Infrastructure Resilience Assessment Research Articles

Correlated spatiotemporal downscaling of Euro‐CORDEX climatic data for infrastructure resilience assessment

AbstractA methodology is presented for downscaling the Euro‐CORDEX climatic projections in order to derive spatially and temporally correlated weather fields that can be used for risk and resilience assessment of large‐scale asset portfolios or interconnected infrastructure. The temporal resolution of the Euro‐CORDEX data is downscaled to a 10 min basis by employing a modified analogue‐type approach that utilizes the k‐NN algorithm along with measurements from weather stations. The aim is to generate composite “Frankenstein” days comprising 144 jigsaw pieces of observed 10 min timeseries that are scaled and/or shifted, and matched together to form a continuous daily record. These point‐estimates, valid only at the locations of the weather stations, are expanded spatially by employing high‐fidelity weather intensity measure fields that provide variable yet synchronous patterns of weather parameters at all locations of interest. As a case study, the Euro‐CORDEX projections for wind, temperature, and precipitation are downscaled for the Metsovo‐Panagia segment of Egnatia Odos highway in Greece, by employing high‐fidelity Computational Fluid Dynamic simulations that account for the topography of the site to simulate turbulent wind flows. These are combined with measurements of two local weather stations to generate the Frankenstein timeseries and corresponding weather fields that can be used for estimating operability, recovery and direct/indirect loss statistics on an event‐by‐event basis for an ensemble of interconnected highway assets.

Converged Security and Information Management System as a Tool for Smart City Infrastructure Resilience Assessment

Current research on smart cities is primarily focused on the area of applicability of information and communication technologies. However, in the context of a multidisciplinary approach, it is also necessary to pay attention to the resilience and converged security of individual infrastructures. Converged security represents a particular security type based on a selected spectrum of certain convergent security types of, assuming the creation of a complementary whole. Considering the outputs of the analysis of security breaches manifestations, this kind of security makes it possible to detect emerging security breaches earlier (still in the symptom stage), thus providing a more efficient and targeted solution suitable for building smart city infrastructure. In its essence, the article refers to the practical application of the converged security theoretical principles presented in the publication to a functional sample, deployed and tested in practical conditions in context of selected smart city infrastructure protection and resilience. Considering the nature of the practical application, the convergence of a wider spectrum of smart security alarm systems in the resilience assessment context is defined. In the beginning, the general principles of security/safety and the need for their convergence are presented. In this context, the mathematical model called Converged Resilience Assessment (CRA) method is presented for better understanding. Subsequently, Physical Security Information Management (PSIM) and Security Information and Event Management (SIEM) systems are described as a technological concept that can be used for resilience assessment. The most beneficial part is the structural, process, and functional description of the Converged Security and Information Management System (CSIM) using the concept of smart security alarm systems converged security.

Stormwater Infrastructure Resilience Assessment against Seismic Hazard Using Bayesian Belief Network.

Resilient stormwater infrastructure is one of the fundamental components of resilient and sustainable cities. For this, the resilience assessment of stormwater infrastructure against earthquake hazards is crucial for municipal authorities. The objective of this study is to develop a resilience assessment framework for stormwater pipe infrastructure against seismic hazards. A Bayesian belief network (BBN)-based stormwater infrastructure resilience model is constructed based on the published literature and expert knowledge. The developed framework is implemented in the city of Regina, Canada, to assess the city's stormwater pipe infrastructure resilience. The outcome of the model indicates that proposed BBN-based stormwater infrastructure resilience model can effectively quantify uncertainties and handle the nonlinear relationships between several reliability and recovery factors. The model is also capable of identifying the most sensitive and vulnerable stormwater pipes within the network.

