Resilience Of Infrastructure Systems Research Articles

Restoration resource allocation model for enhancing resilience of interdependent infrastructure systems

Enhancing the resilience of infrastructure systems is critical to the sustainability of the society against multiple disruptive events. This paper develops an approach for allocating restoration resources to enhance resilience of interdependent infrastructure systems. According to Inoperability Input–Output Model, a resilience metric for infrastructure systems is developed, in which the performance loss of infrastructure systems resulting from a disruptive event is measured in economic loss and inoperability. Model for determining the optimal infrastructure restoration resources allocation is proposed with the objective of maximizing resilience. Infrastructure interdependence is modeled by the Dynamic Inoperability Input-Output Model (DIIM), which is an accepted economic model for describing the interconnected relationship of industry sectors. To investigate the utility of the restoration resource allocation model, numerical analysis is conducted with an example derived from the data provided by the US Bureau of Economic Analysis. The results show that: (1) the optimal restoration resource allocation varies with the resource budget; (2) for a specific disruptive event, there exists an optimal resource budget which can minimize the sum of restoration cost and the performance loss of infrastructure system; and (3) the significance of factors such as initial inoperability of infrastructure systems on the optimal allocation. The proposed model can assist the decision makers in (i) better understand the effects of resource allocation, and (ii) deciding which allocation strategies should be used following a disruptive event.

A compositional demand/supply framework to quantify the resilience of civil infrastructure systems (Re-CoDeS)

Disaster resilience of civil infrastructure systems is essential to security and economic stability of communities. The novel compositional demand/supply resilience framework named Re-CoDeS (Resilience-Compositional Demand/Supply) generalizes the concept of disaster resilience across the spectrum of civil infrastructure systems by accounting not only for the ability of the civil infrastructure system to supply its service to the community, but also for the community demand for such service in the aftermath of a disaster. A Lack of Resilience is consequently observed when the demand for service cannot be fully supplied. Normalized resilience measures are proposed to allow for direct comparisons between different civil infrastructure systems at component and system levels. A scheme is introduced to classify component and system configurations with respect to their resilience. In addition to quantify the resilience of civil infrastructure systems, Re-CoDeS can be used to evaluate or design community risk mitigation strategies and to optimize post-disaster recovery.

A mathematical framework to optimize resilience of interdependent critical infrastructure systems under spatially localized attacks

This paper defines the hazards that can cause direct localized damage or interruption as Spatially Localized Attacks (SLAs). The SLAs-induced impact on a critical infrastructure system (CIS) is modeled as the failure of system components that exist within some localized area while those outside this area remain operating. Instead of identifying and analyzing each type of the SLAs-related hazards, this paper makes a worst-case analysis and proposes a mathematical framework to support resilience optimization of interdependent CISs under the worst SLA. For illustrative purposes, this paper mainly considers two types of strategies to enhance CIS resilience, including protecting weak components, and building new components to increase redundancy. The problem is mathematically formulated as a tri-level defender–attacker–defender model, which is exactly solved by a proposed decomposition algorithm. The case study on interdependent power and water systems demonstrates how the proposed approach can not only identify the optimum resilience enhancement strategy as well as the worst-case SLA, but also analyze the importance of considering interdependencies from both the attacker's and the defender's perspectives.

Resilience of Infrastructure Systems to Sea-Level Rise in Coastal Areas: Impacts, Adaptation Measures, and Implementation Challenges

Expansive areas of low elevation in many densely populated coastal areas are at elevated risk of storm surges and flooding due to torrential precipitation, as a result of sea level rise. These phenomena could have catastrophic impacts on coastal communities and result in the destruction of critical infrastructure, disruption of economic activities and salt water contamination of the water supply. The objective of the study presented in this paper was to identify various impacts of sea level rise on civil infrastructures in coastal areas and examine the adaptation measures suggested in the existing literature. To this end, a systemic review of the existing literature was conducted in order to identify a repository of studies addressing sea level rise impacts and adaptation measures in the context of infrastructure systems. The study focused on three infrastructure sectors: water and wastewater, energy, and road transportation. The collected information was then analyzed in order to identify different categories of sea level rise impacts and corresponding adaptation measures. The findings of the study are threefold: (1) the major categories of sea level rise impacts on different infrastructure systems; (2) measures for protection, accommodation, and retreat in response to sea level rise impacts; and (3) challenges related to implementing adaptation measures.

