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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F4: Critical Energy Infrastructure".

Deadline for manuscript submissions: closed (5 October 2021) | Viewed by 30726

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Special Issue Editor

Prof. Dr. Abbas Barabadi

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Guest Editor

Department of Technology and Safety, The Arctic University of Norway, 6050 Langnes, 9037 Tromsø, Norway
Interests: risk management; resilience assessment; maintenance engineering; robustness; inspection; critical infrastructures; reliability engineering

Special Issue Information

Dear Colleagues,

Society relies heavily on critical infrastructure (CI) systems to provide and maintain vital societal functions. A critical infrastructure (CI) is defined as an asset or a system that is essential for the maintenance of vital societal functions, health, safety, security, economic or social wellbeing of people, and the disruption or destruction of which would have a significant impact on society because of the failure to maintain those functions. CI is an integrated system of people, ecological context, and engineered systems. Traditionally, to ensure the delivery of such functions, the focus of designers and operators has been on the protection of infrastructure systems from adverse and extreme events. However, recent events such as COVID-19 have illustrated that it is very difficult, and sometime not possible, to protect such systems from all kinds of possible hazards.

Moreover, these events have shown that it is difficult to precisely predict the all-potential hazards and their potentially cascading and complex impacts. This makes available risk management practice insufficient for the protection of infrastructure systems on which society depends. Hence, there has been a shift from the protection of critical infrastructure to the resilience of critical infrastructure, increasing the focus on preparedness, response, and recovery. In other words, having a resilient infrastructure, with the ability to limit the consequences of an impact through timely and efficient recovery processes, will certainly benefit infrastructure operators and society as a whole.

Despite the growing number of studies on resilience in engineering systems, the methods for operationalizing resilience in CI have yet to be defined. Resilience management is an approach that focuses primarily on management of the expected performance of a system operating on different operational conditions. The aim is to increase the robustness and recoverability of system against external/internal shocks and stresses in the face of change and uncertainty. Therefore, to have an effective resilience management, an understanding of how to measure and assess resilience is required. Moreover, we need to know how infrastructure resilience can be degraded or improved ex and post-external/internal shocks. Hence, a diverse perspective including ecology, engineering, psychology, and policy, economics, and organizational sciences is needed to understand and operationalize resilience management of CI systems.

The objective of this Special Issue is to document research contributions in the field of CI resilience management and assessment to continue building a resilient infrastructure knowledge community. In particular, we look for interdisciplinary contributions to management of resilience in CI from engineering disciplines as well as disciplines such as management, sociology, ecology, political science, psychology, urban sciences, geography, and economics.

Prof. Dr. Abbas Barabadi
Guest Editor

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Keywords

Risk analysis
Infrastructure systems
Resilience
Community resilience
Technical resilience
Safe-to-fail
Adaptive management
Interdependency
Maintenance engineering

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Published Papers (9 papers)

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Research

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17 pages, 2877 KiB

Open AccessArticle

Criteria-Based Fuzzy Logic Risk Analysis of Wind Farms Operation in Cold Climate Regions

by Albara M. Mustafa and Abbas Barabadi

Energies 2022, 15(4), 1335; https://doi.org/10.3390/en15041335 - 12 Feb 2022

Cited by 3 | Viewed by 1919

Abstract

Different risks are associated with the operation and maintenance of wind farms in cold climate regions, mainly due to the harsh weather conditions that wind farms experience in that region such as the (i) increased stoppage rate of wind turbines due to harsh weather conditions, (ii) limited accessibility to wind farms due to snow cover on roads, and (iii) cold stress to workers at wind farms. In addition, there are risks that are caused by wind farms during their operation, which impact the surrounding environment and community such as the (iv) risk of ice throw from wind turbines, (v) environmental risks caused by the wind farms, and (vi) social opposition risk to installing wind farms in cold climate regions, such as the Arctic. The analysis of these six risks provides an overall view of the potential risks encountered by designers, operators, and decision makers at wind farms. This paper presents a methodology to quantify the aforementioned risks using fuzzy logic method. At first, two criteria were established for the probability and the consequences of each risk; with the use of experts’ judgments, membership functions were graphed to reflect the two established criteria, which represented the input to the risk analysis process. Furthermore, membership functions were created for the risk levels, which represented the output. To test the proposed methodology, a wind farm in Arctic Norway was selected as a case study to quantify its risks. Experts provided their assessments of the probability and consequences of each risk on a scale from 0–10, depending on the description of the wind farm provided to them. Risk levels were calculated using MATLAB fuzzy logic toolbox and ranked accordingly. Limited accessibility to the wind farm was ranked as the highest risk, while the social opposition to the wind farm was ranked as the lowest. In addition, to demonstrate the effects of the Arctic operating conditions on performance and safety of the wind farm, the same methodology was applied to a wind farm located in a non-cold-climate region, which showed that the risks ranked differently. Full article

