A Comparative Analysis of Performance-Based Resilience Metrics via a Quantitative-Qualitative Combined Approach: Are We Measuring the Same Thing?

Since the proposal of the pioneering“resilience triangle”paradigm,various time-series performance-based metrics have been devised for resilience quantification.The numerous choices diversify the toolbox for measuring this compound system concept;however,this multiplicity causes intractable questions for applications,including“Do these metrics measure the same resilience?”and“Which one to pick under what circumstance?”In this study,we attempted to address these two fundamental issues using a comprehensive comparative investigation.Through a quantitative-qualitative combined approach,12 popular performance-based resilience metrics are compared using empirical data from China’s aviation system under the disturbance of COVID-19.Quantitative results indicate that only 12 of the 66 metric pairs are strongly positively correlated and with no significant differences in quantification outcomes;qualitative results indicate that the majority of the metrics are based on different definition interpretations,basic components,and expression forms,and thus essentially measure different resilience.The advantages and disadvantages of each metric are comparatively discussed,and a“how to choose”guideline for metric users is proposed.This study is an introspective investigation of resilience quantification studies,aiming to offer a new perspective to scrutinize those benchmarking metrics.

Resilient power grid for smart city

Modern power grid has a fundamental role in the operation of smart cities.However,high impact low probability extreme events bring severe challenges to the security of urban power grid.With an increasing focus on these threats,the resilience of urban power grid has become a prior topic for a modern smart city.A resilient power grid can resist,adapt to,and timely recover from disruptions.It has four characteristics,namely anticipation,absorption,adaptation,and recovery.This paper aims to systematically investigate the development of resilient power grid for smart city.Firstly,this paper makes a review on the high impact low probability extreme events categories that influence power grid,which can be divided into extreme weather and natural disaster,human-made malicious attacks,and social crisis.Then,resilience evaluation frameworks and quantification metrics are discussed.In addition,various existing resilience enhancement strategies,which are based on microgrids,active distribution networks,integrated and multi energy systems,distributed energy resources and flexible resources,cyber-physical systems,and some resilience enhancement methods,including probabilistic forecasting and analysis,artificial intelligence driven methods,and other cutting-edge technologies are summarized.Finally,this paper presents some further possible directions and developments for urban power grid resilience research,which focus on power-electronized urban distribution network,flexible distributed resource aggregation,cyber-physical-social systems,multi-energy systems,intelligent electrical transportation and artificial intelligence and Big Data technology.

A Probabilistic Approach to the Evaluation of Seismic Resilience in Road Asset Management

Road networks are classified as critical infrastructure systems.Their loss of functionality not only hinders residential and commercial activities,but also compromises evacuation and rescue after disasters.Dealing with risks to key strategic objectives is not new to asset management,and risk management is considered one of the core elements of asset management.Risk analysis has recently focused on understanding and designing strategies for resilience,especially in the case of seismic events that present a significant hazard to highway transportation networks.Following a review of risk and resilience concepts and metrics,an innovative methodology to stochastically assess the economic resources needed to restore damaged infrastructures,one that is a relevant and complementary element within a wider resilience-based framework,is proposed.The original methodology is based on collecting and analyzing ex post reconstruction and hazard data and was calibrated on data measured during the earthquake that struck central Italy in 2016 and collected in the following recovery phase.Although further improvements are needed,the proposed approach can be used effectively by road managers to provide useful information in developing seismic retrofitting plans.