Resilience in social insect infrastructure systems.

Both human and insect societies depend on complex and highly coordinated infrastructure systems, such as communication networks, supply chains and transportation networks. Like human-designed infrastructure systems, those of social insects are regularly subject to disruptions such as natural disasters, blockages or breaks in the transportation network, fluctuations in supply and/or demand, outbreaks of disease and loss of individuals. Unlike human-designed systems, there is no deliberate planning or centralized control system; rather, individual insects make simple decisions based on local information. How do these highly decentralized, leaderless systems deal with disruption? What factors make a social insect system resilient, and which factors lead to its collapse? In this review, we bring together literature on resilience in three key social insect infrastructure systems: transportation networks, supply chains and communication networks. We describe how systems differentially invest in three pathways to resilience: resistance, redirection or reconstruction. We suggest that investment in particular resistance pathways is related to the severity and frequency of disturbance. In the final section, we lay out a prospectus for future research. Human infrastructure networks are rapidly becoming decentralized and interconnected; indeed, more like social insect infrastructures. Human infrastructure management might therefore learn from social insect researchers, who can in turn make use of the mature analytical and simulation tools developed for the study of human infrastructure resilience.

Interdependence and dynamics of essential services in an extensive risk context: a case study in Montserrat, West Indies

Abstract. The essential services that support urban living are complex and interdependent, and their disruption in disasters directly affects society. Yet there are few empirical studies to inform our understanding of the vulnerabilities and resilience of complex infrastructure systems in disasters. This research takes a systems thinking approach to explore the dynamic behaviour of a network of essential services, in the presence and absence of volcanic ashfall hazards in Montserrat, West Indies. Adopting a case study methodology and qualitative methods to gather empirical data, we centre the study on the healthcare system and its interconnected network of essential services. We identify different types of relationship between sectors and develop a new interdependence classification system for analysis. Relationships are further categorised by hazard conditions, for use in extensive risk contexts. During heightened volcanic activity, relationships between systems transform in both number and type: connections increase across the network by 41%, and adapt to increase cooperation and information sharing. Interconnections add capacities to the network, increasing the resilience of prioritised sectors. This in-depth and context-specific approach provides a new methodology for studying the dynamics of infrastructure interdependence in an extensive risk context, and can be adapted for use in other hazard contexts.

Computation of bridge seismic fragility by large‐scale simulation for probabilistic resilience analysis

SummarySeismic resilience of structures and infrastructure systems is a fast developing concept in the field of disaster management, promoting communities that are resistant and quickly recoverable in case of an extreme event. In this contest, probabilistic seismic demand and fragility analyses are two key elements of the seismic resilience assessment in the majority of the proposed methodologies. Several techniques are available to calculate fragility curves for different types of structures. In particular, to assess the seismic performance of the regional transportation infrastructure, methods for the fragility curve estimation for entire classes of bridges are required. These methods usually rely on a set of assumptions, partially because of the limited information. Other assumptions were introduced at the time when computational resources were inadequate for a purely numerical approach and closed‐form solutions were a convenient alternative. For instance, some of these popular assumptions are aimed at simplifying the model of the engineering demand. In this paper, a simulation‐based methodology is proposed, to take advantage of the computational resources widely available today and avoid such assumptions on the demand. The resulting increase in accuracy is estimated on a typical class of bridges (multi‐span simply supported). Most importantly, the quantitative impact of the assumptions is assessed in the context of a life‐cycle loss estimation analysis and resilience analysis. The results show that some assumptions preserve an acceptable level of accuracy, but others introduce a considerable error in the fragility curves and, in turn, in the expected resilience and life‐cycle losses of the structure. Copyright © 2015 John Wiley & Sons, Ltd.

Real-time remote monitoring: the DuraMote platform and experiments towards future, advanced, large-scale SCADA systems

The economic and social prosperity of our society depends much on the proper functioning of structures and civil infrastructure systems. Structural health monitoring has been long recognised as a vital tool to preclude and/or mitigate degradation effects and failures of structural systems. Along this line, the DuraMote platform is presented in this paper (named after Durable and Mote) together with real-life applications, laboratory and field experiments, which promote the effort to expand the existing concept of structural monitoring into remote, real-time, continuous and permanent performance monitoring of spatially extended systems. Successful implementation of this technology can improve the resilience and sustainability of large-scale complex infrastructure systems and lead to future, advanced Supervisory Control and Data Acquisition methodology that could be routinely used in a variety of structures and networks.