Stormwater Green Infrastructure Resilience Assessment: A Social-Ecological Framework for Urban Stormwater Management

Urban areas are increasingly vulnerable to the effects of climate change. Stormwater Green infrastructure (SWGI) is seen as an approach to increase the climate resilience of urban areas, because they can buffer precipitation changes brought on by climate change. However, SWGI features themselves need to be resilient to climate change to be able to contribute to the resilience of cities. Thus, we aimed to develop a SWGI resilience assessment framework that could be used to identify challenges and to inform decisionmakers’ efforts to enhance resilience. We developed a resilience assessment framework based upon a resilience matrix approach to recognize effective resilience categories for SWGI by reviewing the literature on critical functionality and barriers to implementation and operation. These categories for SWGI included policy, design, maintenance, economic factors and social factors that influence SWGI functionality. We then identified specific aspects under each category that could be used for assessing SWGI resilience, recognizing that SWGI has critical functionalities and factors controlling its viability. Unlike other SWGI assessment frameworks that are focused on ecosystem services as a final outcome, we worked from a socio-ecological perspective in order to include socio-economic and policy factors and design and planning aspects that affect service provision. Developing a resilience assessment framework is critical for management because it can reveal the specific challenges facing SWGI resilience that have traditionally been overlooked, such as maintenance and social factors. This specific framework can also lead to efficient planning and management by identifying interrelations and hierarchical relationships of categories that influence resilience. Application of this framework will rely upon expert input to connect broad dimensions with specific indicators for SWGI to local priorities in resilience planning.

A Social Welfare–Based Infrastructure Resilience Assessment Framework: Toward Equitable Resilience for Infrastructure Development

Resilient infrastructure, which better withstands, adapts, and recovers from disasters, can limit disaster impacts, such as disruptions to infrastructure services and time and efforts needed for recovery. However, in the context of a disaster, the impacts on infrastructure are not evenly distributed across different communities. Thus, we need to account for such disparities (or inequalities) when assessing infrastructure resilience. To address this need, this paper proposes a social-welfare–based infrastructure resilience assessment (SW-Infra-RA) model for quantifying the collective resilience of infrastructure serving multiple communities. This model accounts for (1) disaster inequality—the unequal distributions of disaster impacts on infrastructure across different communities; and (2) disaster vulnerability—the disaster impacts on the infrastructure of communities that suffer from the most severe impacts—both of which have impacts on the collective resilience of infrastructure. A set of hypothetical and real case studies were conducted to illustrate the use of the proposed model in quantitatively assessing infrastructure resilience. This study contributes to the body of knowledge by providing a new infrastructure resilience assessment model that accounts for disaster inequality and vulnerability. The proposed model has the potential to support the development and investment of infrastructure in a more equitable manner; it facilitates equitable resilience in future infrastructure planning and development.

Bridge infrastructure resilience assessment against seismic hazard using Bayesian best worst method

The transportation agencies are experiencing numerous challenges in endorsing resilient bridge infrastructure against natural disasters such as earthquakes, floods, and hurricanes. Therefore, assessing bridge infrastructure resiliency against a seismic hazard is critical to improve the bridge's endurance and effective recovery. In this study, assessment of bridge infrastructure resilience against the seismic hazard is performed using Bayesian best–worst method (BWM). Based on a literature review, 15 resilience parameters were identified for bridge infrastructure resiliency against seismic hazard under two primary aspects, namely reliability and recovery. The Bayesian BWM was used to calculate the weight of the seismic resilience variables based on the experts’ judgment. Finally, a bridge resiliency index was developed to determine the weights of the parameters. To demonstrate the applicability of the proposed model, the infrastructure resiliency against the seismic hazard of a bridge located in Vancouver Island, British Columbia, Canada is assessed using two sets of data from two different sources. It is mentionable that the resiliency index was close for both of the data sets. The outcome of this study is that the model for determining bridge resiliency index will assist the bridge engineer and policymakers make effective decisions in the face of future seismic danger.

