Critical infrastructure systems(CISs)play a key role in the socio-economic activity of a society,but are exposed to an array of disruptive events that can greatly impact their function and performance.Therefore,unders...Critical infrastructure systems(CISs)play a key role in the socio-economic activity of a society,but are exposed to an array of disruptive events that can greatly impact their function and performance.Therefore,understanding the underlying behaviors of CISs and their response to perturbations is needed to better prepare for,and mitigate the impact of,future disruptions.Resilience is one characteristic of CISs that influences the extent and severity of the impact induced by extreme events.Resilience is often dissected into four dimensions:robustness,redundancy,resourcefulness,and rapidity,known as the“4Rs”.This study proposes a framework to assess the resilience of an infrastructure network in terms of these four dimensions under optimal resource allocation strategies and incorporates interdependencies between different CISs,with resilience considered as a stochastic variable.The proposed framework combines an agent-based infrastructure interdependency model,advanced optimization algorithms,Bayesian network techniques,and Monte Carlo simulation to assess the resilience of an infrastructure network.The applicability and flexibility of the proposed framework is demonstrated with a case study using a network of CISs in Austin,Texas,where the resilience of the network is assessed and a“what-if”analysis is performed.展开更多
Lifelines are critical infrastructure systems characterized by a high level of interdependency that can lead to cascading failures after any disaster.Many approaches can be used to analyze infrastructural interdepende...Lifelines are critical infrastructure systems characterized by a high level of interdependency that can lead to cascading failures after any disaster.Many approaches can be used to analyze infrastructural interdependencies,but they are usually not able to describe the sequence of events during emergencies.Therefore,interdependencies need to be modeled also taking into account the time effects.The methodology proposed in this paper is based on a modified version of the Input-output Inoperability Model and returns the probabilities of failure for each node of the system.Lifelines are modeled using graph theory,while perturbations,representing a natural or man-made disaster,are applied to the elements of the network following predetermined rules.The cascading effects among interdependent networks have been simulated using a spatial multilayer approach,while the use of an adjacency tensor allows to consider the temporal dimension and its effects.The method has been tested on a case study based on the 2011 Fukushima Dai-ichi nuclear disaster.Different configurations of the system have been analyzed and their probability of occurrence evaluated.Two models of the nuclear power plant have been developed to evaluate how different spatial scales and levels of detail affect the results.展开更多
Critical infrastructures are becoming increasingly interdependent and vulnerable to cascading failures. Existing studies have analyzed the vulnerability of interdependent networks to cascading failures from the static...Critical infrastructures are becoming increasingly interdependent and vulnerable to cascading failures. Existing studies have analyzed the vulnerability of interdependent networks to cascading failures from the static perspective of network topology structure. This paper develops a more realistic cascading failures model that considers the dynamic redistribution of load in power network to explore the vulnerability of interdependent power-water networks. In this model, the critical tolerance threshold is originally proposed to indicate the vulnerability of network to cascading failures. In addition, some key parameters that are important to network vulnerability are identified and quantified through numerical simulation. Results show that cascading failures can be prevented when the values of tolerance parameter are above a critical tolerance threshold. Otherwise interdependent networks collapse after attacking a critical fraction of power nodes. Interdependent networks become more vulnerable with the increase in interdependence strength, which implies the importance of protecting those interconnected nodes to reduce the consequences of cascading failures. Interdependent networks are most vulnerable under high-load attack, which shows the significance of protecting high-load nodes.展开更多
Critical infrastructures(CI) are designated sectors that if incapacitated or destroyed by natural disasters would have a serious impact on national security and economic and social welfare. Due to the interdependenc...Critical infrastructures(CI) are designated sectors that if incapacitated or destroyed by natural disasters would have a serious impact on national security and economic and social welfare. Due to the interdependency of critical infrastructures failure of one infrastructure during a natural disaster such as earthquake or flood may cause failure of another and so on through a cascade or escalating effect. Quantification of these types of interdependencies between critical infrastructures is essential for effective response and management of resources for rescue, recovery, and restoration during times of crises. This paper proposes a new mathematical framework based on an