Improving the resilience of critical infrastructures: Evidence-based insights from a systematic literature review

Rathnayaka, Bawantha; Siriwardana, Chandana; Robert, Dilan; Amaratunga, Rdg; Setunge, Sujeeva

doi:10.1016/j.ijdrr.2022.103123

Cited by 27 publications

(9 citation statements)

References 52 publications

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“…Likewise, Zimmerman ( 5 ), Dudenhoeffer et al ( 20 ), Zhang and Peeta ( 6 ), and Sharkey et al ( 21 ) classify interdependency from different perspectives; some classifications are based on systems’ functionalities, reaction to natural hazards, restoration, or economic impacts. Some other studies ( 22 – 25 ) focus on the resilience of infrastructures against natural disasters and human threats and propose frameworks to show the response of connected systems to these situations when human, environmental, and system performance and adaptivity are combined. State-of-the-art literature suggests the significant importance of analyzing infrastructures as connected systems rather than an individual entity.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Interdependencies Between Transportation and Stormwater Networks: The Case of Norman, Oklahoma

Kays,

Sadri,

Muraleetharan

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The significance of critical infrastructure systems in maintaining productivity is undeniable. However, such systems remain susceptible to external disturbances and cascading failures. Instead of operating independently, these physical systems, such as transportation and stormwater systems, form an interdependent system. This interdependence, particularly important during flooding, illustrates that the failure of a stormwater system can disrupt traffic networks. To explore the extent of such interdependency, this study investigates the transportation and stormwater networks in Norman, Oklahoma. Using network science theories and concepts of multilayered networks, this paper analyzes these systems, both individually and in combination. The study identifies closely located components in the road and stormwater networks using Moran's I spatial autocorrelation metric. Next, the connectivity of these networks is represented in a graph format to investigate the topological credentials (i.e., rank of relative importance) of the network components (i.e., water inlets, road intersections as nodes, and stormwater conduits, road segments as links). Moreover, such credentials further change by considering the weights of the network components (i.e., average daily traffic, water flow). The proximity-based connectivity considerations between these networks utilizing Moran's I significance score revealed a good indicator of spatial interdependency. When incorporating directionality, the multilayer network analysis highlights that highly central components tend to cluster spatially, unlike the undirected counterpart. The study also identifies vulnerable locations and network components in a combined network setting that differ from the networks in isolation. In doing so, the research reveals new insights governing the complex reliance of transportation systems on neighboring stormwater systems.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring the Interdependencies Between Transportation and Stormwater Networks: The Case of Norman, Oklahoma

Kays,

Sadri,

Muraleetharan

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…3 Furthermore, by adopting the "resilience triangle model," (i.e., a variant of that shown in Figure 1A to represent the initial loss after a disaster and the following restoration of the system functionality, many researchers have proposed different indices, metrics, and frameworks to assess the resilience performance of structural systems. [5][6][7][8] Although the framework enables quantitative assessment of the initial loss and the recovery process considering various uncertainties, Lim et al 4 identified its three critical limitations. First, the restoration curve models are often arbitrarily chosen by the modelers, which may lead to different resilience performance evaluations.…”

Section: Introductionmentioning

confidence: 99%

“…A framework consisting of four attributes—robustness, redundancy, resourcefulness, and rapidity—is the most widely used resilience concept in the structural engineering field 3 . Furthermore, by adopting the “resilience triangle model,” (i.e., a variant of that shown in Figure 1A to represent the initial loss after a disaster and the following restoration of the system functionality, many researchers have proposed different indices, metrics, and frameworks to assess the resilience performance of structural systems 5–8 . Although the framework enables quantitative assessment of the initial loss and the recovery process considering various uncertainties, Lim et al 4 .…”

Section: Introductionmentioning

confidence: 99%

System‐reliability‐based disaster resilience analysis for structures considering aleatory uncertainties in external loads

Yi,

Kim

2023

Earthq Engng Struct Dyn

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The concept of disaster resilience is getting more prominent in the era of climate change due to the increase in the intensities and uncertainties of disaster events. To effectively assess the holistic capacity of structural systems, a disaster resilience analysis framework has been developed from a system‐reliability‐based perspective. The framework evaluates resilience in terms of reliability, redundancy, and recoverability and provides quantitative indices of reliability and redundancy for structures with a resilience threshold. Although this framework enables the comprehensive evaluation of disaster resilience performance, practical applications of such concepts to the structures subjected to dynamic excitations with large aleatory uncertainty, such as earthquakes, remain challenging. This study develops a framework to assess the resilience performance of structures by taking into account the aleatory uncertainties in external forces. Along with the development of reliability and redundancy curves that can effectively accommodate such excitations, a new resilience threshold representation is proposed to incorporate recoverability in the decision‐making process. Moreover, we provide efficient procedures for calculating the reliability and redundancy curves to alleviate the computational complexity during the resilience analysis. Two earthquake application examples are presented targeting a nine‐story building and a cable‐stayed bridge system to demonstrate the enhanced practical applicability of the proposed framework.

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“…Resilience is a key term discussed when thinking about mitigating impacts on CIs due to natural hazards (Rathnayaka et al , 2022). The term resilience is defined in different studies.…”

Section: Introductionmentioning

confidence: 99%

Development of critical infrastructure resilience index for cities in Sri Lanka

Perera

Siriwardana

Arachchi

2022

IJDRBE

Self Cite

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Purpose Infrastructures become critical with the emerging threats triggering through disasters. Sri Lanka is a country with a higher risk of disaster impacts, in which the eye-opening has widened towards mitigating the damages towards critical infrastructures. Based on this, the purpose of this paper is to develop an index that identifies the significance of critical infrastructure resilience. Design/methodology/approach From the initial literature survey, disaster resilience is defined as capacity of three stages, absorptive, adaptive and restorative along with ten indicators to measure capacities. Selected indicators were then checked for suitability for scope of the research based on opinions of seven experts. Subsequently, the critical infrastructure resilience index (CIRI) was introduced such that the numerical values for each indicator are aggregated using the Z score method. Statistical relations between the actual impact against disasters and CIRI calculated for administrative regions in Sri Lanka were used as the final step to validate the developed index. Findings Resilience index development is presented in this paper with a comprehensive methodology of developing and validation. Further, the case study results imply the weakness and strengths in each resilience capacities, which are important in decision-making. Research limitations/implications Unavailability of disaster impact data and centralized data repository were main constrains in the validation process of this research. Hence proxy data was used to validate resilience index in this research. Originality/value This research identified and validated a novel approach of defining disaster resilience index for regional decision-making.

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Improving the resilience of critical infrastructures: Evidence-based insights from a systematic literature review

Cited by 27 publications

References 52 publications

Exploring the Interdependencies Between Transportation and Stormwater Networks: The Case of Norman, Oklahoma

Exploring the Interdependencies Between Transportation and Stormwater Networks: The Case of Norman, Oklahoma

System‐reliability‐based disaster resilience analysis for structures considering aleatory uncertainties in external loads

Development of critical infrastructure resilience index for cities in Sri Lanka

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