Resilience of Critical Infrastructure Systems: A Systematic Literature Review of Measurement Frameworks

Critical infrastructures such as transportation, power, telecommunication, water supply, and hospitals play a vital role in effectively managing post-disaster responses. The resilience of critical infrastructures should be incorporated in the planning and designing phase based on the risk assessment in a particular geographic area. However, the framework to assess critical infrastructure resilience (CIR) is variably conceptualised. Therefore, the objective of this study was to critically appraise the existing CIR assessment frameworks developed since the adoption of the Sendai Framework in 2015 with the hazard focus on earthquakes. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was used for the selection of the 24 most relevant studies, and these were analysed to delineate existing frameworks, models, and concepts. The study found that there are wide-ranging disparities among the existing frameworks to assess the infrastructure resilience, and it has become a key challenge to prioritise resilience-based investment in the infrastructure sector. Furthermore, key attributes such as performance indicators, emergency aspects, and damage assessment need to be considered for different disaster phases—ex-ante, during, and ex-post—to improve the long-term resilience of critical infrastructure. Subsequently, an integrated and adaptable infrastructure resilience assessment framework is proposed for proper critical infrastructure planning and resilience-based investment decision making.

Predictive resilience of interdependent water and transportation infrastructures: A sociotechnical approach

Infrastructures are interdependent systems and their interdependency can influence their resilience to routine failures and extreme events. Even though infrastructure resilience has been widely explored, few studies have considered physical, spatial, and social dimensions simultaneously. In this paper, we propose a resilience assessment framework for interdependent water and transportation infrastructures. The framework incorporates the physical network of these infrastructures, social vulnerability indicators, and predictive analytics for a sociotechnical resilience assessment. It enables us to measure the impact of random failures due to aging infrastructures, natural disasters, and their cascading failures. We applied the proposed framework to the City of Tampa, FL. The results indicated that areas with higher social vulnerability are more prone to cascading failures caused by both random breakdowns and natural disasters. While natural disasters affect all land use classes similarly, random failures have a greater impact on residential and institutional land use. The findings of this study highlight that infrastructure interdependency and the consequences of cascading failures should be taken into account in a coordinated infrastructure resilience assessment and planning. Further, socioeconomic factors and land use features should be incorporated in interdependent resilience assessment for a more comprehensive and equitable resilience planning.

Urban Infrastructure Resilience Assessment During Mega Sport Events Using a Multi-Criteria Approach

Critical infrastructures share the same space and mutual dependencies, and consequently contribute to city's overall resilience. However, each has different assessment methods and metrics, which prevents the integration of the performance results for different infrastructures into a single holistic evaluation index that can reflect the city's overall resilience and expected performance. To address this challenge, we propose the development of a multi-criteria-based integration approach that can allow the combination of different evaluation results from different assessment methods through partial factors. The proposed equation incorporates several factors, which can be divided into four groups: (1) method-efficiency factors, (2) infrastructure importance factors, (3) infrastructures' interdependency factors, and (4) strict limits that ensure that the infrastructures are suitable for hosting mega sports events. Using this approach, we hope to open the way for future research into suitable factors and facilitate resilience-based design in sustainable development and municipal planning.

Development of a civil infrastructure resilience assessment framework and its application to a nuclear power plant

Recent disruptive events, such as earthquakes or floods, have caused severe damage to civil infrastructure systems. Thus, there is a need to extend the focus of traditional design practices to include resilience-based design approaches which can help in defining preventive actions and measures to mitigate the consequences caused by such disruptive events. This paper presents a Civil Infrastructure Resilience Assessment Framework (CIRAF) to assess the seismic fragility and resilience of a single or multiple interconnected civil infrastructure systems following a disruptive event. Once the information regarding the infrastructure system, hazards, fragility functions, component's damage state correlation, recovery models, and upgrade models are identified, then the framework can be used to quantify the loss of functionality, recovery time, repair cost, and overall resilience using a Bayesian Network approach. A state-of-the-art engineering tool is also developed using the framework that would enable the stakeholders to compare different upgrade strategies through an easy to use web interface and thus easing the decision-making process. A case study of a hypothetical nuclear power generation system is used to assess the seismic fragility and resilience using the proposed framework.

