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Coastal water supply infrastructure systems (CWSISs) are exposed to saltwater intrusion (SWI) exacerbated by sea level rise (SLR) stressors. However, most existing definitions of CWSIS resilience are confined to the system's global severity, while uncertainties from SLR are not considered. In this paper, we develop a CWSIS model considering uncertainties from SLR and define a new formula for resilience based on three components of system severity, i.e., social severity (SevS) affected by water shortages to end users, regional severity (SevR) caused by water shortages in water treatment plants (WTPs), and technological severity (SevT) considering water shortages in wells. A case study in Xingcheng is designed to (1) examine the resilience response of CWSISs to future SLR, (2) identify vulnerable components, and (3) compare the cost-effectiveness of different measures for enhancing resilience using scenario analysis with full consideration of future uncertainties from SLR. This paper contributes to the development of sustainability assessments for urban water systems subject to future sea level change. Finding response measures with high adaptiveness across a variety of future scenarios is crucial for establishing a sustainable urban water system in the long term. [ABSTRACT FROM AUTHOR]

Emergency managers (EMs) need nuanced data that contextualize the local-scale risks and impacts posed by major storm events (e.g. hurricanes and nor'easters). Traditional tools available to EMs, such as weather forecasts or storm surge predictions, do not provide actionable data regarding specific local concerns, such as access by emergency vehicles and potential communication disruptions. However, new storm models now have sufficient resolution to make informed emergency management at the local scale. This paper presents a Participatory Action Research (PAR) approach to capture critical infrastructure managers concerns about hurricanes and nor'easters in Providence, Rhode Island (USA). Using these data collection approach, concerns can be integrated into numerical storm models and used in emergency management to flag potential consequences in real time during the advance of a storm. This paper presents the methodology and results from a pilot project conducted for emergency managers and highlights implications for practice and future academic research. [ABSTRACT FROM AUTHOR]

AbstractTo mitigate or minimize the effect of explosions and save lives, assets, and resources, a diverse range of methods has been applied to protect civil infrastructures against potential blast loads. Particularly, lightweight concrete has recently been investigated as a building block for protective walls against explosions in confined structures. However, the effect of configuring multiple walls against blast loads has not been examined yet. In this paper, we perform a numerical parametric study on various factors affecting the performance of multiple layers of walls composed of lightweight concrete blocks and their effect on blast wave pressure reduction in a blast shelter. [ABSTRACT FROM AUTHOR]

The work proposes a technique to improve the infrastructure and energy resilience of new developments during the planning stage. Several resilience-related parameters are developed in this paper that can be used to quantify resilience. To apply these parameters, the work assumes various energy outage scenarios varying from less than 24 h to 3 weeks. During these scenarios, a neighborhood population can be relocated to several public buildings promoting better utilization of onsite energy resources. The technique is applied to four representative neighborhoods encompassing various sustainability measures including clean energy. Further, this paper demonstrates an urban scale improvement technique for greater energy and infrastructure resilience. The results indicate a significant improvement in infrastructure resilience by relocating public shelter buildings on the main street intersections so that these can be easily accessible during energy outages or disaster events. Energy resilience can be achieved by the appropriate design of onsite energy resources to eliminate vulnerabilities. For instance, 8.8% to 15.4% of additional land for solar thermal collectors can eliminate thermal energy vulnerabilities. When surplus generation from onsite resources is twice or more as compared to demand during their unavailability, the electrical vulnerability can be eliminated by employing suitable battery banks in various buildings. [ABSTRACT FROM AUTHOR]

