Thornthwaite moisture index and depth of suction change under current and future climate‒An Australian study

Climate change is one of the major global challenges and it can have a significant influence on the behaviour and resilience of geotechnical structures.The changes in moisture content in soil lead to effective stress changes and can be accompanied by significant volume changes in reactive/expansive soils.The volume change leads to ground movement and can exert additional stresses on structures founded on or within a shallow depth of such soils.Climate change is likely to amplify the ground movement potential and the associated problems are likely to worsen.The effect of atmospheric boundary interaction on soil behaviour has often been correlated to Thornthwaite moisture index(TMI).In this study,the long-term weather data and anticipated future projections for various emission scenarios were used to generate a series of TMI maps for Australia.The changes in TMI were then correlated to the depth of suction change(H s),an important input in ground movement calculation.Under all climate scenarios considered,reductions in TMI and increases in H s values were observed.A hypothetical design scenario of a footing on expansive soil under current and future climate is discussed.It is observed that a design that might be considered adequate under the current climate scenario,may fail under future scenarios and accommodations should be made in the design for such events.

Bayesian network-based resilience assessment of interdependent infrastructure systems under optimal resource allocation strategies

Critical infrastructure systems(CISs)play a key role in the socio-economic activity of a society,but are exposed to an array of disruptive events that can greatly impact their function and performance.Therefore,understanding the underlying behaviors of CISs and their response to perturbations is needed to better prepare for,and mitigate the impact of,future disruptions.Resilience is one characteristic of CISs that influences the extent and severity of the impact induced by extreme events.Resilience is often dissected into four dimensions:robustness,redundancy,resourcefulness,and rapidity,known as the“4Rs”.This study proposes a framework to assess the resilience of an infrastructure network in terms of these four dimensions under optimal resource allocation strategies and incorporates interdependencies between different CISs,with resilience considered as a stochastic variable.The proposed framework combines an agent-based infrastructure interdependency model,advanced optimization algorithms,Bayesian network techniques,and Monte Carlo simulation to assess the resilience of an infrastructure network.The applicability and flexibility of the proposed framework is demonstrated with a case study using a network of CISs in Austin,Texas,where the resilience of the network is assessed and a“what-if”analysis is performed.

Unraveling fundamental properties of power system resilience curves using unsupervised machine learning

Power system is vital to modern societies,while it is susceptible to hazard events.Thus,analyzing resilience characteristics of power system is important.The standard model of infrastructure resilience,the resilience triangle,has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades.However,the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves(e.g.,residual performance and duration of recovery).Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data.Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance.There is a dire dearth of empirical studies in the field,which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems.To address this gap,this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States.Through the use of unsupervised machine learning,we examined different curve archetypes,as well as the fundamental properties of each resilience curve archetype.The results show two primary archetypes for power grid resilience curves,triangular curves,and trapezoidal curves.Triangular curves characterize resilience behavior based on three fundamental properties:1.critical functionality threshold,2.critical functionality recovery rate,and 3.recovery pivot point.Trapezoidal archetypes explain resilience curves based on 1.duration of sustained function loss and 2.constant recovery rate.The longer the duration of sustained function loss,the slower the constant rate of recovery.The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.

Untangling the relationship between power outage and population activity recovery in disasters

Despite recognition of the relationship between infrastructure resilience and community recovery,very limited empirical evidence exists regarding the extent to which the disruptions in and restoration of infrastructure ser-vices contribute to the speed of community recovery.To address this gap,this study investigates the relationship between community and infrastructure systems in the context of hurricane impacts,focusing on the recovery dy-namics of population activity and power infrastructure restoration.Empirical observational data were utilized to analyze the extent of impact,recovery duration,and recovery types of both systems in the aftermath of Hurricane Ida.The study reveals three key findings.First,power outage duration positively correlates with outage extent until a certain impact threshold is reached.Beyond this threshold,restoration time remains relatively stable re-gardless of outage magnitude.This finding underscores the need to strengthen power infrastructure,particularly in extreme weather conditions,to minimize outage restoration time.Second,power was fully restored in 70% of affected areas before population activity levels normalized.This finding suggests the role infrastructure function-ality plays in post-disaster community recovery.Quicker power restoration did not equate to rapid population activity recovery due to other possible factors such as transportation,housing damage,and business interruptions.Finally,if power outages last beyond two weeks,community activity resumes before complete power restoration,indicating adaptability in prolonged outage scenarios.This implies the capacity of communities to adapt to on-going power outages and continue daily life activities.These findings offer valuable empirical insights into the interaction between human activities and infrastructure systems,such as power outages,during extreme weather events.They also enhance our empirical understanding of how infrastructure resilience influences community recovery.By identifying the critical thresholds for power outage functionality and duration that affect popula-tion activity recovery,this study furthers our understanding of how infrastructure performance intertwines with community functioning in extreme weather conditions.Hence,the findings can inform infrastructure operators,emergency managers,and public officials about the significance of resilient infrastructure in life activity recovery of communities when facing extreme weather hazards.