Developing a multi-scale modeling system for resilience assessment of green-grey drainage infrastructures under climate change and sea level rise impact

Multi-scale modeling analysis is often required for comprehensive resilience assessment of urban drainage infrastructures to account for global climate change impact and local watershed response. The goal of this study was to develop a multi-scale modeling platform for drainage infrastructure resilience assessment in a coastal watershed. The model employs scale-dependent informatics, including hydroinformatics, climate informatics, and geoinformatics, to support a comprehensive hydrodynamic stormwater and hydrologic model, called the Interconnected Channel and Pond Routing Model. Low Impact Development (LID), deemed as green drainage infrastructure, was adopted and assessed in the Cross Bayou Watershed, Florida. The Cross Bayou Canal is the grey infrastructure, which dissects the watershed and connects both Tampa Bay and Boca Ciega Bay on its northeastern and southwestern ends, respectively. Modeling scenarios are driven by watershed-scale rainfall/runoff, coastal high tide, and global sea level rise, respectively or collectively, to evaluate the green-grey drainage infrastructure system in response to current and future coastal flood hazards predicted for year 2030. The quantitative resilience metrics, such as peak inflow reduction at flood zone, were chosen to reflect storms that pose threats to the watershed, now and in the future year 2030, for climate change scenarios derived by the Statistical Downscaling Model. Results indicate that the effectiveness of LID depends on the rainfall type being considered, such as convective storm versus frontal rain, and sub-daily rainfall patterns, as well as a groundwater table analysis.

Advancing Cyber Resilience Analysis with Performance-Based Metrics from Infrastructure Assessments

Cyber resilience is becoming increasingly recognized as a critical component of comprehensive cybersecurity practices. Current cyber resilience assessment approaches are primarily qualitative methods, making validation of their resilience analyses and enhancement recommendations difficult, if not impossible. The evolution of infrastructure resilience assessment methods has paralleled that of their cyber counterparts. However, the development of performance-based assessment methods has shown promise for overcoming the validation challenge for infrastructure systems. This paper describes a hybrid infrastructure resilience assessment approach that combines both qualitative analysis techniques with performance-based metrics. The qualitative component enables identification of system features that limit resilience, and the quantitative metrics can be used to evaluate and confirm the effectiveness of proposed mitigation options. The authors propose adaptation of this methodology for cyber resilience analysis. A case study is presented to demonstrate how the approach could be applied to a hypothetical system.

Assessing resilience in a regional road-based transportation network

Resilience deals with the response of the system in the face of shock and its ability to continue to provide the expected service delivery levels. In the transportation infrastructure, system shocks due to man-made and natural causes occur frequently and result in substantial economic losses; it is therefore crucial to enhance the resilience of this infrastructure. Improving the resilience of systems creates a need for developing metrics that measure the current resilience of the system and provides a benchmark for evaluating different strategies for improving resilience. In this paper, we propose a framework for assessing the resilience of a regional road network. The methodology introduced in this paper is the Networked Infrastructure Resilience Assessment (NIRA) framework, which allows decision-makers to assess the resilience of networked infrastructures from a multi-metric perspective. The resilience metrics measure the impact of disruptions on the system performance measures. The three identified metrics for road networks are the travel time resilience, environmental resilience and cost resilience. The resilience values are measured by introducing hypothetical disruptions to a network model of a regional transportation network. The NIRA framework is applied to the transportation corridor between Boston and New York City. We also investigate the impact of disruptions on the traveller’s mode choice.

Infrastructure resilience assessment through control design

Infrastructure resilience is a priority for homeland security in many nations around the globe. This paper describes a new approach for quantitatively assessing the resilience of critical infrastructure systems. The mathematics of optimal control design provides the theoretical foundation for this methodology. This foundation enables the inclusion of recovery costs within the resilience assessment approach, a unique capability for quantitative resilience assessment techniques. This paper describes the formulation of the optimal control problem for a set of representative infrastructure models. This example demonstrates the importance of recovery costs in quantitative resilience analysis, and the increased capability provided by this approach’s ability to discern between varying levels of resilience.