Interdependent Infrastructure Research Articles

Interdependent network restoration games with incomplete information and bounded rationality.

Communities face the challenge of finding restoration strategies in the aftermath of disasters. In particular, independent and self-interested utility managers devise such strategies for infrastructure through a heuristic decentralized process. This paper takes a game-theoretic approach to model the decentralized and strategic restoration decision making with application to interdependent infrastructure. Particularly, we model the decision process using simultaneous games to investigate decision makers' conflicting preferences. We employ Bayesian games to incorporate the realistic assumptions of poor interagent communication, resulting in incomplete information. Also, we account for behavioral biases such as bounded rationality, cooperative behavior or lack thereof, and equality-driven resource allocations. We test our models using ideal, synthetic interdependent networks, and the realistic infrastructure of Shelby County, TN. Results show that cooperation leads to the best-performing decisions even if others are not cooperative. The necessity of cooperation is even higher when there is a dominant player whose service is vital to other players. Our sensitivity results highlight the significant influence of resource availability and allocation on the performance of restoration plans. Our approach enhances the practicality of decision models for community resilience, and unravels novel policy strategies such as cooperationincentives.

Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors

Infrastructure systems are particularly vulnerable to climate hazards, such as flooding, wildfires, cyclones and temperature fluctuations. Responding to these threats in a proportionate and targeted way requires quantitative analysis of climate risks, which underpins infrastructure resilience and adaptation strategies. The aim of this paper is to review the recent developments in quantitative climate risk analysis for key infrastructure sectors, including water and wastewater, telecommunications, health and education, transport (seaports, airports, road, rail and inland waterways), and energy (generation, transmission and distribution). We identify several overarching research gaps, which include the (i) limited consideration of multi-hazard and multi-infrastructure interactions within a single modelling framework, (ii) scarcity of studies focusing on certain combinations of climate hazards and infrastructure types, (iii) difficulties in scaling-up climate risk analysis across geographies, (iv) increasing challenge of validating models, (v) untapped potential of further knowledge spillovers across sectors, (vi) need to embed equity considerations into modelling frameworks, and (vii) quantifying a wider set of impact metrics. We argue that a cross-sectoral systems approach enables knowledge sharing and a better integration of infrastructure interdependencies between multiple sectors.

Probabilistic framework for quantifying infrastructure systems’ resilience against floods

ABSTRACT This manuscript proposes a probabilistic framework to quantitatively assess communities’ resilience by simulating the state and serviceability of their infrastructure systems from the onset of a flood until the end of recovery. It comprises multiple interconnected models. Hazard model characterizes flood parameters (e.g., depth and velocity). Depth-damage curves estimate the damage to infrastructure systems’ components. The HEC-FIA mortality model characterizes the flood-induced casualties. Serviceability models determine the ability of interdependent infrastructure systems to meet the demand during the recovery process. The recovery models estimate the recovery duration and cost of damaged components probabilistically. The Monte Carlo analysis is employed to consider multiple relevant uncertainties, generating distributions of the flood-induced losses under various scenarios. The ratio of total losses to the gross regional product characterizes the community’s resilience. This framework facilitates the assessment of the impact of various resilience enhancement measures, guiding the selection of appropriate measures through a cost-benefit analysis.

A resilience-based methodology to assess the degree of interdependency between infrastructure

Seismic vulnerability of urban regions depends on the infrastructure systems that need to remain operative during and after earthquakes. In modern societies, the increasing interdependence of the various infrastructures may significantly affect the community resilience because a damage or a failure on a system may propagate to other interconnected ones. In this regard, the original concept of seismic resilience needs to be expanded to a more general definition that may consider the dependence of multiple infrastructures. In particular, this paper aims to develop a formulation of the seismic resilience by proposing an algebraic matrix form for the calculation of the seismic resilience. The presented formulation has the main advantages to be easily implemented in numerical platforms and to calculate the degree of interdependency between infrastructure.

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.

Agent-based modelling of high-speed railway interdependent critical infrastructures facing physical and cyber threats

Globally, high-speed rail systems serve nearly 2 billion passenger-km daily. By virtue, they are a critical infrastructure like telecommunication and power networks. Accordingly, they become a catalyst for societal and economic growth stemming from the mobility business. The highspeed rail operations are very complex and interdependent, owing to the escalated demands for long-distance interconnected transportation. In recent years, there have been unreasonable delays for passengers as a new norm due to unfortunate train cancellations and relaxation of mobility performance requirements. Therefore, accurate measurements, monitoring and prediction of disruptive impacts and service performance metrices are indispensable. Within the scope of high-speed rail services, this paper examines how agent-based and multi-agent-based models are utilized to address such the challenges. Our findings reveal that the current use of agents or multi-agent models has some limitations for practical applications. Previous studies showed that mathematical methods to assess the resilience of critical infrastructures, railway scheduling, and vehicle dispatching can yield more satisfactory outcomes, although the approaches can be relatively time-consuming. In contrast, agent-based and multi-agent-based models can shorten processing time and uncover disruptive events more promptly. The paper thus showcases several emerging concepts, including i) the utilization of big data for crisis management, ii) interconnectivity analysis of high-speed rail infrastructures, and iii) enhancement of transport resilience. In addition, our findings identify the most influential agents and their possible applications to enhance systems resilience of highspeed rail networks when dealing with unforeseen physical and cyber threats.