Extreme weather events can cause significant damage to power distribution network infrastructure, often resulting in power outages. Distribution Network Operators (DNOs) are faced with the challenging task of responding to these outages in real time while maintaining a resilient grid. Our paper presents an innovative approach to alert operators about the potential risk associated with upcoming extreme weather through a normalized fragility curve. The uniqueness of the curve is the ability to capture regional differences across a DNO's territory while presenting operators with a means of setting unified risk thresholds. This can support a proactive response and allow the staging of necessary resources to minimize the threat posed by such events. Our approach captures the changes in failure probability associated with differing wind regimes and demonstrates the benefit of sub‐regional meteorological information. The proposed approach is demonstrated for wind events using 20 years of historical fault records from a DNO in the United Kingdom (UK). While its efficacy is demonstrated for windstorms in the UK, the approach could be applied globally to develop normalized fragility curves for other types of seasonal extreme weather events such as snowstorms, hurricanes, or linked hazards such as wildfires. The approach can also facilitate an understanding of how infrastructure may operate under future climate conditions, supporting proactive adaptation. [ABSTRACT FROM AUTHOR]

Over the past years, the frequency and scope of disasters affecting the United States have significantly increased. Government agencies have made efforts in improving the nation's disaster response framework to minimize fatalities and economic loss due to disasters. Disaster response has evolved with the emergency management agencies incorporating systematic changes in their organization and emergency response functions to accommodate lessons learned from past disaster events. Technological advancements in disaster response have also improved the agencies' ability to prepare for and respond to natural hazards. The transportation and logistics sector has a primary role in emergency response during and after disasters. In this light, this paper seeks to identify how effective policy changes and new technology have aided the transportation and logistics sector in emergency response and identify gaps in current practices for further improvement. Specifically, this study compares and contrasts the transportation and logistical support to emergency relief efforts during and after two major Hurricane events in the U.S., namely Hurricane Katrina (which affected New Orleans in 2005) and Hurricane Harvey (which affected Houston in 2017). This comparison intends to outline the major steps taken by the government and the private entities in the transportation and logistics sector to facilitate emergency response and the issues faced during the process. Finally, the paper summarizes the lessons learned from both the Hurricane events and provides recommendations for further improvements in transportation and logistical support to disaster response. [ABSTRACT FROM AUTHOR]

The search for a bona fide robustness index to quantify the system performance upon unexpected disturbances has been a continuous research effort. Previous studies have largely neglected to reflect the damage evolution feature of a progressive collapse failure. To bridge this important knowledge gap, this paper presents a novel risk-based robustness index for structures prone to disproportionate progressive collapse. Defined on a new concept, referred to as damage evolution curve, the proposed index considers the full spectrum of risk due to initiating events, and rigorously quantifies the impact of intermediate partial damages on the robustness assessment. Examples using a Daniels system, a truss, and an idealised moment-resistant frame are presented to illustrate how the proposed index is applied for assessing the robustness of structures with increasing complexity. In addition, a comprehensive comparative study against four existing robustness indices is carried out. It is shown that the proposed index provides consistent results for different influencing factors, whereas the other indices either respond counter-intuitively or are insensitive to some of the factors. With consideration of intermediate damage and failure consequences, the development of the proposed robustness index represents an important step towards the performance-based design against progressive collapse. [ABSTRACT FROM AUTHOR]

While the trend towards building resilience in infrastructure systems for disaster risk reduction is accelerating, the application of infrastructure resilience is largely conceptual or an embellishment to established modelling techniques. In response, this paper sets out the theoretical foundations of infrastructure resilience, which broadly applies across critical infrastructure networks. This is followed by reviewing system-based and network-based approaches to resilience assessment with a focus on transport infrastructure, where there is an emerging body of studies. It spotlights critical issues in conflating resilience with other concepts of infrastructure safety management or merely tagging resilience on established methods, indicating a phenomenon of "old wine in new bottles". Also, oversimplifying disruption scenarios does not provide a sufficient basis for informed resilience planning. This paper reminds readers to look beyond resilience as a mere buzzword and offers guidance on how to do so by recognising the theoretical and methodological cornerstones of infrastructure resilience. [ABSTRACT FROM AUTHOR]

