Resilient Cities and Structures最新文献

The time-dependent resilience of an in-service aging structure provides quantitative measure of the structural ability to prepare for, adapt to, withstand and recover from disruptive events. Resilience models have been proposed in the literature to evaluate the resilience of aging structures subjected to discrete load processes, which are, however, not applicable to handle resilience problems considering continuous load processes. In this paper, a new method is developed to evaluate the time-dependent resilience of aging structures subjected to a continuous load process. The proposed method serves as the complement of the existing resilience models addressing discrete load processes, and takes into account the aging effects of the structural resistance/capacity and the nonstationarity in loads as a result of climate change. A structure suffers from a damage state upon the occurrence of an upcrossing of the load effect with respect to the resistance/capacity, leading to the reduction of the performance function, followed by a recovery process that restores the performance. The proposed method enables the time-dependent resilience to be evaluated via a closed form solution. It is also revealed that, the proposed resilience model takes an extended form of the existing formula for upcrossing-based time-dependent reliability, thus establishing a unified framework for the two quantities. The applicability of the proposed method is demonstrated through examining the time-dependent resilience of a residential building subjected to wind load. The effects of key factors on resilience, including the nonstationarity and correlation structure of the load process, as well as the resistance/capacity deterioration scenario, are investigated through an example. In particular, the structural resilience would be overestimated if ignoring the potential impacts of climate change, which is a relatively non-conservative evaluation.

Critical Infrastructures (CIs), which serve as the foundation of our modern society, are facing increasing risks from cyber threats, physical attacks, and natural disasters. Additionally, the interdependencies between CIs throughout their operational lifespan can also significantly impact their integrity and safety. As a result, enhancing the resilience of CIs has emerged as a top priority for many countries, including the European Union. This involves not only understanding the threats/attacks themselves but also gaining knowledge about the areas and infrastructures that could potentially be affected. A European Union-funded project named PRECINCT (Preparedness and Resilience Enforcement for Critical INfrastructure Cascading Cyber-Physical Threats), under the Horizon 2020 program, tries to connect private and public stakeholders of CIs in a specific geographical area. The key objective of this project is to establish a common cyber-physical security management approach that will ensure the protection of both citizens and infrastructures, creating a secure territory. This paper presents the components of PRECINCT, including a directory of PRECINCT Critical Infrastructure Protection (CIP) blueprints. These blueprints support CI communities in designing integrated ecosystems, operating and replicating PRECINCT components (or toolkits). The integration enables coordinated security and resilience management, incorporating improved 'installation-specific' security solutions. Additionally, Serious Games (SG), and Digital Twins (DT) are a significant part of this project, serving as a novel vulnerability evaluation method for analysing complicated multi-system cascading effects in the PRECINCT Living Labs (LLs). The use of SG supports the concentrated advancement of innovative resilience enhancement services.

Adequacy of structural fire design in uncommon structures is conceptually ensured through cost-benefit analysis where the future costs are balanced against the benefits of safety investment. Cost-benefit analyses, however, are complicated and computationally challenging, and hence impractical for application to individual projects. To address this issue, design guidance proposes target reliability indices for normal design conditions, but no target reliability indices are defined for structural fire design. We revisit the background of the cost-optimization based approach underlying normal design target reliability indices then we extend this approach for the case of fire design of structures. We also propose a modified objective function for cost-optimization which simplifies the evaluation of target reliability indices and reduces the number of assumptions. The optimum safety level is expressed as a function of a new dimensionless variable named “Damage-to-investment indicator” (DII). The cost optimization approach is validated for the target reliability indices for normal design condition. The method is then applied for evaluating DII and the associated optimum reliability indices for fire-exposed structures. Two case studies are presented: (i) a one-way loaded reinforced concrete slab and (ii) a steel column under axial loading. This study thus provides a framework for deriving optimum (target) reliability index for structural fire design which can support the development of rational provisions in codes and standards.

A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions. The effect of the hazard geometry (center and angle of tornado path as well as the tornado width) is studied herein on how it influences the recovery of physical and social systems within the community. Given that pre-disaster preparedness including mitigation strategies (e.g., retrofits) and policies (e.g., insurance) is crucial for increasing the resilience of the community and facilitating a faster recovery process, in this study, the impact of various mitigation strategies and policies on the recovery trajectory and resilience of a typical US community subjected to a tornado is investigated considering different sources of uncertainties. The virtual testbed of Centerville is selected in this paper and is modeled by adopting the Agent-based modeling (ABM) approach which is a powerful tool for conducting community resilience analysis that simulates the behavior of different types of agents and their interactions to capture their interdependencies. The results are presented in the form of recovery time series as well as calculated resilience indices for various community systems (lifeline networks, schools, healthcare, businesses, and households). The results of this study can help deepen our understanding of how to efficiently expedite the recovery process of a community.

In the last decade, the detection and attribution science that links climate change to extreme weather and climate events has emerged as a growing field of research with an increasing body of literature. This paper overviews the methods for extreme event attribution (EEA) and discusses the new insights that EEA provides for infrastructure adaptation. We found that EEA can inform stakeholders about current climate risk, support vulnerability-based and hazard-based adaptations, assist in the development of cost-effective adaptation strategies, and enhance justice and equity in the allocation of adaptation resources. As engineering practice shifts from a retrospective approach to a proactive, forward-looking risk management strategy, EEA can be used together with climate projections to enhance the comprehensiveness of decision making, including planning and preparing for unprecedented extreme events. Additionally, attribution assessment can be more useful for adaptation planning when the exposure and vulnerability of communities to past events are analyzed, and future changes in the probability of extreme events are evaluated. Given large uncertainties inherent in event attribution and climate projections, future research should examine the sensitivity of engineering design to climate model uncertainties, and adapt engineering practice, including building codes, to uncertain future conditions. While this study focuses on adaptation planning, EEA can also be a useful tool for informing and enhancing decisions related to climate mitigation.