(This article belongs to the Special Issue Critical Infrastructure Resilience Assessment and Management)

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16 pages, 10226 KiB

Open AccessArticle

Resilience Assessment: A Performance-Based Importance Measure

by Ali Nouri Qarahasanlou, Ali Zamani, Abbas Barabadi and Mahdi Mokhberdoran

Energies 2021, 14(22), 7575; https://doi.org/10.3390/en14227575 - 12 Nov 2021

Viewed by 1797

Abstract

The resilience of a system can be considered as a function of its reliability and recoverability. Hence, for effective resilience management, the reliability and recoverability of all components which build up the system need to be identified. After that, their importance should be identified using an appropriate model for future resource allocation. The critical infrastructures are under dynamic stress due to operational conditions. Such stress can significantly affect the recoverability and reliability of a system’s components, the system configuration, and consequently, the importance of components. Hence, their effect on the developed importance measure needs to be identified and then quantified appropriately. The dynamic operational condition can be modeled using the risk factors. However, in most of the available importance measures, the effect of risk factors has not been addressed properly. In this paper, a reliability importance measure has been used to determine the critical components considering the effect of risk factors. The application of the model has been shown through a case study. Full article

(This article belongs to the Special Issue Critical Infrastructure Resilience Assessment and Management)

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17 pages, 34209 KiB

Open AccessArticle

A Digital Information Model Framework for UAS-Enabled Bridge Inspection

by Kamal Achuthan, Nick Hay, Mostafa Aliyari and Yonas Zewdu Ayele

Energies 2021, 14(19), 6017; https://doi.org/10.3390/en14196017 - 22 Sep 2021

Cited by 15 | Viewed by 3078

Abstract

Unmanned aerial systems (UAS) provide two main functions with regards to bridge inspections: (1) high-quality digital imaging to detect element defects; (2) spatial point cloud data for the reconstruction of 3D asset models. With UAS being a relatively new inspection method, there is little in the way of existing framework for storing, processing and managing the resulting inspection data. This study has proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS). Previous efforts focusing on visualisation functionality have focused on BIM and GIS as separate entities, which has a number of problems associated with it. This methodology has a core focus on the integration of BIM and GIS, providing an effective and efficient information model, which provides vital visual context to inspectors and users of the BMS. Three-dimensional GIS visualisation allows the user to navigate through a fully interactive environment, where element level inspection information can be obtained through point-and-click operations on the 3D structural model. Two visualisation environments were created: a web-based GIS application and a desktop solution. Both environments develop a fully interactive, user-friendly model which have fulfilled the aims of coordinating and streamlining the BMS process. Full article

(This article belongs to the Special Issue Critical Infrastructure Resilience Assessment and Management)

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15 pages, 2255 KiB

Open AccessArticle

Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks

by Albara M. Mustafa and Abbas Barabadi

Energies 2021, 14(15), 4439; https://doi.org/10.3390/en14154439 - 22 Jul 2021

Cited by 4 | Viewed by 2049

Abstract

Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of resilience-enhanced strategies. This paper proposes a probabilistic model to calculate the resilience of wind farms facing disruptive weather conditions. In this study, the resilience of wind farms is considered to be a function of their reliability, maintainability, supportability, and organizational resilience. The relationships between these resilience variables can be structured using Bayesian network models. The use of Bayesian networks allows for analyzing different resilience scenarios. Moreover, Bayesian networks can be used to quantify resilience, which is demonstrated in this paper with a case study of a wind farm in Arctic Norway. The results of the case study show that the wind farm is highly resilient under normal operating conditions, and slightly degraded under Arctic operating conditions. Moreover, the case study introduced the calculation of wind farm resilience under Arctic black swan conditions. A black swan scenario is an unknowable unknown scenario that can affect a system with low probability and very high extreme consequences. The results of the analysis show that the resilience of the wind farm is significantly degraded when operating under Arctic black swan conditions. In addition, a backward propagation of the Bayesian network illustrates the percentage of improvement required in each resilience factor in order to attain a certain level of resilience of the wind farm under Arctic black swan conditions. Full article