Having become vital to satisfying basic human needs, water distribution systems (WDSs) are considered critical infrastructure. They are vulnerable to critical events such as extreme weather, natural and man-made disasters, armed conflicts etc. To account for critical events during design and operation of WDSs, the concept of resilience is frequently mentioned. How resilience of WDSs can be assessed using resilience metrics has been the subject of research of many publications. The aim of this paper is to inspect the alignment between a general understanding of resilience in WDSs and the metrics used for their resilience assessment. A novel framework for categorising resilience metrics for WDSs is presented. A literature review of resilience metrics for WDSs is performed and the results are analysed using the developed framework. The results show that the existing resilience metrics are not able to capture resilience in its complexityresilience metrics do not really assess resilience of the WDSs as a whole, but rather focus only on specific functions and properties which can make the WDSs resilient. [Display omitted] • Resilience metrics for water distribution systems do not fully assess resilience. • Focus lies on specific functions and properties of resilient systems. • Resilience metrics do not assure generality with regard to different critical events. [ABSTRACT FROM AUTHOR]

The U.S. economy and way of life are highly dependent on transportation systems, which move goods and people locally, regionally, nationally, and internationally. Major disruptions to transportation networks due to natural hazards, manmade hazards (notably terrorist and cyberattacks), accidents, or infrastructure failure can cause substantial social and economic impacts. Through qualitative and quantitative analyses, including an in-depth review of the literature and semi-structured interviews, we identify barriers to and opportunities for lowering transportation-related disaster losses and for improving infrastructure risk management including the need for better data and metrics to support resilience. Our analyses are supported by a review of transportation-related Public Assistance (PA) expenditures to understand the magnitude and variation of expenditures over time. PA is a U.S. Federal Emergency Management Agency (FEMA) program dedicated to partial reimbursement of hazard-related losses for public infrastructure. Our analyses show that the availability and significant outlays of PA after disasters inhibit risk-based loss reduction actions by infrastructure managers. Risk management tools, notably insurance and mitigation measures, can efficiently reduce disaster losses and speed recovery times. This paper concludes with recommendations for policy measures to facilitate improvements in transportation infrastructure resilience. • Almost $10 billion federal disaster assistance for transportation (2003–2017). • Availability of public funds for restoration reduces loss reduction activities. • Insurance and mitigation measures reduce disaster losses and speed recovery time. • Need for data, metrics, study of emerging risks, tools for resilience financing. • Study developed seven recommendations for enhancing infrastructure resilience. [ABSTRACT FROM AUTHOR]

Natural hazards such as windstorms, earthquakes, and floods cause damage and failure to both structural and non-structural elements, significantly impacting the functional integrity and overall performance of the building systems. The economic loss due to such events and the often tortuous path to recovery call for revisiting engineering approaches to resilience assessment through developing simulation methods that provide a balance between fidelity and efficiency, particularly if the post-event assessment is to be performed at a large scale, e.g., the scale of a community. In this paper, we develop a discrete simulation framework for modeling the response of structures as a middle-ground solution between overly simplistic multi-degree-of-freedom models on the one hand and intricate FE models on the other. The framework draws upon the Potential-of-Mean-Force (PMF) approach to Lattice Element Method (LEM) where the main idea is to discretize the system into a set of particles that interact with each other through prescribed interaction potentials. These potentials are calibrated beforehand at member scale, for different structural and non-structural components. Here we focus on providing the main elements and the steps necessary for adaptation to modeling structural components in linear regime and leave the extension to nonlinear regime and the modeling of damage for future developments. This includes calibration of the potentials for structural members of different types (1D vs. 2D) under different actions (axial, bending, in-plane and out-of-plane actions) through an energetic handshake between the lattice model and continuum theories, e.g., the Timoshenko beam theory and Kirchhoff–Love plate theory. We explore the utility of the proposed method through its application to simulation of a set of building systems with different levels of complexity and under various loading conditions. [Display omitted] • A simulation framework based on Potential of Mean Force (PMF) is proposed. • The PMF allows a formulation of Lattice Element Method (LEM) in an energetic setting. • The interaction potentials for the PMF LEM are calibrated based on energy equivalence arguments. • Calibrated potentials are proposed for both in-plane and out-of-plane actions. • The PMF LEM is validated via application to structural systems with different complexity. [ABSTRACT FROM AUTHOR]