asymmetric relation matrix constructed in a bottom-up approach for modeling and analyzing interdependencies of critical infrastructures. Asymmetric dependency matrices can be constructed using the asymmetric incidence coefficient based on node-level relationships defined between nodes for measuring the strength of interdependency between node and node, node and network, and networks and networks. These asymmetric matrices are further analyzed for ranking infrastructures in terms of their relative importance and for identifying nodes and infrastructure networks that play a critical role in chain effects among infrastructures involved in geo-disaster events such as flooding. Examples of interdependency analysis for the identification of vulnerabilities among fifteen national defense-related infrastructure sectors by the Australian government and a simulated example using the newly developed GIS-based network simulator Geo PN are used to validate and demonstrate the implementation and effectiveness of interdependency analysis methods in analyzing infrastructure interdependency during a flooding event.展开更多
Civil infrastructure system maintenance planning is to determine which facility should be repaired,when and how maintenance should be carried out,and what treatment should be used under budget and other resource const...Civil infrastructure system maintenance planning is to determine which facility should be repaired,when and how maintenance should be carried out,and what treatment should be used under budget and other resource constraints.In the existing literature,various simulation and optimization models have been developed to help select the optimal maintenance plan.However,the developed models overlooked the deterioration propagation between adjacent connected facilities of the network infrastructure system.For instance,a facility receiving zero maintenance or having a failure of maintenance treatment affects not only the condition of itself,but also the deterioration rate of its neighboring facilities.This raises the call for taking the deterioration propagation into consideration when developing optimization models and capture to which extent it can affect the optimal maintenance plan.Therefore,in this paper,an infrastructure maintenance planning model considering the deterioration propagation between facilities is formulated as a mixed integer linear programming problem.A heuristic algorithm was proposed to solve the problem efficiently.Example networks were tested for the performance comparison between CPLEX and the heuristic algorithm.The proposed model performs better than models without the deterioration propagation.展开更多
文摘Critical infrastructure systems(CISs)play a key role in the socio-economic activity of a society,but are exposed to an array of disruptive events that can greatly impact their function and performance.Therefore,understanding the underlying behaviors of CISs and their response to perturbations is needed to better prepare for,and mitigate the impact of,future disruptions.Resilience is one characteristic of CISs that influences the extent and severity of the impact induced by extreme events.Resilience is often dissected into four dimensions:robustness,redundancy,resourcefulness,and rapidity,known as the“4Rs”.This study proposes a framework to assess the resilience of an infrastructure network in terms of these four dimensions under optimal resource allocation strategies and incorporates interdependencies between different CISs,with resilience considered as a stochastic variable.The proposed framework combines an agent-based infrastructure interdependency model,advanced optimization algorithms,Bayesian network techniques,and Monte Carlo simulation to assess the resilience of an infrastructure network.The applicability and flexibility of the proposed framework is demonstrated with a case study using a network of CISs in Austin,Texas,where the resilience of the network is assessed and a“what-if”analysis is performed.
基金the European Research Council under the Grant agreement no.ERC_IDEAL RESCUE_637842 of the project IDEAL RESCUE_Integrated Design and Control of Sustainable Communities during Emergencies.
文摘Lifelines are critical infrastructure systems characterized by a high level of interdependency that can lead to cascading failures after any disaster.Many approaches can be used to analyze infrastructural interdependencies,but they are usually not able to describe the sequence of events during emergencies.Therefore,interdependencies need to be modeled also taking into account the time effects.The methodology proposed in this paper is based on a modified version of the Input-output Inoperability Model and returns the probabilities of failure for each node of the system.Lifelines are modeled using graph theory,while perturbations,representing a natural or man-made disaster,are applied to the elements of the network following predetermined rules.The cascading effects among interdependent networks have been simulated using a spatial multilayer approach,while the use of an adjacency tensor allows to consider the temporal dimension and its effects.The method has been tested on a case study based on the 2011 Fukushima Dai-ichi nuclear disaster.Different configurations of the system have been analyzed and their probability of occurrence evaluated.Two models of the nuclear power plant have been developed to evaluate how different spatial scales and levels of detail affect the results.