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

AbstractEffective 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.

Seismic Resilience of Interdependent Built Environment for Integrating Structural Health Monitoring and Emerging Technologies in Decision-Making

The functionality of interdependent infrastructure and resilience to seismic hazards has become a topic of importance across the world. The ability to optimize an engineered solution and support informed decision-making is highly dependent on the availability of comprehensive datasets and requires substantial effort to ingest into community-scale models. In this article, a comprehensive seismic resilience modeling methodology is developed, with detailed multi-disciplinary datasets, and is explored using the state-of-the-science algorithms within the interdependent networked community resilience modeling environment (IN-CORE). The methodology includes a six-step chained/linked process consists of: (a) community data and information, (b) spatial seismic hazard analysis using next-generation attenuation, (c) interdependent community model development, (d) physical damage and functionality analysis, (e) socio-economic impact analysis and (f) structural health monitoring (SHM) and emerging technologies (ET). An illustrative case study is presented to demonstrate the seismic functionality and resilience assessment of Shelby County in Memphis, Tennessee, in the United States. From the discussion of results, it is then concluded that data from structural health monitoring and emerging technologies is a viable approach to enhance characterising the seismic hazard resilience of infrastructure, enabling rapid and in-depth understanding of structural behaviour in emergency situations. Moreover, considering the momentum of the digitalization era, setting an holistic framework on resilience that includes SHM and ET will allow reducing uncertainties that are still a challenge to quantify and propagate, supported by sequential updating techniques from Bayesian statistics.

Understanding cascading risks through real-world interdependent urban infrastructure

Understanding cascading risks through real-world interdependent urban infrastructure

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

Resilience-driven post-disaster restoration of interdependent infrastructure systems under different decision-making environments

Resilience-driven post-disaster restoration of interdependent infrastructure systems under different decision-making environments

The ATLAS Alarm Helper

The Detector Safety System is the last line of defence to protect the ATLAS detector against abnormal and potentially even unforeseen situations. It is designed to return the detector to a safe state based on predefined actions triggered by alarms which are triggered on their part by specific sets of conditions. Every alarm whether it results in an action taken or not is followed up by the operations team that assesses the criticality, takes countermeasures and identifies the point of failure. From experience abnormal situations can result either from faults or from side effects of planned interventions which were either not properly identified despite the mandatory planning and review or where a mistake during execution occurred. In many cases there are multiple interventions ongoing simultaneously in order to profit from shutdown periods. The rapid analysis of alarms while the incident is ongoing is often complicated due to the complexity of the ATLAS detector and its infrastructure and the large number of responsible groups and experts. A new Alarm Helper tool was designed to assist the operation team, particularly the operator in the control room responsible for infrastructure and safety (SLIMOS – Shift Leader in Matters of Safety), by providing real-time information about ongoing interventions and the possible related causes of failure. The new tool will combine historical events, documentation, and limited knowledge about ongoing interventions. It extends the Expert System which visualizes and simulates infrastructure inter-dependencies and allows to trace faults or alarms to a list of potential points of failure. The new tool also proposes which experts should be contacted in the particular circumstances.

InfrastructureModels: Composable Multi-infrastructure Optimization in Julia

In recent years, there has been an increasing need to understand the complex interdependencies between critical infrastructure systems, for example, electric power, natural gas, and potable water. Whereas open-source and commercial tools for the independent simulation of these systems are well established, frameworks for cosimulation with other systems are nascent and tools for co-optimization are scarce—the major challenge being the hidden combinatorics that arise when connecting multiple-infrastructure system models. Building toward a comprehensive solution for modeling interdependent infrastructure systems, this work presents InfrastructureModels, an extensible, open-source mathematical programming framework for co-optimizing multiple interdependent infrastructures. This work provides new insights into methods and programming abstractions that make state-of-the-art independent infrastructure models composable with minimal additional effort. To that end, this paper presents the design of the InfrastructureModels framework, documents key components of the software’s implementation, and demonstrates its effectiveness with three case studies on canonical co-optimization tasks arising in interdependent infrastructure systems. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: The work was funded by Los Alamos National Laboratory’s Directed Research and Development project “The Optimization of Machine Learning: Imposing Requirements on Artificial Intelligence” and the U.S. Department of Energy’s Office of Electricity Advanced Grid Modeling projects “Joint Power System and Natural Gas Pipeline Optimal Expansion Planning” and “Coordinated Planning and Operation of Water and Power Infrastructures for Increased Resilience and Reliability.” This work was carried out under the U.S. DOE contract no. [DE-AC52-06NA25396]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0118 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0118 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Post-disaster restoration planning of interdependent infrastructure Systems: A framework to balance social and economic impacts

