Modeling interdependent effects of infrastructure failures using imprecise dependency information

Balakrishnan, Srijith; Zhang, Zhanmin

doi:10.1080/23789689.2020.1735836

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…However, the deeper motives of network formation have not been directly observed while analyses of the network formation mechanisms and node interaction processes have been lacking [43]. All three approaches, agent-based models, system dynamics-based models, and input-output models, are inevitably subjective in describing interdependency and influencing research findings [44][45][46]. Regression models have been used in earlier studies to analyze the interactions between infrastructures [47,48], however, regression models are based on the assumption of actors' interdependency, and infrastructures are typically related rather than independent of one another.…”

Section: Methods Used To Study Infrastructure Interdependencymentioning

confidence: 99%

Decision-Making Based on Network Analyses of New Infrastructure Layouts

Wang

Zhang

et al. 2022

Buildings

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New Infrastructure (NI) has gradually become a new driving force for regional economic growth and an important part of the construction of new urban infrastructure in many countries, including China. Compared with traditional infrastructures, these NIs have mutually supportive functions and complex interrelationships that create interconnected networks of resources, information, and other interactions during the construction of the NIs. Therefore, it is important to analyze such correlation networks and explore their formation mechanisms in order to develop more scientific and reasonable strategies for NI investment and construction. In this study, the interdependence between NIs in Chongqing was analyzed as an example. Social network analysis (SNA) was used for the overall characteristics of the interdependency network of the NIs and an exponential random graph model (ERGM) was used to reveal the formation mechanism of this network. The results showed that information infrastructure is the key node for enhancing the effectiveness of Chongqing’s NI needs and its government should play a coordinating role. The network of related relationships is characterized by “reciprocity” and “small group”. The aggregation of NIs with such characteristics can produce an agglomeration effect. So, in the planning of NIs, the coordination among management departments should be strengthened and project locations should be reasonably arranged according to the functional interactive characteristics of the projects.

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Section: Methods Used To Study Infrastructure Interdependencymentioning

confidence: 99%

Decision-Making Based on Network Analyses of New Infrastructure Layouts

Wang

Zhang

et al. 2022

Buildings

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“…The agent-based modeling (ABM) is a bottom-up approach for modeling integrated infrastructure networks at a more granular scale by considering them as system-of-systems (SoS) [26]. ABMs simulate infrastructure components as agents and allow them to interact with each other in representative operating environments to obtain quantitative insights into the system behavior [27,28,29]. Some of the key issues pertaining to ABM are its over-dependence on assumptions, calibration of parameters, and validation of the results [7].…”

Section: Traditional Infrastructure Simulation Modelsmentioning

confidence: 99%

InfraRisk: An Open-Source Simulation Platform for Asset-Level Resilience Analysis in Interconnected Infrastructure Networks

Balakrishnan,

Cassottana

2022

Preprint

Self Cite

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Integrated simulation models are emerging as an alternative for analyzing large-scale interdependent infrastructure networks due to their modeling advantages over traditional interdependency models. This paper presents an open-source integrated simulation package for the asset-level analysis of interdependent infrastructure systems. The simulation platform, named 'InfraRisk' and developed in Python, can simulate disaster-induced infrastructure failures and subsequent post-disaster restoration in interconnected water-, power-, and road networks. InfraRisk consists of an infrastructure module, a hazard module, a recovery module, a simulation module, and a resilience quantification module. The infrastructure module integrates existing infrastructure network packages (wntr for water networks, pandapower for power systems, and a static traffic assignment model for transportation networks) through an interface that facilitates the network-level simulation of interdependent failures. The hazard module generates infrastructure component failure sequences based on various disaster characteristics. The recovery module determines repair sequences and assigns repair crews based on predefined heuristics-based recovery strategies or model predictive control (MPC) based optimization. Based on the schedule, the simulation module implements the network-wide simulation of the consequences of the disaster impacts and the recovery actions. The resilience quantification module offers system-level and consumer-level metrics to quantify

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“…The natural gas-fired generation has been considered one of the most economical ways to reduce emissions and move toward a decarbonized power grid, 6 , 7 , 8 , 9 and it has been considered an efficient way to provide operational flexibility to support renewable integrations. 10 , 11 , 12 , 13 However, other studies raised concerns about the strong coupling between electricity and gas systems, such as energy security, 14 , 15 , 16 , 17 financial risk, 18 , 19 and delay of renewable energy transitions. 20 , 21 New England, where electricity generation and gas consumption are strongly intertwined, has been continuously receiving benefits and suffering issues from the tight coupling between electricity and gas.…”

Section: Introductionmentioning

confidence: 99%

Implications of electricity and gas price coupling in US New England region

Zhang,

Li,

Fang

et al. 2024

iScience

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Modeling interdependent effects of infrastructure failures using imprecise dependency information

Cited by 7 publications

References 38 publications

Decision-Making Based on Network Analyses of New Infrastructure Layouts

Decision-Making Based on Network Analyses of New Infrastructure Layouts

InfraRisk: An Open-Source Simulation Platform for Asset-Level Resilience Analysis in Interconnected Infrastructure Networks

Implications of electricity and gas price coupling in US New England region

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