Evaluating the flooding level impacts on urban metro networks and travel demand:behavioral analyses,agent-based simulation,and large-scale case study

With urban residents’increasing reliance on metro systems for commuting and other daily activities,extreme weather events such as heavy rainfall and flooding impacting the metro system services are becoming increasingly of concern.Plans for such emergency interruptions require a thorough understanding of the potential outcomes on both the system and individual component scales.However,due to the complex dynamics,constraints,and interactions of the elements involved(e.g.,disaster,infrastructure,service operation,and travel behavior),there is still no framework that comprehensively evaluates the system performance across different spatiotemporal scales and is flexible enough to handle increasingly detailed travel behavior,transit service,and disaster information data.Built on an agent-based model(ABM)framework,this study adopts a data-driven ABM simulation approach informed by actual metro operation and travel demand data to investigate the impact of flood-induced station closures on travelers as well as the overall system response.A before-after comparison is conducted where the traveler behaviors in disaster scenarios are obtained from a discrete choice model of alternative stations and routes.A case study of the Shanghai Metro is used to demonstrate the ability of the proposed approach in evaluating the impacts of flood-induced station closures on individual traveler behavior under normal operation and a series of water level rise scenarios of up to 5m.It was found that,when the flood-induced station closures only affect a few river-side stations in the city center,the travelers experience only minor disruptions to their trips due to the availability of unaffected stations nearby as a backup.However,as the water level increases and more stations(mainly in the suburban area)are affected,up to 25%of trips are no longer being fulfilled due to the loss of entrances,exits,or transfer links.The system experiences overall less crowdedness in terms of passenger volume and platform waiting time with a few exceptions of increased passenger load due to concentrations of passenger flows to alternative stations under flooding-induced station closures.The proposed approach can be adapted to other disaster scenarios to reveal the disaster impacts on both aggregated and disaggregated levels and guide the design of more spatio-and temporally-targeted emergency plans for metro systems.

On the effects of salient parameters for an efficient probabilistic seismic loss assessment of tunnels in alluvial soils

Tunnels are critical infrastructure for the sustainable development of urban areas worldwide,especially for modern metropolises.This study investigates the effects of salient parameters,such as the soil conditions,tunnel burial depth,tunnel construction quality,and aging phenomena of the lining,on the direct seismic losses of circular tunnels in alluvial deposits when exposed to ground seismic shaking.For this purpose,a practical approach is employed to probabilistically assess the direct losses of single tunnel segment with unit length,as well as of tunnel elements representative of the Shanghai Metro Lines 1 and 10,assuming various levels of seismic intensity.The findings of this study can serve as the basis for decision-making,seismic loss,and risk management based on the principles of infrastructure resilience.

Organizing resilient infrastructure initiatives:A study on conceptualization,motivation,and operation of ten initiatives in the Netherlands

Resilient infrastructure is critical to a sustainable and functioning society.Infrastructure management and(re)development are highly complex processes encompassing various stakeholders’interests while they are pres-sured by the uncertainty of climate change and social transition.In response to these challenges,various resilience initiatives emerged with different motivations and approaches.The purpose of this research is to understand the interplay between motivations and organizational approaches as well as resilience conceptualization.This can provide insights into which domains of resilience have been focused on and what needs to be improved in their or-ganizational approaches to realize motivations.This research specifically investigates ten resilient infrastructure initiatives in the Netherlands.By using scoping review and content analysis,our results highlight that resilience initiatives conceptualize resilience in different ways,mainly focusing on built and organizational resilience with a focus on long-term and wider geographic scope.Each initiative had several motivations,including 1)creat-ing innovative solutions,2)sharing knowledge,3)promoting commitment and cooperation,and 4)promoting resilience.These motivations are reflected in the organizational approach.For example,there was a strong link between the motivation‘creating shared knowledge’and the organizational approach‘research collaboration.’Generic motivation such as‘promoting resilience’does not have one mainstreaming approach,which shows promoting resilience in practice is still in the exploration stage.This research provides major motivations and organizational approaches and their link within the resilient infrastructure initiatives which can contribute to better organizing similar initiatives aiming for resilient infrastructure.

