Interdependent Infrastructure Networks Research Articles

Developing resilience pathways for interdependent infrastructure networks: A simulation-based approach with consideration to risk preferences of decision-makers

In this study, we propose a methodological framework to identify and evaluate cost-effective pathways for enhancing resilience in large-scale interdependent infrastructure systems, considering decision-makers’ risk preferences. We focus on understanding how decision-makers with varying risk preferences perceive the benefits from infrastructure resilience investments and compare them with upfront costs in the context of high-impact low-probability (HILP) events. First, we compute the costs of interventions as the sum of their capital costs and maintenance costs. The benefits of the interventions include the reduction in physical damage costs and business disruption losses resulting from the improved resilience of the network. In the final stage, we develop statistical models to predict the perceived net benefits of different network resilience configurations in power, water, and transport networks. These models are employed in an optimization framework to identify optimal resilience investment pathways. By incorporating Cumulative Prospect Theory (CPT) in the optimization framework, we show that decision-makers who assign higher weights to low probability events tend to allocate more resources towards post-disaster recovery strategies leading to increased resilience against HILP events, like earthquakes. We illustrate the methodology using a case study of the interdependent infrastructure network in Shelby County, Tennessee.

Robustness of higher-order interdependent networks with reinforced nodes.

In reality, pairwise interactions are no longer sufficient to describe the higher-order interactions between nodes, such as brain networks, social networks, etc., which often contain groups of three or more nodes. Since the failure of one node in a high-order network can lead to the failure of all simplices in which it is located and quickly propagates to the whole system through the interdependencies between networks, multilayered high-order interdependent networks are challenged with high vulnerability risks. To increase the robustness of higher-order networks, in this paper, we proposed a theoretical model of a two-layer partial high-order interdependent network, where a proportion of reinforced nodes are introduced that can function and support their simplices and components, even losing connection with the giant component. We study the order parameter of the proposed model, including the giant component and functional components containing at least one reinforced node, via theoretical analysis and simulations. Rich phase transition phenomena can be observed by varying the density of 2-simplices and the proportion of the network's reinforced nodes. Increasing the density of 2-simplices makes a double transition appear in the network. The proportion of reinforced nodes can alter the type of second transition of the network from discontinuous to continuous or transition-free, which is verified on the double random simplicial complex, double scale-free simplicial complex, and real-world datasets, indicating that reinforced nodes can significantly enhance the robustness of the network and can prevent networks from abrupt collapse. Therefore, the proposed model provides insights for designing robust interdependent infrastructure networks.

Application of clustering algorithms for dimensionality reduction in infrastructure resilience prediction models

Recent studies increasingly adopt simulation-based machine learning (ML) models to analyse critical infrastructure system resilience. For realistic applications, these ML models consider the component-level characteristics that influence the network response during emergencies. However, such an approach could result in a large number of features and cause ML models to suffer from the ’curse of dimensionality’. A clustering-based method is presented that simultaneously minimises the problem of high-dimensionality and improves the prediction accuracy of ML models developed for resilience analysis in large-scale interdependent infrastructure networks. The methodology has three parts: (a) generation of simulation dataset, (b) network component clustering, and (c) dimensionality reduction and development of prediction models. First, an interdependent infrastructure simulation model simulates the network-wide consequences of various disruptive events. The component-level features are extracted from the simulated data. Next, clustering algorithms are used to derive the cluster-level features by grouping component-level features based on their topological and functional characteristics. Finally, ML algorithms are used to develop models that predict the network-wide impacts of disruptive events using the cluster-level features. The applicability of the method is demonstrated using an interdependent power-water-transport testbed. The proposed method can be used to develop decision-support tools for post-disaster recovery of infrastructure networks.