(This article belongs to the Special Issue Critical Infrastructure Resilience Assessment and Management)

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25 pages, 8843 KiB

Open AccessArticle

A Case Study in View of Developing Predictive Models for Water Supply System Management

by Katarzyna Pietrucha-Urbanik, Barbara Tchórzewska-Cieślak and Mohamed Eid

Energies 2021, 14(11), 3305; https://doi.org/10.3390/en14113305 - 4 Jun 2021

Cited by 6 | Viewed by 2161

Abstract

Initiated by a case study to assess the effectiveness of the modernisation actions undertaken in a water supply system, some R&D activities were conducted to construct a global predictive model, based on the available operational failure and recovery data. The available operational data, regarding the water supply system, are the pipes’ diameter, failure modes, materials, functional conditions, seasonality, and the number of failures and time-to-recover intervals. The operational data are provided by the water company responsible of the supply system. A predictive global model is proposed based on the output of the operational data statistical assessment. It should assess the expected effectiveness of decisions taken in support of the modernisation and the extension plan. Full article

(This article belongs to the Special Issue Critical Infrastructure Resilience Assessment and Management)

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20 pages, 2030 KiB

Open AccessArticle

Resilience Assessment in Electricity Critical Infrastructure from the Point of View of Converged Security

by Martin Hromada, David Rehak and Ludek Lukas

Energies 2021, 14(6), 1624; https://doi.org/10.3390/en14061624 - 15 Mar 2021

Cited by 15 | Viewed by 2334

Abstract

In terms of service provision, the electricity sector is the most important critical infrastructure sector, on the supply of which the vast majority of society and its basic vital functions depend. Extensive disruption of these supplies would have negative effects not only on basic human needs, but also on the economy and security of the state. For this reason, it is necessary to ensure permanent and comprehensive monitoring of the infrastructure elements resilience level, especially against threats with a multispectral impact on several areas of security. For this reason, the authors of the article developed the Converged Resilience Assessment (CRA) method, which enables advanced assessment of the electricity critical infrastructure elements resilience from the converged security point of view. Converged security in this case combines (converges) physical, cyber and operational security into a complementary unit. This reflects the integral determinants of resilience across related areas of security/safety. The CRA method focuses mainly on information and situation management, which integrates and correlates information (signals) from systems and sensors in order to obtain an overview of the situation and the subsequent effective management of its solution. The practical use of the proposed method is demonstrated on a selected element of the Czech Republic transmission system. The CRA method is currently embodied in a functional sample that has been piloted on several TSO elements. Further development of this method is seen mainly in fulfilling the logic of network infrastructure and reflection between elementary and intersectoral links in the context of synergistic and cascading effects in a broader context. Full article

(This article belongs to the Special Issue Critical Infrastructure Resilience Assessment and Management)

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13 pages, 1345 KiB

Open AccessArticle

Functional Safety Concept to Support Hazard Assessment and Risk Management in Water-Supply Systems

by Barbara Tchórzewska-Cieślak, Katarzyna Pietrucha-Urbanik and Mohamed Eid

Energies 2021, 14(4), 947; https://doi.org/10.3390/en14040947 - 11 Feb 2021

Cited by 9 | Viewed by 2188

Abstract

Within the frame of upgrading and modernisation of the Water Supply System (WSS), our work is focussing on the safety systems/devices implemented or that should be implemented in the WSS. The implementation of safety systems is supposed to reduce hazard occurrence and hazardous consequences in case of a WSS unsafe disruption. To assess this reduction, we preconise the use of the safety integrity levels standards. The implementation of the safety systems/devices is undertaken on the ground of the multi-barriers safeguard approach. The “Water Contamination Hazard” is considered in the paper. A case study is presented, assessed and conclusions are drawn. The methodology presented in the paper and the results of the case study assessment will contribute to the decision-making regarding the upgrading of the safety and the performance of the WSS. Full article

(This article belongs to the Special Issue Critical Infrastructure Resilience Assessment and Management)

