An adaptive robust framework for the optimization of the resilience of interdependent infrastructures under natural hazards

Modeling cascading failure of interdependent critical infrastructure systems using HLA-based co-simulation

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

In the recent years, Sri Lanka’s focus on its infrastructure has grown due to its significance to the country’s economy, security, and quality of life. A resilient critical infrastructure system is crucial in reducing the impact of natural and human induced risks and weaknesses. In this context, comprehensive knowledge of a nation’s legal and policy framework would be of great assistance in building pathways towards strengthening the resilience of critical infrastructure systems. Concerning the need, this study aims to assess the ability of the existing legal and policy framework for complex, interdependent infrastructure systems in Sri Lanka to protect its energy and telecommunication infrastructure against natural and human-induced hazards. The objectives of the study include: (1) determining the existing legal and policy framework for energy, telecommunication infrastructure in Sri Lanka; and (2) comparing the legal and policy provisions for protecting these infrastructures against hazards in Sri Lanka with the international context. The study involved a comprehensive literature synthesis to understand the scope of critical infrastructure in the global context. Further, preliminary interviews were conducted to obtain the direction for the identification of the existing legal and policy framework related to the infrastructure sectors in Sri Lanka. Finally, the study examined the available provisions in the framework, alongside a desk study, to assess their effectiveness in safeguarding critical infrastructure. A comparison between Sri Lanka and the international context highlighted significant gaps in the legal and policy framework, particularly in terms of protecting the nation's infrastructure.

<p><span>In flood risk analysis it is a key principle to predetermine consequences of flooding to assets, people and infrastructures. Damages to critical infrastructures are not restricted to the flooded area. The effects of directly affected objects cascades to other infrastructures, which are not directly affected by a flood. Modelling critical infrastructure networks is one possible answer to the question ‘how to include indirect and direct impacts to critical infrastructures?’.</span></p><p>Critical infrastructures are connected in very complex networks. The modelling of those networks has been a basis for different purposes (Ouyang, 2014). Thus, it is a challenge to determine the right method to model a critical infrastructure network. For this example, a network-based and topology-based method will be applied (Pant et al., 2018). The basic model elements are points, connectors and polygons which are utilized to resemble the critical infrastructure network characteristics.</p><p>The objective of this model is to complement the state-of-the-art flood risk analysis with a quantitative analysis of critical infrastructure damages and disruptions for people and infrastructures. These results deliver an extended basis to differentiate the flood risk assessment and to derive measures for flood risk mitigation strategies. From a technical point of view, a critical infrastructure damage analysis will be integrated into the tool ProMaIDes (Bachmann, 2020), a free software for a risk-based evaluation of flood risk mitigation measures.</p><p>The data on critical infrastructure cascades and their potential linkages is scars but necessary for an actionable modelling. The CIrcle method from Deltares delivers a method for a workshop that has proven to deliver applicable datasets for identifying and connecting infrastructures on basis of cascading effects (de Bruijn et al., 2019). The data gained from CIrcle workshops will be one compound for the critical infrastructure network model.</p><p>Acknowledgment: This work is part of the BMBF-IKARIM funded project PARADes (Participatory assessment of flood related disaster prevention and development of an adapted coping system in Ghana).</p><p>Bachmann, D. (2020). ProMaIDeS - Knowledge Base. https://promaides.myjetbrains.com</p><p>de Bruijn, K. M., Maran, C., Zygnerski, M., Jurado, J., Burzel, A., Jeuken, C., & Obeysekera, J. (2019). Flood resilience of critical infrastructure: Approach and method applied to Fort Lauderdale, Florida. Water (Switzerland), 11(3). https://doi.org/10.3390/w11030517</p><p>Ouyang, M. (2014). Review on modeling and simulation of interdependent critical infrastructure systems. Reliability Engineering and System Safety, 121, 43–60. https://doi.org/10.1016/j.ress.2013.06.040</p><p>Pant, R., Thacker, S., Hall, J. W., Alderson, D., & Barr, S. (2018). Critical infrastructure impact assessment due to flood exposure. Journal of Flood Risk Management, 11(1), 22–33. https://doi.org/10.1111/jfr3.12288</p>

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

Machine learning applications in the resilience of interdependent critical infrastructure systems—A systematic literature review

Assessing the impact of systemic heterogeneity on failure propagation across interdependent critical infrastructure systems