Effective Practices in Flood Adaptation by Recognizing System, Organization, and Project Interdependencies

Flooding is a historical and intensifying challenge to transportation infrastructure and system performance and the interconnected systems it supports. Resilience programs attempt to meet the challenges flooding poses by adapting the infrastructure to withstand environmental pressures and preserve function during and after disasters. Current adaptation practices, however, often take the form of hard protective measures, implemented exclusively on transportation assets with little coordination between organizations. As a result, projects can be overly expensive, offer incomplete protection, and result in catastrophic failures across multiple infrastructure systems, such as in the aftermath of Hurricane Katrina in New Orleans. To build adaptive capabilities effectively, this study recognized the interdependencies among multiple infrastructure systems at the system-, organizational-, and project levels of planning. At the system level, adaptations addressing hazard-based threats to infrastructure systems—such as stormwater and transportation—strengthened infrastructure against cascading failures and improve efficiency in adapting interdependent infrastructure networks. At an organization level, teams that self-reorganized to fit project needs were found to respond more effectively to change and overcame difficulties in implementing adaptations. At a project level, anticipating and addressing causal relationships between the environment and infrastructure were found to improve the reliability of adaptations against catastrophic failure. The effective practices presented came from Malaysia, Sweden, Poland, and other countries around the world. Implementation of these complementary practices, together with asset-based adaptations as needed, can build adaptive capabilities by addressing the interdependencies between infrastructure planning at multiple levels.

Data for critical infrastructure network modelling of natural hazard impacts: Needs and influence on model characteristics

Natural hazards impact interdependent infrastructure networks that keep modern society functional. While a variety of modelling approaches are available to represent critical infrastructure networks (CINs) on different scales and analyse the impacts of natural hazards, a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data. The resulting data gaps often require modellers to assume specific technical parameters, functional relationships, and system behaviours. In other cases, expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. The uncertainties introduced by these assumptions and extrapolations and their influence on the quality of modelling outcomes are often poorly understood and difficult to capture, thereby eroding the reliability of these models to guide resilience enhancements. Additionally, ways of overcoming the data availability challenges in CIN modelling, with respect to each modelling purpose, remain an open question. To address these challenges, a generic modelling workflow is derived from existing modelling approaches to examine model definition and validations, as well as the six CIN modelling stages, including mapping of infrastructure assets, quantification of dependencies, assessment of natural hazard impacts, response & recovery, quantification of CI services, and adaptation measures. The data requirements of each stage were systematically defined, and the literature on potential sources was reviewed to enhance data collection and raise awareness of potential pitfalls. The application of the derived workflow funnels into a framework to assess data availability challenges. This is shown through three case studies, taking into account their different modelling purposes: hazard hotspot assessments, hazard risk management, and sectoral adaptation. Based on the three model purpose types provided, a framework is suggested to explore the implications of data scarcity for certain data types, as well as their reasons and consequences for CIN model reliability. Finally, a discussion on overcoming the challenges of data scarcity is presented.

Approaching the challenge of multi-phase, multi-hazard volcanic impact assessment through the lens of systemic risk: application to Taranaki Mounga

Effective volcanic impact and risk assessment underpins effective volcanic disaster risk management. Yet contemporary volcanic risk assessments face a number of challenges, including delineating hazard and impact sequences, and identifying and quantifying systemic risks. A more holistic approach to impact assessment is required, which incorporates the complex, multi-hazard nature of volcanic eruptions and the dynamic nature of vulnerability before, during and after a volcanic event. Addressing this need requires a multidisciplinary, integrated approach, involving scientists and stakeholders to co-develop decision-support tools that are scientifically credible and operationally relevant to provide a foundation for robust, evidence-based risk reduction decisions. This study presents a dynamic, longitudinal impact assessment framework for multi-phase, multi-hazard volcanic events and applies the framework to interdependent critical infrastructure networks in the Taranaki region of Aotearoa New Zealand, where Taranaki Mounga volcano has a high likelihood of producing a multi-phase explosive eruption within the next 50 years. In the framework, multi-phase scenarios temporally alternate multi-hazard footprints with risk reduction opportunities. Thus, direct and cascading impacts and any risk management actions carry through to the next phase of activity. The framework forms a testbed for more targeted mitigation and response planning and allows the investigation of optimal intervention timing for mitigation strategies during an evolving eruption. Using ‘risk management’ scenarios, we find the timing of mitigation intervention to be crucial in reducing disaster losses associated with volcanic activity. This is particularly apparent in indirect, systemic losses that cascade from direct damage to infrastructure assets. This novel, dynamic impact assessment approach addresses the increasing end-user need for impact-based decision-support tools that inform robust response and resilience planning.