基金Acknowledgments This work is supported by National Natural Science Foundation of China (No. 71501158 71471146) and "the Fundamental Research Funds for the Central Universities". The authors would like to thank the referees for their efforts to improve the quality of this paper.
文摘Critical infrastructures are becoming increasingly interdependent and vulnerable to cascading failures. Existing studies have analyzed the vulnerability of interdependent networks to cascading failures from the static perspective of network topology structure. This paper develops a more realistic cascading failures model that considers the dynamic redistribution of load in power network to explore the vulnerability of interdependent power-water networks. In this model, the critical tolerance threshold is originally proposed to indicate the vulnerability of network to cascading failures. In addition, some key parameters that are important to network vulnerability are identified and quantified through numerical simulation. Results show that cascading failures can be prevented when the values of tolerance parameter are above a critical tolerance threshold. Otherwise interdependent networks collapse after attacking a critical fraction of power nodes. Interdependent networks become more vulnerable with the increase in interdependence strength, which implies the importance of protecting those interconnected nodes to reduce the consequences of cascading failures. Interdependent networks are most vulnerable under high-load attack, which shows the significance of protecting high-load nodes.
基金finically supported by a project “Modeling Infrastructure Interdependency for Emergency Management Using a Network-Centric Spatial Decision Support System Approach” awarded jointly by the Natural Science and Engineering Research Council of Canada (NSERC)the Public Safety and Emergency Preparedness Canada (PSEPC) (No.JIIRP 312733-04)
文摘Critical infrastructures(CI) are designated sectors that if incapacitated or destroyed by natural disasters would have a serious impact on national security and economic and social welfare. Due to the interdependency of critical infrastructures failure of one infrastructure during a natural disaster such as earthquake or flood may cause failure of another and so on through a cascade or escalating effect. Quantification of these types of interdependencies between critical infrastructures is essential for effective response and management of resources for rescue, recovery, and restoration during times of crises. This paper proposes a new mathematical framework based on an asymmetric relation matrix constructed in a bottom-up approach for modeling and analyzing interdependencies of critical infrastructures. Asymmetric dependency matrices can be constructed using the asymmetric incidence coefficient based on node-level relationships defined between nodes for measuring the strength of interdependency between node and node, node and network, and networks and networks. These asymmetric matrices are further analyzed for ranking infrastructures in terms of their relative importance and for identifying nodes and infrastructure networks that play a critical role in chain effects among infrastructures involved in geo-disaster events such as flooding. Examples of interdependency analysis for the identification of vulnerabilities among fifteen national defense-related infrastructure sectors by the Australian government and a simulated example using the newly developed GIS-based network simulator Geo PN are used to validate and demonstrate the implementation and effectiveness of interdependency analysis methods in analyzing infrastructure interdependency during a flooding event.
文摘Civil infrastructure system maintenance planning is to determine which facility should be repaired,when and how maintenance should be carried out,and what treatment should be used under budget and other resource constraints.In the existing literature,various simulation and optimization models have been developed to help select the optimal maintenance plan.However,the developed models overlooked the deterioration propagation between adjacent connected facilities of the network infrastructure system.For instance,a facility receiving zero maintenance or having a failure of maintenance treatment affects not only the condition of itself,but also the deterioration rate of its neighboring facilities.This raises the call for taking the deterioration propagation into consideration when developing optimization models and capture to which extent it can affect the optimal maintenance plan.Therefore,in this paper,an infrastructure maintenance planning model considering the deterioration propagation between facilities is formulated as a mixed integer linear programming problem.A heuristic algorithm was proposed to solve the problem efficiently.Example networks were tested for the performance comparison between CPLEX and the heuristic algorithm.The proposed model performs better than models without the deterioration propagation.