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16 pages, 3785 KiB

Open AccessArticle

Automatic Crack Segmentation for UAV-Assisted Bridge Inspection

by Yonas Zewdu Ayele, Mostafa Aliyari, David Griffiths and Enrique Lopez Droguett

Energies 2020, 13(23), 6250; https://doi.org/10.3390/en13236250 - 27 Nov 2020

Cited by 83 | Viewed by 5816

Abstract

Bridges are a critical piece of infrastructure in the network of road and rail transport system. Many of the bridges in Norway (in Europe) are at the end of their lifespan, therefore regular inspection and maintenance are critical to ensure the safety of their operations. However, the traditional inspection procedures and resources required are so time consuming and costly that there exists a significant maintenance backlog. The central thrust of this paper is to demonstrate the significant benefits of adapting a Unmanned Aerial Vehicle (UAV)-assisted inspection to reduce the time and costs of bridge inspection and established the research needs associated with the processing of the (big) data produced by such autonomous technologies. In this regard, a methodology is proposed for analysing the bridge damage that comprises three key stages, (i) data collection and model training, where one performs experiments and trials to perfect drone flights for inspection using case study bridges to inform and provide necessary (big) data for the second key stage, (ii) 3D construction, where one built 3D models that offer a permanent record of element geometry for each bridge asset, which could be used for navigation and control purposes, (iii) damage identification and analysis, where deep learning-based data analytics and modelling are applied for processing and analysing UAV image data and to perform bridge damage performance assessment. The proposed methodology is exemplified via UAV-assisted inspection of Skodsberg bridge, a 140 m prestressed concrete bridge, in the Viken county in eastern Norway. Full article

(This article belongs to the Special Issue Critical Infrastructure Resilience Assessment and Management)

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Review

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32 pages, 3319 KiB

Open AccessReview

The Resilience of Critical Infrastructure Systems: A Systematic Literature Review

by Adel Mottahedi, Farhang Sereshki, Mohammad Ataei, Ali Nouri Qarahasanlou and Abbas Barabadi

Energies 2021, 14(6), 1571; https://doi.org/10.3390/en14061571 - 12 Mar 2021

Cited by 45 | Viewed by 8361

Abstract

Risk management is a fundamental approach to improving critical infrastructure systems’ safety against disruptive events. This approach focuses on designing robust critical infrastructure systems (CISs) that could resist disruptive events by minimizing the possible events’ probability and consequences using preventive and protective programs. However, recent disasters like COVID-19 have shown that most CISs cannot stand against all potential disruptions. Recently there is a transition from robust design to resilience design of CISs, increasing the focus on preparedness, response, and recovery. Resilient CISs withstand most of the internal and external shocks, and if they fail, they can bounce back to the operational phase as soon as possible using minimum resources. Moreover, in resilient CISs, early warning enables managers to get timely information about the proximity and development of distributions. An understanding of the concept of resilience, its influential factors, and available evaluation and analyzing tools are required to have effective resilience management. Moreover, it is important to highlight the current gaps. Technological resilience is a new concept associated with some ambiguity around its definition, its terms, and its applications. Hence, using the concept of resilience without understanding these variations may lead to ineffective pre- and post-disruption planning. A well-established systematic literature review can provide a deep understanding regarding the concept of resilience, its limitation, and applications. The aim of this paper is to conduct a systematic literature review to study the current research around technological CISs’ resilience. In the review, 192 primary studies published between 2003 and 2020 are reviewed. Based on the results, the concept of resilience has gradually found its place among researchers since 2003, and the number of related studies has grown significantly. It emerges from the review that a CIS can be considered as resilient if it has (i) the ability to imagine what to expect, (ii) the ability to protect and resist a disruption, (iii) the ability to absorb the adverse effects of disruption, (iv) the ability to adapt to new conditions and changes caused by disruption, and (v) the ability to recover the CIS’s normal performance level after a disruption. It was shown that robustness is the most frequent resilience contributing factor among the reviewed primary studies. Resilience analysis approaches can be classified into four main groups: empirical, simulation, index-based, and qualitative approaches. Simulation approaches, as dominant models, mostly study real case studies, while empirical methods, specifically those that are deterministic, are built based on many assumptions that are difficult to justify in many cases. Full article

(This article belongs to the Special Issue Critical Infrastructure Resilience Assessment and Management)

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