Highlights • We support retrofit/repair decisions in transportation networks under seismic hazards. • We integrate probabilistic seismic hazard assessment and stochastic optimization. • We test the capabilities of decomposition techniques for large scale optimization. • We provide a illustrative example for a complex network with realistic features. Abstract In the context of infrastructure resilience and risk management, key decisions occur in the aftermath of adverse events (e.g., immediate response and later repair of damage), but preemptive decisions must be made under uncertainty about the specific disaster realization to face in the future (e.g., strengthening components, or allocating resources for post-event activities). This paper proposes an optimization framework to address problems in which preemptive decisions are coupled with those eventually required to respond to an uncertain adverse event. Specifically, strategic decisions are pursued regarding whether to proactively retrofit or reactively repair bridges in a transportation network under seismic hazards, with the objective of minimizing the cost of maintaining a target network performance metric throughout a set of possible adverse scenarios. A two-stage stochastic programming approach is presented, which relates pre- and post-event decisions, accounting for the uncertainty throughout scenarios. The proposed approach implies a decomposition of the optimization problem that enables the analysis of large sets of scenarios, which is advantageous when dealing with complex networks as the ones addressed in infrastructure engineering practice. The methodological framework is presented along with an analysis of the San Francisco Bay Area transportation network, as an instance of a realistic, complex infrastructure network. Results evidence the potential of the approach to provide risk-informed decision support, while being able to deal with large sets of components and scenarios under an exact optimization approach, and solving problems with large number of variables and constraints. [ABSTRACT FROM AUTHOR]

Most formal engineering approaches to uncertainty use risk-based methods. Because risk formulations have a number of limitations there are situations where a resilience approach is preferable. A problem with resilience is the difficulty of measuring it. The paper discusses the issue and shows how a resilience formulation was used to prioritise actions to improve infrastructure resilience in an extensive region of New Zealand. The region was a complex system-of-systems so a systems approach was used. Once modelled, the infrastructure system was probed using three natural-hazard scenarios to determine system-element vulnerabilities. The vulnerability of each element was then matched with an importance value reflecting the effect of an element failure on community resilience. Community resilience was quantified in terms of overall income coming from three main sources each of which could be characterised by flow in a virtual pipeline. The pipelines were complex: tourism, for instance, required not only roads but also accommodation, communication, access and so on. The effect of infrastructure failures on pipeline flow and hence income quantified the relative importance of each infrastructure element. The vulnerability and importance values as a pair prioritised resilience-improving intervention for the element. [ABSTRACT FROM AUTHOR]

Innovative technologies are presenting opportunities to improve resilience of energy plans for industrial and military installations. The investment rationale is complicated by uncertain future conditions across the system lifecycle, including technology, climate, economy, and others. This paper introduces resilience analytics with scenario-based preferences as follows. Risk is addressed here as the degree of disruption of priorities for investments in engineering systems. The particular concern of this paper is disruption from shifts in public values, and to evaluate the resilience of investment plans to such shifts. It recognizes resilience models as compilations of instantaneous framings of initiatives, objectives, stakeholder preferences, and uncertainties. Problem frames can be considered in series, where inputs to frames are the outputs of previous frames. Or frames can be considered in parallel, featuring joint inputs while addressing differing questions. This paper presents a case study of resilience analytics focusing on two quantitative frames. In the first frame, scenario-based preferences are used to identify combinations of factors disruptive to energy innovation at installations. In the second frame, estimation of lifecycle costs is performed with respect to factors that were identified as influential in the previous frame. [ABSTRACT FROM AUTHOR]