The advancement in information and communication technologies and the integration with electric power grids, has made the later more pervasive, extensive, and in some cases complex in terms of design, structure, operations, and management. This complexity-induced convergence means the disruptions in one part of the system cascade to other areas, causing secondary, tertiary, and even higher-order destructive effects. Increasing complexity also means an increase in both systems' vulnerabilities and threats exposure. In most countries, various control measures are being implemented by both security engineers and regulatory bodies; aiming to intensify security requirements as well as compliance. This security objective is to ensure that critical infrastructure systems are not only protected but are also effective and resilient at all times. From the perspective of network theory, the paper proposes an infrastructure interdependence reliability metric; as a technique to assess the functional and structural impact of a systematic cyberattack on system (critical infrastructure) and its interdependent systems. The metric computes the infrastructure interdependence effectiveness index and resilience ratio among interdependent infrastructure systems. The reliability approach provides a perspective in understanding how systems convergence impact systems’ overall functionality, performance, and resilience. For researchers, the study presents a new approach that advances existing discussion on systems convergence in a heterogeneous environment such as IoT.

This paper contextualizes the nature of threats to critical infrastructure, especially vulnerabilities within electric grid systems, and analyzes key considerations for the protection architecture of such systems. By exploring historical case studies, we demonstrate the potential for blind spots in infrastructure protection policy, which can leave electric grids vulnerable to a variety of threats, including improvisational malignant devices. These devices in turn have the potential to catalyze cascading failure scenarios within interdependent critical infrastructure systems, constituting “wicked problems” of complexity that bear relevance to a variety of public and private institutions responsible for the provision of essential services.

The critical infrastructures protection landscape is a vast and varied pattern of independent, but interconnected infrastructure systems that are essential to the function of our modern society. The U.S. policy on critical infrastructure protection has been continually evolving since the “President's Commission on Critical Infrastructure Protection” was published in 1997. In response to these policies, federal, state, and local governments, along with research institutions, have invested a substantial amount of time and effort into identifying and analyzing critical infrastructure, their functions, and dependencies/interdependencies to better understand their vulnerabilities. To date, the ability to assess vulnerabilities, resiliency, and priorities for protecting interdependent critical infrastructure systems from an all‐hazards perspective remains a difficult problem. In this paper we introduce the All‐Hazards Analysis (AHA) methodology, which provides an integrated functional basis across infrastructure systems, through the implementation of a common language and a scalable level of decomposition to effectively evaluate the resilience of interconnected infrastructure systems. AHA models infrastructure systems as directed multidimensional graphs, which enable the evaluation of cross‐sector interdependencies prior to, during, and after disruptive events. Finally, and by design, AHA enables the cross linking of data taxonomies to enable more effective data sharing, such as the National Critical Functions (NCF) and Infrastructure Data Taxonomy (IDT).

<p>Critical infrastructure (CI) is fundamental for the functioning of a society and forms the backbone for socio-economic development. Natural hazards, however, pose a major threat to CI. The destruction of CI, and the disruption of essential services they provide may hamper societies and economies. Moreover, the overall risk for CI is expected to rise. This is due climate change (i.e. intensification and more frequent hazards), and socio-economic development (i.e. increase in the amount and value of CI).</p><p>Building sustainable and resilient infrastructure is a key to reducing the impacts of natural hazards and climate change on society. However, an in-depth knowledge of the global CI that is directly at risk for natural hazards is still lacking. The development of a harmonized dataset integrating the geospatial locations of the main CI systems at a global scale will aid to our knowledge on the CI that is exposed and at risk for natural hazards.</p><p>We present a first-of-its-kind globally consistent spatial dataset for the representation of CI. In this study, an index to express the spatial intensity of CI at the global scale is developed: the Critical Infrastructure System Index (CISI). The CISI is expressed in a dimensionless value ranging between 0 (being no CI intensity) and 1 (being highest CI intensity). The CISI aggregates high resolution spatial information of CI based on OpenStreetMap (OSM) data. For the development of this index, a total of 34 CI types (e.g. primary roads, waste-water plants and hospitals) are defined and categorized under seven overarching CI systems: transportation, energy, tele-communication, waste, water, health and education. Spatial data on these CI types are extracted by using a selection of 78 OSM tags. The detailed spatial data is rasterized into a harmonized and consistent dataset with a resolution of 0.1x0.1 degrees.</p><p>This novel global dataset will be a valuable starting point for policy makers, planners, and researchers in several fields. The dataset can be deployed as a tool to gain insights in the current landscape of the CI network, to identify hotspots of CI, and to gain exposure information for risk assessments. We use open data hosted by OSM, and provide code for further use and development. In this study, we demonstrate the database and CISI at a global scale, but the publicly accessible code can also be used to further develop the dataset with latest releases of data on CI provided by OSM as well as other (open) sources for any location and any resolution.</p>