Game-theoretic algorithm for interdependent infrastructure network restoration in a decentralized environment.

Having reliable interdependent infrastructure networks is vital for well-being of a safe and productive society. Systems are vulnerable to failure or performance loss due to their interdependence among various networks, as each failure can propagate through the whole system. Although the conventional view has concentrated on optimizing the restoration of critical interdependent infrastructure networks using a centralized approach, having a lone actor as a decision-maker in the system is substantially different from the actual restoration decision environment, wherein infrastructure utilities make their own decisions about how to restore their network service. In a decentralized environment, the definition of whole system optimality does not apply as each decision-maker's interest may not converge with the others. Subsequently, this results in each decision-maker developing its own reward functions. Therefore, in this study, we address the concern of having multiple decision-makers with various payoff functions in interdependent networks by proposing a decentralized game theory algorithm for finding Nash equilibria solutions for network restoration in postdisaster situations.

Developing Resilience Pathways for Interdependent Infrastructure Networks: A Simulation-Based Approach with Consideration to Risk Preferences of Decision-Makers

Developing Resilience Pathways for Interdependent Infrastructure Networks: A Simulation-Based Approach with Consideration to Risk Preferences of Decision-Makers

Cascading failures in interdependent networks with reinforced crucial nodes and dependency groups

Previous studies of group percolation models in interdependent networks with reinforced nodes have rarely addressed the effects of the degree of reinforced nodes and the heterogeneity of group size distribution. In this paper, a cascading failure model in interdependent networks with reinforced crucial nodes and dependency groups is investigated numerically and analytically. For each group, we assume that if all the nodes in a group fail on one network, a node on another network that depends on that group will fail. We find that rich percolation transitions can be classified into three types: discontinuous, continuous, and hybrid phase transitions, which depend on the density of reinforced crucial nodes, the group size, and the heterogeneity of group size distribution. Importantly, our proposed crucial reinforced method has higher reinforcement efficiency than the random reinforced method. More significantly, we develop a general theoretical framework to calculate the percolation transition points and the shift point of percolation types. Simulation results show that the robustness of interdependent networks can be improved by increasing the density of reinforced crucial nodes, the group size, and the heterogeneity of group size distribution. Our theoretical results can well agree with numerical simulations. These findings might develop a new perspective for designing more resilient interdependent infrastructure networks.

ResQ-IOS: An iterative optimization-based simulation framework for quantifying the resilience of interdependent critical infrastructure systems to natural hazards

Critical Infrastructure Systems are highly complex and interdependent. Growing complexity and interdependency between infrastructure systems and frequent exposure to extreme events have inevitably increased the probability of cascading failures and the prolonged lack of serviceability in urban communities, especially so for energy systems. The resilience analysis of interdependent infrastructure systems against natural hazards provides stakeholders with a comprehensive outlook on recovery strategies to minimize the damage costs and losses caused by extreme events. This paper introduces the ResQ-IOS, a Resilience Quantification Iterative Optimization-based Simulation (IOS) framework for quantifying the resilience of interdependent infrastructure systems to natural hazards with the capability of considering the real-world conditions for the status of infrastructure systems' components. The ResQ-IOS framework consists of five modules: risk assessment, simulation, optimization, database, and controller. To evaluate the capabilities of this framework, the seismic resilience of interdependent energy infrastructure networks (power, natural gas, and water) in Shelby County (TN), USA, was assessed. The results of the resilience analysis of the case study suggest that the water network is the best candidate for implementing pre-disaster Resilience Enhancement Measures (REMs), like increasing the supply capacity. Due to the controlling role of the power network in the community's recovery process, it is recommended that post-disaster REMs, such as increasing the number of Repair and Maintenance (R&M) teams, should be applied to the power network to speed up the restoration of failed components in that network and consequently, shorten the recovery duration of the community. The ResQ-IOS can be employed as a useful computational tool for planning the resilience-oriented sustainable development of urban communities by, for example, deploying Renewable Energy (RE)-based strategies to enhance their disaster resilience.