Inkjet-printed, flexible, and photoactive thin film strain sensors

In an effort to improve the resilience and reliability of infrastructure systems, many advanced sensors have been developed for structural health monitoring. However, these sensors often suffer from high energy demand, large form factors, and have difficulties conforming to complex structural surfaces. Recently, a self-sensing poly(3-hexylthiophene)–based thin film sensor was proposed, and it was shown that light illumination generates an electrical current that was also proportional to applied strains. However, its strain sensing range was limited largely due to the brittleness of the electrodes. In this study, inkjet printing was employed for printing flexible polymer electrodes that enhanced fabrication scalability and its strain sensing range. First, a commercial off-the-shelf Canon inkjet printer was customized for electrode fabrication. Second, a conductive ink solution was prepared for printing the conductive and transparent thin film electrodes. Then, the printed thin film electrodes were characterized for their electrical conductivity, optical transmittance, profile (i.e. roughness and thickness), and piezoresistivity. Finally, poly(3-hexylthiophene)–based strain sensors were fabricated using these electrodes, and strain sensing was validated to 2% tensile strains.

Simulation-based characterisation of critical infrastructure system resilience

This paper presents a simulation-based approach to the resilience analysis of critical infrastructures systems, making possible the characterisation of the structural resilience features of the system and estimation of the benefits that are derived from improved resilience practices, i.e., preparedness or responsiveness, due to enhanced information-sharing processes among the actors. Resilience characterisation is composed of assessment of the node vitality [i.e., the missed service demand (MSD) generated at the system level] and agility (i.e., the sensitivity of system performance to an improved response time in the node). Scenario analysis is subsequently used to simulate different strategies intended to minimise the impacts of large events. The transportation system in the metropolitan area of Milan is used for this purpose; more specifically, a reduction of disruption response times in agile nodes was simulated according to a real snowfall scenario.

Toward Disaster‐Resilient Cities: Characterizing Resilience of Infrastructure Systems with Expert Judgments

Resilient infrastructure systems are essential for cities to withstand and rapidly recover from natural and human-induced disasters, yet electric power, transportation, and other infrastructures are highly vulnerable and interdependent. New approaches for characterizing the resilience of sets of infrastructure systems are urgently needed, at community and regional scales. This article develops a practical approach for analysts to characterize a community's infrastructure vulnerability and resilience in disasters. It addresses key challenges of incomplete incentives, partial information, and few opportunities for learning. The approach is demonstrated for Metro Vancouver, Canada, in the context of earthquake and flood risk. The methodological approach is practical and focuses on potential disruptions to infrastructure services. In spirit, it resembles probability elicitation with multiple experts; however, it elicits disruption and recovery over time, rather than uncertainties regarding system function at a given point in time. It develops information on regional infrastructure risk and engages infrastructure organizations in the process. Information sharing, iteration, and learning among the participants provide the basis for more informed estimates of infrastructure system robustness and recovery that incorporate the potential for interdependent failures after an extreme event. Results demonstrate the vital importance of cross-sectoral communication to develop shared understanding of regional infrastructure disruption in disasters. For Vancouver, specific results indicate that in a hypothetical M7.3 earthquake, virtually all infrastructures would suffer severe disruption of service in the immediate aftermath, with many experiencing moderate disruption two weeks afterward. Electric power, land transportation, and telecommunications are identified as core infrastructure sectors.

Network theory for infrastructure systems modelling

Currently, there is a great deal of interest in assessing the resilience of infrastructure systems. Much of this interest stems from the realisation that these systems are not only critical to civil defence but also, given the correct set of circumstances, can fail catastrophically. Three case studies are presented that show how network theory, which has been successfully applied to other fields, can also be used to help understand potential vulnerabilities in infrastructure systems. Through these case studies it is shown that traditional network theory can be extended to analyse infrastructures that are large, spatially distributed systems, or that carry flows of resources or are interconnected with other infrastructure systems. These methods demonstrate how this approach can help infrastructure designers, owners and operators to make rapid assessments of vulnerabilities in their systems and to identify components that are more important to the functioning of the these networks. Furthermore, this approach provides a basis for identifying and prioritising appropriate measures to improve the reliability of infrastructure at the systems scale.