A significant increase in catastrophic events worldwide has had a disastrous impact on the built environment, as these disruptive events devastate critical infrastructure lifelines, resulting in a substantial reduction of services across a community. Management of critical infrastructure assets is essential, and this can be achieved by optimal resource allocation to key assets within interconnected systems. The majority of current literature focuses on capturing the behavior of infrastructure systems individually, which presents a computational problem when dealing with complex, interdependent infrastructure systems. In this study, a comprehensive framework to evaluate a type of influence metric for ranking node and edge assets within an infrastructure network is presented. The influence metrics are shown to identify the importance of individual assets relative to one another, considering their dependencies with other critical networks. The city of Lima is utilized as a testbed to demonstrate the effectiveness of the proposed influence metric. We found that the failure of individual assets in critical infrastructure systems, such as water treatment plants, major and minor power stations, water reservoirs, and hospitals, affected a maximum percentage of [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] of the Lima population, respectively. The proposed influence metric is observed to outperform degree centrality in identifying critical assets within individual infrastructure lifelines, considering complex dependencies on other systems. This approach highlights a direction to understand dependent networks in general and can open up new frontiers in understanding complex system behavior.

The study aims at supporting the stakeholders involved in the emergency management (EM) activities to tackle the challenges related to scenarios involving interdependent critical infrastructure (CI) systems, by building resilience. The primary objective of implementing the capability-based approach is to enable and foster collaborative EM in the context of public-private collaborations for CI resilience. The READ framework and related tool have been tested to support stakeholders' resilience capacities assessment with respect to cross-border disruptions and thus identify the main areas where progress is needed. Two pilot cases were used to validate the approach and demonstrate its applicability in the context of regional public-private collaborations for critical infrastructure protection and Resilience with different degree of development and level of maturity, namely Basque Country (Spain) and Lombardy Region (Italy). The practitioners' feedback from both application cases confirmed the usefulness of such approach and helped to identify areas for future research and improvement.

On 4/5 December 2015 a slow moving frontal system associated with the extratropical cyclone Storm Desmond brought record‐breaking levels of rainfall to Lancaster. Both high ground and low‐lying areas were already saturated following the wettest November on record, and on the evening of 5 December the River Lune flooded into the city. Critical road and rail transport networks ground to a halt, and the city and the surrounding areas were left without mains power for 2 days after a key substation flooded, consequently leaving the majority of communication services inoperable. Was this a ‘Perfect Storm’, or a glimpse of what the future may hold for our cities as they face more frequent extreme weather events, set against the backdrop of urban population growth, and increasingly interdependent critical urban infrastructure systems?

The failures of interdependent critical infrastructure systems (CISs) caused by disasters could result in significant impacts on the economy and society of cities. Although existing studies have proposed several socioeconomic impact indicators of CIS failures, using these indicators as optimization objectives of restoration sequences, most of them only selected one indicator and failed to reveal their differences. This study aims to analyze the differences between various socioeconomic impact indicators in evaluating post-disaster CIS performance and to identify their effects on the optimized restoration sequences. To achieve this objective, this study simulates the failure propagation and recovery process of CISs, based on network modeling, and constructs six socioeconomic impact indicators for evaluating CIS performance and optimizing the restoration sequence. Then, this study analyzes the effects of different socioeconomic impact indicators by comparing the differences between post-disaster CIS performance, as well as the corresponding restoration sequence and recovery efficiency, among five groups. The results indicate that ignoring social impacts would significantly underestimate the consequences of CIS failures, and the restoration sequence aimed at minimizing social impact differs from other methods, with the recovery efficiency in regards to the social impact notably lower than that of the economic impact. This implies that evaluating the multidimensional social impacts is essential for accurately understanding the worst-case consequences of CIS failures with a bottom-line perspective.