Resilience Enhancement for Multistate Interdependent Infrastructure Networks: From a Preparedness Perspective

Reducing vulnerability and enhancing resilience of infrastructure networks subject to uncertain disruptions is a challenging task as these networks become increasingly interconnected and interdependent. This article devotes to addressing the preparedness planning problem of interdependent infrastructure networks. The interdependency between infrastructure networks is characterized by a two-way physical interdependency, that is, the state of one infrastructure network is dependent on that of another infrastructure network, and vice versa. Furthermore, the multistate characteristics of infrastructure networks are taken into account and accommodated to the studied physical interdependency. A tailored two-stage stochastic programming framework, which is able to cope with the uncertainty associated with disruption scenarios, is put forth to facilitate an effective resilience enhancement strategy under a limited budget. In this framework, the first-stage problem selects the optimal protection levels of network components in advance, whereas the second-stage problem optimizes the network operation after a disruption scenario occurs. The proposed framework is implemented to an illustrative system composed of interdependent power–water distribution networks to demonstrate the effectiveness of the resilience enhancement strategy.

Analyzing the tradeoff between vulnerability and recoverability investments for interdependent infrastructure networks

Critical cyber-physical infrastructure networks such as electric power and water networks form the backbone of a functional society as these networks provide the main resources for economic productivity, healthy communities, and general day-to-day operations. However, (i) these infrastructure networks have become more interdependent on each other in terms of functionality, and (ii) community networks become more dependent on these critical infrastructures in terms of existence. Thus, studying the entirety of their resilience process—both vulnerability and recoverability against a disruption—is of importance, particularly analyzing how investing (i) before to strengthen components versus (ii) afterward to restore disrupted components can affect the resilience of infrastructure networks in terms of system performance and time. This paper takes a first step toward analyzing the tradeoff between reducing vulnerability and enhancing recoverability in interdependent infrastructure networks with multiple time and budget allocations, accounting for not only the performance of the physical networks but also the potentially socially vulnerable communities that rely upon them. The proposed model (i) maximizes a measure of the resilience of the interdependent infrastructure networks, while it (ii) minimizes the total cost associated with the pre- and post-event resource allocation. The model is illustrated with networks in Shelby County, TN, USA.

The Research On Statistical Identification of Critical Infrastructure Chain Failure Paths

Using literature studies, case studies, empirical induction and statistical identification, we proposed an analytical framework based on empirical induction to identify and analyze chain failure problems among interdependent critical infrastructures. A case database of critical infrastructure chain failures was established, and as the research emphasis the identification method of critical infrastructure chain failure paths was constructed and designed. The chain failure paths of the critical infrastructure and its sub-networks in China and other countries and regions were discussed. Logical relationships of critical infrastructure chain failures were extracted from the established database covering 229 critical infrastructure chain failure cases. Statistics, identification, and analysis of data were conducted to form a quantitative assessment and understanding of the vulnerability of interdependent critical infrastructure networks. Finally, identification methods and probabilistic statistical methods for chain failure paths of critical infrastructure were designed and applied to the case base of this paper. Using this approach, fatal and severe failure paths of critical infrastructure can be identified and the efficiency of the relevant authorities' inspection work will be improved under cost constraints.