A state-of-the-art review of the development of self-healing concrete for resilient infrastructure

The brittleness of cement composites makes cracks almost inevitable,producing a serious limitation on the lifespan,resilience,and safety of concrete infrastructure.To address this brittleness,self-healing concrete has been developed for regaining its mechanical and durability properties after becoming cracked,thereby promising sustainable development of concrete infrastructure.This paper provides a comprehensive review of the latest developments in self-healing concrete.It begins by summarizing the methods used to evaluate the self-healing efficiency of concrete.Next,it compares strategies for achieving healing concrete.It then discusses the typical approaches for developing self-healing concrete.Finally,critical insights are proposed to guide future studies on the development of novel self-healing concrete.This review will be useful for researchers and practitioners interested in the field of self-healing concrete and its potential to improve the durability,resilience,and safety of concrete infrastructure.

On water security, sustainability, and the water-food-energy-climate nexus

The role of water security in sustainable development and in the nexus of water, food, energy and climate interactions is examined from the starting point of the definition of water security offered by Grey and Sadoff. Much about the notion of security has to do with the presumption of scarcity in the resources required to meet human needs. The treatment of scarcity in mainstream economics is in turn examined, therefore, in relation to how each of us as individuals reconciles means with ends, a procedure at the core of the idea of sustainable development. According to the Grey-Sadoff definition, attaining water security amounts to achieving basic, single- sector water development as a precursor of more general, self-sustaining, multi-sectoral development. This is con- sistent with the way in which water is treated as ＂first among equals＂, i.e. privileged, in thinking about what is key in achieving security around the nexus of water, food, energy and climate. Cities, of course, are locations where demands for these multiple resource-energy flows are increasingly being generated. The paper discusses two important facets of security, i.e., diversity of access to resources and services （such as sanitation） and resilience in the behavior of coupled human-built-natural systems. Eight quasi-operational principles, by which to gauge nexus security with respect to city buildings and infrastructure, are developed.

Advancing index-based climate risk assessment to facilitate adaptation planning:Application in Shanghai and Shenzhen,China

One of the key issues in climate risk management is to develop climate resilient infrastructure so as to ensure safety and sustainability of urban functioning systems as well as mitigate the adverse impacts associated with increasing climate hazards.However,conventional methods of assessing risks do not fully address the interaction of various subsystems within the city system and are unable to consolidate diverse opinions of various stakeholders on their assessments of sector-specific risks posed by climate change.To address this gap,this study advances an integrated-systems-analysis tool-Climate Risk Assessment of Infrastructure Tool(CRAIT),and applies it to analyze and compare the extent of risk factor exposure and vulnerability over time across five critical urban infrastructure sectors in Shanghai and Shenzhen,two cities that have distinctive geo-climate profiles and histories of infrastructure development.The results show significantly higher level of variation between the two cities in terms of vulnerability levels than that of exposure.More specifically,the sectors of critical buildings,water,energy,and information&communication in Shenzhen have significantly higher vulnerability levels than Shanghai in both the 2000s and the 2050s.We further discussed the vulnerability levels of subsystems in each sector and proposed twelve potential adaptation options for the roads system based on four sets of criteria:technical feasibility,flexibility,co-benefits,and policy compatibility.The application of CRAIT is bound to be a knowledge co-production process with the local experts and stakeholders.This knowledge co-production process highlights the importance of management advancements and nature-based green solutions in managing climate change risk in the future though differences are observed across the efficacy categories due to the geographical and meteorological conditions in the two cities.This study demonstrates that this knowledge co-creation process is valuable in facilitating policymakers'decision-making and their feedback to scientific understanding in climate risk assessment,and that this approach has general applicability for cities in other regions and countries.