Evaluation of Spatial Resilience of Highway Networks in Response to Adverse Weather Conditions

Evaluation of Urban Spatial Resilience and Its Influencing Factors: Case Study of the Harbin–Changchun Urban Agglomeration in China

This study constructs a framework for evaluating urban spatial resilience based on five dimensions: scale, intensity, morphology, function, and benefit. Likewise, it empirically analyzes the spatial differences and influencing factors of urban spatial resilience in the Harbin–Changchun urban agglomeration from 2000 to 2020. Overall, the spatial resilience of the Harbin–Changchun urban agglomeration declined from 2000 to 2019. In addition, its ability to resist external disturbances weakened. The five dimensions of spatial resilience declined. However, urban spatial morphological resilience slightly increased. The spatial diversity of the Harbin–Changchun urban agglomeration is obvious, implying that the spatial resilience of cities in the central region, mainly in Suihua and Songyuan, is higher than in peripheral areas of the urban agglomeration, mostly in the Yanbian Korean Autonomous Prefecture, Siping, and Qiqihar. The period between 2000 and 2019 was dominated by cities with fluctuating spatial resilience. Furthermore, urban spatial resilience is influenced by a combination of factors, with economic support being the primary one. The selection of the urban spatial resilience research index system in this study is more spatially oriented and more accurately reflects the urban spatial resilience situation, which, in turn, provides a new planning perspective for urban planning in China.

The Resilience of Critical Infrastructure Systems: A Systematic Literature Review

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.