Full-time scale resilience enhancement strategies for an integrated electricity-gas-transportation coupled system

The frequent occurrence of extreme disaster events with low probability and high risk poses a serious threat to the safe and stable operation of power grids. With the development of energy internet technology, the deep integration of power distribution, natural gas and transportation networks has opened a promising way for resilient distribution networks. This paper proposes a full-time scale resilience enhancement framework for an integrated electricity-gas-transportation coupled system oriented to energy internet. In this framework, a minimax regret robust optimization model considering the uncertainty of extreme disaster attacks is developed to make the line hardening strategy in the pre-disaster stage, which is solved by the column-and-constraint generation algorithm. Furthermore, the synthetic restoration model considering the co-optimization of dynamic dispatch of gas-fired units, network reconfiguration and damaged component repair is established, in which the coupling influence of the gas and transportation networks on the restoration strategy during the disaster and post disaster stages is taken into account. And the sequential cone programming method and generalized Benders decomposition algorithm is implemented to solve the above model. Finally, the proposed model and solution method are validated via a modified 33-bus power distribution network coupled with a 12-node transportation network and a 7-node gas network. The simulation results demonstrate that the proposed minmax regret robust optimization model can achieve a pre-disaster defensive strategy with less conservativeness in contrast to the conventional robust optimization model. Specifically, the cost under the worst-case attacks by the former has reduced by 11.52–22.29% compared to the latter. Moreover, the influence of the interdependent critical infrastructure networks on the synthetic restoration strategy in the disaster and post-disaster stages has been investigated. In particular, the power output by gas-fired units has reduced by 2699 kW when considering the operation constraints of the gas network. Additionally, due to the time-varying characteristics of traffic flow and commuting time in the transportation network, the repair time for the damaged components is shortened by 1.09 h while the preparation time of crews changes from 2.5 to 1.5 h.

Integrated reinforcement and repair of interdependent infrastructure networks under disaster-related uncertainties

Natural or human-inflicted disasters may cause large-scale disruptions in the services of infrastructure networks including power, water, and telecommunication. Restoring the services of these infrastructures is vital in the aftermath of the disaster, so that search-and-rescue activities, relief transportation, and restoration efforts can be efficiently facilitated. On the other hand, operations of these infrastructures may depend on receiving services from one another, resulting in an interdependent network structure. Consequently, addressing the decisions of network reinforcement before the disaster and the repairs in its aftermath needs to take into account this interdependent structure, as well as the uncertainties arising from the timing, location, and magnitude of the disaster.This paper introduces the Stochastic Interdependent Infrastructure Reinforcement and Repair Problem, which considers the pre-disaster reinforcement of interdependent network components and post-disaster repair scheduling in an integrated manner. In making these decisions, the uncertainty on which network components will be disrupted is incorporated into the problem definition. The problem is modeled using scenario-based two-stage stochastic programming. A heuristic based on a genetic algorithm and partial optimization is proposed to solve realistically-sized instances of the problem. Computational experiments not only show that the heuristic is able to find near-optimal solutions within reasonable times, but also illustrate the ability of the approach to help derive managerial insights.

Community structure recovery optimization for partial disruption, functionality, and restoration in interdependent networks

Community structures exist in different infrastructure networks such as water, gas, and power networks among others, where each network is split into multiple sets of components. Such sets are sparsely connected but have densely connected components within each one of them. However, critical networks often depend on each other to function properly. Consequently, such interdependencies can both improve efficiency of the collection of networks but also increase their vulnerability to a disruptive event. In this paper, we address the interdependent network restoration problem from community structures perspective. That is, how to restore community structures in a set of infrastructure networks, that are physically interdependent, following a disruptive event. Accordingly, we develop an optimization model using mixed integer programming that aims to enhance the resilience of the set of infrastructure networks considering their physical interdependency. The model prioritizes the restoration tasks for each infrastructure network, according to their influence on the performance of their respected networks, and allocates and schedules the selected restoration tasks to the available work crews. Furthermore, we present a numerical experiment for the developed optimization model with a real set of interdependent infrastructure networks in Shelby County, TN, USA, considering multiple hypothetical earthquake scenarios of different magnitudes.