It is imperative that water supply networks (WSNs) continue to perform even after subjected to natural and anthropogenic hazards, and it is even more important in the case of earthquakes for fighting fires that usually follow. Past earthquakes have caused significant damage to WSNs rendering them dysfunctional and potentially threatening human survival. Although there has been a lot of research conducted in the past on the behavior of buried pipelines, there are few metrics and models that are readily usable for improving the performance of large WSNs prone to seismic hazards. This paper proposes an easy-to-use metric for quantifying resilience and an optimization framework for improving WSN resilience subjected to budgetary constraints. The proposed resilience metric is dependent on hazard intensity, estimated pipeline response, and network topology. The use of the proposed metric and the optimization framework are demonstrated on a large (1,874 pipelines and 1,474 demand nodes), real-world WSN in a coastal region prone to earthquakes. The resilience value of the existing WSN used for demonstration is found to increase by approximately 8% with a $10 million investment. The proposed metric and the optimization framework can be helpful in rehabilitation planning and capital improvement works by water utilities. [ABSTRACT FROM AUTHOR]

Recent research into the concept of resilience has shown that it helps key players in urban development to assess and set priorities for resistance and recovery for disaster risk management. However, a competing issue within post-disaster recovery is managing the trade-offs between quickly restoring infrastructure services versus taking time to consider and consult on alternative options. Through an examination of the post-earthquake reconstruction in Christchurch, New Zealand, this paper considers infrastructure resilience by using a hierarchy of measures. This hierarchy shows how infrastructure resilience needs to be considered as a series of interventions in response to different levels of damage. It elucidates the varying nature of resilience measures, the decision-making processes required to implement them and constraints, chiefly in funding, that prevent wider application of such measures. This is an important consideration for defining and acting upon the opportunity for change created by a disaster. Furthermore, a broader examination of resilience in disaster risk management highlights that clarification is needed over what constitutes an appropriate response for community involvement in post-disaster infrastructure reconstruction. [ABSTRACT FROM PUBLISHER]

This paper proposes probabilistic damage and collapse models for reinforced concrete poles in electric power distribution networks and investigates the damage and collapse pattern of poles under earthquake excitations. To this end, detailed finite element models of the H-type reinforced concrete poles are developed and verified using past experimental studies as well as the observed damage in previous earthquakes. The models are then subjected to nonlinear static analyses to study the effect of the loading pattern, loading direction, concrete strength, and failure criteria on the capacity and the collapse pattern of the pole. Next, incremental dynamic analysis is carried out to investigate the sensitivity of the seismic response and collapse pattern of the pole to the direction of ground motion, record-to-record variability, and concrete strength. The results show that the damage pattern under static pull tests, which are the only type of the test conducted on such poles, poorly represent the seismic collapse pattern of the pole. The results also reveal that the most vulnerable segment of the pole is the first 0.5 m of the pole above the ground, which can guide the future retrofit strategies. The results of the incremental dynamic analysis are subsequently employed to develop damage and collapse fragility models for 9, 12, and 15 m long poles using the maximum likelihood method. The analysis accounts for the uncertainty not only in the ground motion but also in the material properties of poles. The proposed models make it possible to account for the damage incurred by the power distribution lines in the seismic risk analysis of the power distribution networks as well as the seismic resilience analysis of electrified communities. • Detailed finite element model of poles is developed and verified by real-world observations. • Effects of various parameters on the collapse pattern of poles is studied by sensitivity analysis. • It is shown that static pull tests poorly represent the seismic performance of poles. • Incremental dynamic analysis reveals the most vulnerable segment of poles. • Damage and collapse fragility models are proposed for risk and resilience analysis of power networks. [ABSTRACT FROM AUTHOR]

