Interdependent Infrastructure Research Articles

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

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.

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.

Multi-agent deep reinforcement learning based decision support model for resilient community post-hazard recovery

After a city-scale natural hazard, policymakers should plan sound decisions on the repair sequence to ensure the resilient recovery of the community, which consists of interdependent infrastructures. Stochastic scheduling for repairing interdependent infrastructure systems is a difficult control problem with huge decision spaces. This study proposes a novel decision support model to determine the optimal restoration policies for the purpose of maximizing disaster resilience. A simulation environment is first developed, consisting of hazard intensity assessment, components damage evaluation, system recovery simulation, and resilience quantification. The graph theory is utilized to represent the interdependencies among different systems, and the heterogeneous graph neural network is integrated into this framework to extract the topology and interdependency information of the whole community. The optimal repair policies approximated by neural networks are trained by a multi-agent deep reinforcement learning algorithm, considering uncertainties of the restoration process. The superiority and efficiency of the proposed method are demonstrated through a case study of the Tsinghua University campus, where different decision-making objectives are considered. The results show that the recovery trajectories determined by the proposed model have the highest performance compared to conventional methods. Besides, the proposed methodology based on transfer learning can achieve high computational efficiency for new damage scenarios. This model is promising to be a high-performance, robust decision-support tool for post-hazard repairing decisions.

Protection-interdiction-restoration for resilient multi-commodity networks

Interdependent critical infrastructure systems represent substantial financial investments and are vital to maintain a fundamental level of social and economic well-being, making them attractive targets for malevolent actors. Many of these infrastructure systems carry multiple products, each with unique needs and importance to different stakeholders. This work extends a tri-level protection-interdiction-restoration model from a single commodity to multiple commodities, solving the model with an integration of Benders’ decomposition and set covering decomposition. We propose a method to limit unmet weighted demand across commodities, considering unique interdependencies between network components and commodity-specific capacity requirements. An optimal solution is found iteratively by alternately fixing protection and interdiction variables. This work is illustrated with a case study of interdependent Swedish power and multi-commodity railway systems. Results demonstrate the convergent behavior of the master and subproblems, the value of network hardening, and the non-uniform network recovery trajectory. For the case study, the railway system was more vulnerable to disruption than the power system, making it a more attractive target for both protection and interdiction. The components that were commonly selected as part of a protection or interdiction plan were generally the ones with the greatest demand for iron ore, a highly critical commodity.

Cyber–Physical–Social Model of Community Resilience by Considering Critical Infrastructure Interdependencies

Each year, several disasters occur, resulting in enormous human, infrastructural, and economic losses. To minimize losses and ensure an adequate emergency response, it is vital to prepare the community for greater shock absorption and recovery after an occurrence. This raises the concept of community resilience and also demands appropriate metrics and prediction models for improved preparedness and adaptability. While a community is impacted in three main ways during a disaster-namely social, physical, and cyber-there are currently no tools to model their interrelationship. Thus, this paper presents a multi-agent cyber-physical-social model of community resilience, taking into account the interconnection of power systems, emergency services, social communities, and cyberspace. To validate the model, we used data on two hurricanes (Irma and Harvey) collected from Twitter, GoogleTrends, FEMA, power utilities, CNN, and Snopes (a fact-checking organization). We also describe methods for quantifying social metrics such as the level of anxiety, risk perception, and cooperation using social sensing, natural language processing, and text mining tools. We examine the suggested paradigm through three different case studies: 1) hurricanes Irma and Harvey; 2) a group of nine agents; and 3) a society comprised of six distinct communities. According to the results, cooperation can positively change individual behavior. Relationships within a community are so crucial that a smaller population with greater empathy may be more resilient. Similar dynamic changes in social characteristics occur when two empathetic communities share resources after a disaster.

A reference architecture for the American Multi-Modal Energy System enterprise

The American Multimodal Energy System (AMES) is a system-of-systems comprised of four separate but interdependent infrastructure enterprises: the electric grid, the natural gas enterprise, the oil enterprise, and the coal enterprise. Their interdependence creates the need to better understand the underlying architecture in order to move towards a more sustainable, resilient and accessible energy system. Collectively, these requirements necessitate a sustainable energy transition that constitute a change in the AMES instantiated architecture; although it leaves its reference architecture largely unchanged. Consequently, from a model-based systems engineering perspective, identifying the underlying enterprise model’s reference architecture becomes a high priority. This paper defines a reference architecture for the AMES and its four component energy enterprises in a single SysML model. The architecture includes (allocated) block definition and activity diagrams for each enterprise. The reference architecture was developed from the S&P Global Platts (GIS) Map Data Pro data set and the EIA Annual Energy Outlook dataset. This reference architecture serves as the foundation from which to accurately and consistently create mathematical and informatic digital twins of the AMES.

Managing infrastructure resilience and adaptation

ABSTRACT The paper contributes to strategic thinking about infrastructure resilience and adaptation. A technique is presented in a case study for analysing options on the timing of protection against climate change. Predetermined trigger points for responses to resilience weaknesses are discussed, showing how flowcharts can act as prompts to the need for action. The roles of national governments in improving resilience in key areas are discussed. The paper introduces the concept of a ‘chain of resilience’ for interdependent infrastructure systems and identifies some key questions for national governments to ask about the linkages between systems before requiring appropriate remedial actions. Practical steps for private owners and investors to increase resilience in their own infrastructure are identified. Some suggestions for tackling the increasingly important area of cyber resilience are presented. The use of actuarial techniques is discussed.

Optimal rehabilitation planning for aged water distribution mains considering cascading failures of interdependent infrastructure systems

Abstract Water distribution networks (WDNs) with other infrastructures constitute a complex and interdependent multi-utility system. Considering interdependencies between WDNs and other urban infrastructures, this work proposes WDN intervention planning using a dynamic multi-utility approach to tackle the challenges of pressure deficits and cascading failures by the decoupling of different infrastructure systems. For this purpose, the study develops reliability indices representing the hydraulic and decoupled statuses of WDNs with neighbor infrastructures; the hydraulic reliability represents the robustness of the network against the water pressure deficit, and decoupling reliability represents the extent to which WDN elements are decoupled from other assets elements. A multi-objective optimization algorithm is employed to develop rehabilitation strategies by introducing three approaches for WDN upgrade following a phased design and construction method. Evaluating intervention plans based on construction cost, reliability and cascade effects shows that, under budget limitation conditions, decoupling a WDN could significantly save the cascade cost such that 1% improvement in the decoupling reliability brings about 157.42 billion Rials cascade cost saving to asset managers. On the other hand, the decoupled network is weak against hydraulic reliability, which could make it by far less resilient network than the coupled network with around 75% hydraulic reliability difference.