Group percolation in interdependent networks with reinforcement network layer.

In many real-world interdependent network systems, nodes often work together to form groups, which can enhance robustness to resist risks. However, previous group percolation models are always of a first-order phase transition, regardless of the group size distribution. This motivates us to investigate a generalized model for group percolation in interdependent networks with a reinforcement network layer to eliminate collapse. Some backup devices that are equipped for a density ρ of reinforced nodes constitute the reinforcement network layer. For each group, we assume that at least one node of the group can function in one network and a node in another network depends on the group to function. We find that increasing the density ρ of reinforcement nodes and the size S of the dependency group can significantly enhance the robustness of interdependent networks. Importantly, we find the existence of a hybrid phase transition behavior and propose a method for calculating the shift point of percolation types. The most interesting finding is the exact universal solution to the minimal density ρ of reinforced nodes (or the minimum group size S) to prevent abrupt collapse for Erdős-Rényi, scale-free, and regular random interdependent networks. Furthermore, we present the validity of the analytic solutions for a triple point ρ (or S ), the corresponding phase transition point p , and second-order phase transition points p in interdependent networks. These findings might yield a broad perspective for designing more resilient interdependent infrastructure networks.

InfraRisk: An open-source simulation platform for resilience analysis in interconnected power–water–transport networks

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 both the risks and resilience of the integrated infrastructure networks against disaster events. InfraRisk provides a virtual platform for decision-makers to experiment and develop region-specific pre-disaster and post-disaster policies to enhance the overall resilience of interdependent urban infrastructure networks.

Simulation and Criticality Assessment of Urban Rail and Interdependent Infrastructure Networks

The role of urban infrastructure is becoming increasingly interdependent, resulting in new sources of vulnerability. Infrastructural asset failure can propagate between rail transportation and other infrastructure networks. There remains a lack of academic research focusing on the dynamic simulation of city-wide infrastructure using real-life data to quantify and cross-compare the criticality of assets. This paper aims to bridge this gap by developing a modeling methodology for interdependent urban infrastructure using complex network theory, which serves as a basis for investigating asset criticality and failure propagation. This modeling framework comprises the distribution of resource supply and demand, the topological representation and skeletonization of the infrastructure network, as well as modeling the propagation of asset failures. The framework is thereafter applied to a case study of the exposure of Greater London’s rail transportation network to failures from electricity infrastructure, selected as a representative example of interdependent infrastructures within a large-scale urban metropolitan area. Two time-based criticality metrics are also proposed to measure the topological extent of infrastructural failures and economic impacts resulting from the failure propagation of given initial failure scenarios. The results of the case study demonstrate that these proposed criticality metrics are effective in capturing the dynamics of failure propagation, and that topological metrics in criticality assessment do not always reflect the resulting economic damages of infrastructural failures.

Project schedule compression for the efficient restoration of interdependent infrastructure systems

The timely restoration of infrastructure networks is critical for society’s functionality and wellbeing. However, it is also a substantially challenging task due to the various logistic and resource restrictions from time to budget and work crew availability. Restoration of infrastructure networks is a time-sensitive task as even a slight delay might cause inconvenience or worse to the community for an extended period. As such, diverse models have centered on determining optimal recovery sequences so that recovery time is minimized. However, in some circumstances, given the importance of a timely recovery, even optimal sequences may not result in a quick enough restoration. To address this problem, this paper proposes a multi-objective model for enhanced restoration based on a mixed integer linear programming model that determines optimal job sequencing and crew assignment for a system of interdependent infrastructure networks, while determining key areas for crashing schedule compression, where additional resources would help expedite the full restoration. The implications of project crashing are represented by a cost benefit analysis for a set of solutions enabling the decision maker to study the tradeoffs between restoration duration and restoration cost. The proposed model is applied to a system of interdependent water, power, and gas networks in Shelby County, TN, USA.