Hazard-induced service interruption of interdependent infrastructure systems (IISs) (e.g., electricity, water, gas, etc.) can lead to significant disruptions of social and economic functions of a modern society. An effective post-event restoration of the IISs is therefore of paramount importance to the overall recovery of a hazard-stricken community as a whole. As opposed to approaches with pure engineering perspectives, this study proposes an IISs restoration planning methodology aimed at balancing tradeoffs between the loss of social services (e.g., health care, food supply, etc.) and that of economic productions (e.g., construction, manufacturing, trade, etc.) throughout the IISs restoration process. The methodology is distinguished from previous researches with the following contributions: i) quantitatively relates the losses of various social services and economic productions to the service disruptions of IISs through the functionality loss of buildings; ii) the IISs disruption-induced overall losses of social services and economic productions accumulated throughout the whole recovery process is set as the bi-objective in formulating IISs restoration plans, and the Pareto optimal solutions are given to satisfy different decision preferences; iii) physics-based models capturing operational mechanisms of the IISs are embedded to provide realistic estimations of commodity supplies at each time step of the restoration optimization. The optimization is coupled with Monte Carlo simulation to uncover the impact of decision preference on community recovery from a statistical point of view. Testbed illustration shows that the decision preference makes significant impact on the recovery of the community as a whole and of different areas in the community with different socioeconomic characteristics.

Modeling the resilience of interdependent networks: The role of function dependency in metro and bus systems

Owing to the pervasive interdependency among networks and the great threat of various disasters, one of the most challenging issues is the resilience evaluation of interdependent networks. Existing studies have been conducted to analyze interdependent network resilience addressing unidirectional dependency, which triggers and propagates network failures. The loss and recovery of network functionality are complicated and important under such interdependency. Ignoring the interdependency nature of different networks would lead to incomplete or incorrect results in their resilience assessment. This paper developed a resilience assessment model for interdependent transit networks under failures. Taking the interdependency relations, network topology, flow characteristics, and demand distribution into account, the proposed methodology explicitly quantifies the impacts of varying network interdependency on the resilience of interdependent networks. The approach was applied to the interdependent metro and bus networks of Xi’an, China. Results show the resilience of interdependent networks is greatly affected by node degree heterogeneity in topology. The higher the heterogeneity of interdependent nodes, the larger the network resilience becomes. The bidirectional function dependency among networks demonstrates dominant effects on the resilience of interdependent networks if one network is disrupted. If interdependent networks have homogeneous function, the resilience of the networks would be significantly improved with the increase in interdependency. The degree of flow matching between both networks plays a particularly important role in network resilience enhancement. Findings of this study would provide practical implications for the design and planning of interdependent infrastructure systems under disasters.

Measuring the economic and societal value of reliability/resilience investments: case studies of islanded communities

ABSTRACT Large-scale disasters have exposed vulnerabilities in energy systems and interdependent infrastructure, underscoring the importance of proactively mitigating risks to critical infrastructure. This paper focuses on strengthening power system planning by incorporating the costs associated with prolonged and extensive power interruptions to bolster resilience. To achieve this, duration-dependent customer damage functions are developed, capturing the direct, indirect, and societal impacts of power interruptions, ranging from short, localized events to widespread and long-duration ones. The research methodology is applied to three islanded communities facing substantial resilience challenges with limited data availability. Three customer interruption cost surveys are conducted with local electricity customers, yielding valuable insights into duration-dependent customer damage functions for direct, indirect, and monetizable societal costs. Significantly, the power interruption cost estimates derived from these functions vary considerably from those in the contiguous U.S. and even between the different islanded communities, reflecting their distinct hazard profiles and geographical characteristics. In conclusion, this study demonstrates the potential of duration-dependent customer damage functions to enhance power system resilience. It also identifies several areas for further research, paving the way for a more robust and resilient power infrastructure.

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.

An asynchronous simulation algorithm for post‐earthquake functional recovery of interdependent infrastructure systems

AbstractThe seismic resilience assessment of infrastructure systems not only requires evaluation of immediate damages after earthquakes, but also estimate of functional losses during the post‐earthquake recovery process. Infrastructure systems are interdependent during post‐earthquake recovery. To accurately consider this interdependence in resilience assessment, many studies have adopted post‐earthquake recovery simulation methods. In this study, an efficient asynchronous simulation algorithm is proposed, which does not require synchronous checking of the states of infrastructure elements at each time step, but rather asynchronously derives the functional recovery time of each element based on its dependency relationships. This study proves that the time complexity of the proposed asynchronous algorithm is proportional to the scale of the infrastructure system and is independent of the time step size. Furthermore, a case study of post‐earthquake recovery of a portfolio of buildings considering safety cordon areas is used to demonstrate that the asynchronous algorithm can significantly reduce the simulation time of large‐scale infrastructure systems compared to synchronous algorithms.