (Portugal) is used as a case to show the practical application of the approach.

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PR-DRA: PageRank-based defense resource allocation methods for securing interdependent systems modeled by attack graphs

Al-Eiadeh,

Abdallah

2024

Int. J. Inf. Secur.

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Classification and mathematical modeling of infrastructure interdependencies

Cited by 31 publications

References 46 publications

Selection of the modeling resolution of infrastructure

Selection of the modeling resolution of infrastructure

Disaster Management in Smart Cities

PR-DRA: PageRank-based defense resource allocation methods for securing interdependent systems modeled by attack graphs

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