Ex-Ante Policy Analysis in Civil Infrastructure Systems

According to the National Academy of Engineering, innovations such as intelligent transportation systems, alternative fuels, and smart grids are critical to enhancing the resilience and sustainability of infrastructure systems. One key to realizing infrastructure innovations is effective policymaking based on a comprehensive analysis of the system. However, policy analysis in infrastructure systems is difficult because of the existence of complex adaptive behaviors and uncertainties. The objective of this paper is to create an ex-ante analysis framework using Agent-Based simulation to facilitate incorporation of uncertainties and complex adaptive behaviors of stakeholders in the analysis of infrastructure policies. The proposed framework includes three phases: definition, abstraction, and implementation. First, the steps in each phase will be discussed. Then, the application of the framework is demonstrated for the assessment of financial innovation policies for U.S. transportation infrastructure. Using hybrid agent-based/system dynamics techniques, a computational model is created to simulate the micro behaviors of state departments of transportation, private institutional investors, and the public. The results of the model include the visualization of the outcomes of different policies and the identification of the desired policy landscapes. The proposed framework provides policymakers with an integrated methodology through which an infrastructure policy problem is formulated, micro behaviors of the agents are modeled, the policy landscape is created, and the desired scenarios are identified.

Optimization of Resilient Biofuel Infrastructure Systems under Natural Hazards

AbstractThe writers aim to develop a new optimization framework that integrates resilience in biofuel infrastructure systems planning, which provides a proactive solution in mitigating the impacts of natural hazards on infrastructure systems. A novel, multiobjective stochastic mixed-integer programming model is developed to establish a biofuel infrastructure system that is effective and resilient in hedging against potential hazards through integrating four dimensions of resilience, as follows: (1) robustness, (2) rapidity, (3) redundancy, and (4) resourcefulness. As a case study, the model is used to evaluate the infrastructure requirements for cellulosic ethanol production from biomass wastes in California, which is prone to seismic hazards. An average of 19% overall system cost reduction can be achieved under seismic hazards by using the developed model over common engineering methods without resilience consideration.

Comparative Evaluation of Resilience Quantification Methods for Infrastructure Systems

Resilience engineering has become an important field representing a new way of understanding and management of safety. The rising interest for resilience engineering and the rapid application of the respective theoretical basis to several application cases alters the traditional approach of infrastructure systems’ risk management. In this context, the quantification of resilience is a challenging issue, which is still far from considered as adequately addressed in the respective literature and, therefore, requires a better insight. The aim of this research is to provide such an insight into currently established and proposed methods for quantifying resilience of infrastructure systems and suggest a methodological framework that effectively responds to the requirements of resilience measuring. To achieve this aim, resilience engineering is shortly presented and discussed, in terms of definition and characteristics and an insight is provided into various resilience quantification methods, such as probabilistic, graph theory, fuzzy inference, and analytical methods. Discussion over these methods reveals their strengths and weaknesses in quantifying resilience. A major finding of this research is that current methods are, mostly, incomplete and largely dependent on concepts and approaches, which emanate from other well-established and well-elaborated methodological frameworks, thus failing to provide solutions in the context of resilience engineering. On the other hand, it is proposed that entropy theory constitutes a framework, which better captures the underlying interrelations of systems modules and, therefore, constitutes a more appropriate and effective framework for quantifying resilience of infrastructure systems.

Resilience of civil infrastructure systems: literature review for improved asset management

Infrastructure resilience has drawn significant attention in recent years, partly because the occurrence of low-probability and high-consequence disruptive events like Hurricane Katrina, the Indonesian tsunami, terrorism attack in New York, and others. Since civil infrastructure systems support society welfare and viability, continuous infrastructural operation is critical. Along protection approaches, resilience concepts support achievement of near-continuous infrastructure operation. A variety of frameworks, models, and tools exist for advancing infrastructure resilience research. Nevertheless, translation of resilience concepts into practical methodologies for informing civil infrastructure operation and management remains challenging. This paper presents a state-of-the-art literature review on civil infrastructure resilience, particularly water distribution systems, enabling practical applications of infrastructure resilience towards improved system management. The literature review has two stages, quantitative and qualitative. Infrastructure resilience is defined to provide a foundation for operationalisation of infrastructure resilience concepts, enabling the inclusion of practical resilience considerations in formal management systems such as infrastructure asset management systems.