• Markov chains to hind cast model with 2014 South Napa earthquake recovery data. • Markov processes to update model with recovery data using Bayesian statistics. • Semi-Markov processes for single hazard recovery considering system damage. • Semi-Markov processes with non-renewal features for multihazard recovery. • Markov-type process and Bayesian network for system-of-systems recovery. State-dependent models can be used to represent the system recovery process as a series of stochastic transitions from lower to higher functional states. However, the applications of these models have been limited in scope and there is a lack of a generalized recovery modeling framework. A generalized framework would permit a robust forecasting of systems and system-of-systems recovery under multiple hazards, and more broadly, would contribute to community disaster preparedness. This paper develops a generalized post hazard-event recovery modeling framework based on state-dependent Markov-type processes. We then apply the proposed framework to solve a spectrum of problems that range from hind-casting single-system recovery following a single hazard event to forecasting post-event trajectories under multiple hazards and modeling the recovery of a system-of-systems. First, Markov chains are used to hind-cast the observed recovery for a portfolio of buildings affected by the 2014 South Napa, California, earthquake. Next, Markov processes are used to formulate a parametric post hazard-event recovery model, which can be updated using Bayesian statistics when relevant datasets become available. Semi-Markov processes are then used to develop a more general model of single hazard recovery, which accounts for the intensity of the loading and level of damage caused by the event. Semi-Markov processes with non-renewal features are then used to account for multihazard interactions in a post-event recovery model, and applied to a case study that involves a community in Charleston, South Carolina. Lastly, Markov-type processes are combined with Bayesian networks to model the recovery of residential, commercial, educational, and industrial buildings (system-of-systems) following a hazard event. These applications demonstrate the versatility of the Markov framework towards handling recovery problems with varying levels of complexity. [ABSTRACT FROM AUTHOR]

• Resilience of an infrastructure network can be improved by expediting the restoration process. • An interdependent infrastructure network can be modeled as a complex adaptive system. • Reinforcement learning can optimize the restoration process of a complex adaptive system. Extreme events can greatly impact the functionalities of infrastructure networks. The ability of an infrastructure network to restore its before-the-event functionality after the occurrence of an extreme event, being recognized as one of the most important aspects of resilience, is also one area where the resilience of the infrastructure network can be improved. To expedite the recovery of the infrastructure network after an extreme event, it is essential to allocate limited repair crews properly to disrupted infrastructure facilities. The loss and recovery of infrastructure functionalities are further complicated by infrastructure interdependencies, which can lead to the propagation of the impact of an extreme event to all infrastructure facilities in the network. The interdependent nature of infrastructure networks also complicates the process of optimally allocating repair crews and the effectiveness assessment of an allocation strategy. This paper proposes a methodological framework, by combining agent-based modeling and reinforcement learning, to assess the effectiveness of a repair crew allocation strategy and optimize the strategy after an extreme event, with which the impact of the extreme event can be better mitigated. [ABSTRACT FROM AUTHOR]

• System recovery under multihazards modeled using series of semi-Markov processes. • Model considers inter-event dependencies during the system recovery process. • Multihazard resilience of a residential building in Charleston, SC, is studied. • Considering inter-event dependencies leads to lesser-predicted resilience. • Reduction in resilience depends on intensities and arrival times of hazards. Civil infrastructure systems are subjected to multiple hazards, including natural and anthropogenic, that disrupt their function or the level of service offered. Estimating the function recovery of these systems (or how soon normalcy of operations will be restored) when subjected to repeated hazard events by considering the inter-event dependencies is an important problem in multihazard infrastructure resilience. However, this problem has been less addressed in the field. This paper proposes a series of semi-Markov processes model to capture the inter-event dependencies in infrastructure recovery when subjected to successive hazard events. Recovery after each new hazard event is represented by a unique semi-Markov process that models the reduced recovery rates and the increased recovery times caused by the system's incomplete recovery from the preceding event. Two novel formulations of the inter-event dependency modeling, namely Maximal Effects Dependency (considers the worst impact of two successive hazard events) and Cumulative Effects Dependency (considers the aggregated impacts of two successive hazard events), are proposed and discussed. The model is demonstrated by considering the following applications: Three-state system subjected to deterministic and random occurrences of identical hazard events; and Multihazard resilience of a building in Charleston, SC, considering earthquake and hurricane hazards. Results indicate that considering inter-event dependencies in recovery modeling can lead to lesser-predicted resilience, thereby affecting resilience-based decision-making. [ABSTRACT FROM AUTHOR]