Time-dependent resilience assessment and improvement of urban infrastructure systems

This paper introduces an approach to assess and improve the time-dependent resilience of urban infrastructure systems, where resilience is defined as the systems' ability to resist various possible hazards, absorb the initial damage from hazards, and recover to normal operation one or multiple times during a time period T. For different values of T and its position relative to current time, there are three forms of resilience: previous resilience, current potential resilience, and future potential resilience. This paper mainly discusses the third form that takes into account the systems' future evolving processes. Taking the power transmission grid in Harris County, Texas, USA as an example, the time-dependent features of resilience and the effectiveness of some resilience-inspired strategies, including enhancement of situational awareness, management of consumer demand, and integration of distributed generators, are all simulated and discussed. Results show a nonlinear nature of resilience as a function of T, which may exhibit a transition from an increasing function to a decreasing function at either a threshold of post-blackout improvement rate, a threshold of load profile with consumer demand management, or a threshold number of integrated distributed generators. These results are further confirmed by studying a typical benchmark system such as the IEEE RTS-96. Such common trends indicate that some resilience strategies may enhance infrastructure system resilience in the short term, but if not managed well, they may compromise practical utility system resilience in the long run.

Resistance and resilience – paradigms for critical local infrastructure

‘Critical infrastructure’ generally refers to significant pieces of plant and equipment, such as power stations and motorways. High population densities in cities, and the increasing interconnectedness of the services and supply chains that sustain them, mean local infrastructure is equally important. Local infrastructure must be able to cope with system shocks, whether from natural hazards, terrorism or catastrophic failures. Engineering design plays a major part in achieving this, but shocks will occur that overwhelm even the most conservative design. Local infrastructure must therefore be able to adapt to, and recover from, shocks: it must be resilient. Local infrastructure has evolved with little consideration for resilience of the interconnected system as a whole, whereas resilience has been the subject of much research in many other systems. This paper explores the lessons that local infrastructure can learn from such research by reviewing literature related to resilience of ecological, economic, physical infrastructure, community/social and government systems. A critical analysis highlights the factors affecting resilience and different approaches that need to be taken into account when attempting to model infrastructure at a local scale. The ultimate aim is to provide an evidence base on which to build resilience into various infrastructures and direct future local infrastructure resilience research in an age of austerity.

Toward a Comparative Framework for Measuring Resilience in Critical Infrastructure Systems

A major problem in building community resilience in the face of disasters is understanding resilience as an organizational property. A fundamental problem in this understanding is the imbalance in disaster research between the large number of variables and the relatively few cases involved. While resilience is a complex process with many variables at work in determining outcomes, disasters afford far fewer cases with which to compare and understand the multitude of these variables. This is especially true when disasters have a great deal of variety and seldom occur in the same places over the same set of community and organizational variables. Under these conditions it continues to be difficult to isolate causal, much less predictive, factors in resilience. This research has found that far more events challenge resilience in communities and organizations than might be assumed, were one looking only at disasters. In fact organizations can signal their readiness for resilience through measurable responses to far smaller-scale performance challenges. This paper presents a strategy for developing resilience indicators in critical infrastructures that would allow comparative assessment of risks to resilience across infrastructures in different sectors and communities. The strategy is illustrated in resilience indicators developed within one infrastructure, a large and complex high-voltage electrical grid. This strategy is extended and its potential analytic application to additional infrastructures and to the assessment of compound inter-infrastructure reliability is discussed.

Empirical Data and Regression Analysis for Estimation of Infrastructure Resilience with Application to Electric Power Outages

Recent natural disasters have highlighted the need for increased planning for disruptive events. Forecasting damage and time that a system will be inoperable is important for disruption planning. The resilience of critical infrastructure systems, or their ability to recover quickly from a disruption, can mitigate adverse consequences of the disruption. This paper quantifies the resilience of a critical infrastructure sector through the dynamic inoperability input-output model (DIIM). The DIIM, which describes how inoperability propagates through a set of interdependent industry and infrastructure sectors following a disruptive event, includes a resilience parameter that has not yet been adequately assessed. This paper provides a data-driven approach to derive the resilience parameter through regression models. Data may contain different disruption scenarios, and regression models can incorporate these scenarios through the use of categorical or dummy variables. A mixed-effects model offers an alternate approach of accounting for these scenarios, and these models estimate parameters based on the combination of all scenarios (fixed effects) and an individual scenario (random effects). These regression models are illustrated with electric power outage data and a regional disruption that uses the DIIM to model production losses in Oklahoma following an electric power outage.