This study examines the feasibility of using a machine learning approach for rapid damage assessment of reinforced concrete (RC) buildings after the earthquake. Since the real-world damaged datasets are lacking, have limited access, or are imbalanced, a simulation dataset is prepared by conducting a nonlinear time history analysis. Different machine learning (ML) models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories: null, slight, moderate, heavy, and collapse. The random forest classifier (RFC) has achieved a higher prediction accuracy on testing and real-world damaged datasets. The structural parameters can be extracted using different means such as Google Earth, Open Street Map, unmanned aerial vehicles, etc. However, recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher cost. For places with no earthquake recording station/device, it is difficult to have ground motion characteristics. For that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground velocity. The random forest regressor (RFR) achieved better results than other regression models on testing and validation datasets. Furthermore, compared with the results of similar research works, a better result is obtained using RFC and RFR on validation datasets. In the end, these models are utilized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi, Saitama, Japan after an earthquake. This damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.

This paper evaluates literature across multiple disciplines and stakeholder types to identify commonalities and contradictions in definitions for community resilience. It aims to support cross-disciplinary discourse to build an interdisciplinary understanding of community resilience. This work identifies the differences between mono-, multi-, inter-, and cross-disciplinary approaches to inform community resilience strategies in academic and practice-based contexts.

Four themes for community resilience were identified through a review of cross-disciplinary literature. These include (1) diverse yet convergent definitions of community resilience and the evolution from equilibrium to adaptation to transformation; (2) equitable and inclusive strategies for the development of community resilience initiatives; (3) when and at what scale strategies should be implemented; and (4) community resilience as a process or an outcome.

This work is valuable to those seeking to familiarise themselves with the concept of community resilience, including educators who deliver courses on community resilience and policy-makers. It is novel in that it presents an interdisciplinary framework for navigating the community resilience discourse beyond individual professional boundaries.

An accurate estimation of wind loads on telecommunication towers is crucial for design, as well as for performing reliability, resilience, and risk assessments. In particular, drag coefficient and interference factor are the most significant factors for wind load computations. Wind tunnel tests and computational fluid dynamics (CFD) are the most appropriate methods to estimate these parameters. While wind tunnel tests are generally preferred in practice, they require dedicated facilities and personnel, and can be expensive if multiple configurations of tower panels and antennas need to be tested under various wind directions (e.g., fragility curve development for system resilience analysis). This paper provides a simple, robust, and easily accessible CFD protocol with widespread applicability, offering a practical solution in situations where wind tunnel testing is not feasible, such as complex tower configurations or cases where the cost of running experiments for all the tower-antennas configurations is prohibitively high. Different turbulence models, structural and fluid boundary conditions and mesh types are tested to provide a streamlined CFD modeling strategy that shows good convergence and balances accuracy, computational time, and robustness. The protocol is calibrated and validated with experimental studies available in the literature. To demonstrate the capabilities of the protocol, three lattice tower panels and antennas with different configurations are analyzed as examples. The protocol successfully estimates the drag and lateral wind loads and their coefficients under different wind directions. Noticeable differences are observed between the estimated wind loads with this protocol and those computed by a simple linear superposition used in most practical applications, indicating the importance of tower-antenna interaction. Also, as expected, the wind loads recommended by design codes overestimate the simulated results. More importantly, the telecommunication design codes inadequately identify the most favorable wind directions that are associated with the lowest wind loads, while the results of the proposed protocol align with observations from experimental studies. This information may be used to select the tower orientation before construction. The findings of this study are of importance for the telecommunication industry, which seeks reliable results with minimal computational efforts. In addition, it enhances the fragility analysis of telecommunication towers under strong winds, and the portfolio risk and resilience assessment of telecommunication systems.

Natural hazards impact interdependent infrastructure networks that keep modern society functional. While a variety of modelling approaches are available to represent critical infrastructure networks (CINs) on different scales and analyse the impacts of natural hazards, a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data. The resulting data gaps often require modellers to assume specific technical parameters, functional relationships, and system behaviours. In other cases, expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps. The uncertainties introduced by these assumptions and extrapolations and their influence on the quality of modelling outcomes are often poorly understood and difficult to capture, thereby eroding the reliability of these models to guide resilience enhancements. Additionally, ways of overcoming the data availability challenges in CIN modelling, with respect to each modelling purpose, remain an open question. To address these challenges, a generic modelling workflow is derived from existing modelling approaches to examine model definition and validations, as well as the six CIN modelling stages, including mapping of infrastructure assets, quantification of dependencies, assessment of natural hazard impacts, response & recovery, quantification of CI services, and adaptation measures. The data requirements of each stage were systematically defined, and the literature on potential sources was reviewed to enhance data collection and raise awareness of potential pitfalls. The application of the derived workflow funnels into a framework to assess data availability challenges. This is shown through three case studies, taking into account their different modelling purposes: hazard hotspot assessments, hazard risk management, and sectoral adaptation. Based on the three model purpose types provided, a framework is suggested to explore the implications of data scarcity for certain data types, as well as their reasons and consequences for CIN model reliability. Finally, a discussion on overcoming the challenges of data scarcity is presented.

Losses due to hazards are inevitable and numerical simulations for estimations are complex. This study proposes a model for estimating correlated seismic damages and losses of a water supply pipeline system as an alternative for numerical simulations. The common approach in other research shows average damage spots per mesh estimated statistically independent to one another. Spatially distributed lifeline systems, such as water supply pipelines, are interconnected, and seismic spatial variability affects the damages across the region; thus, spatial correlation of damage spots is an important factor in target areas for portfolio loss estimation. Generally, simulations are used to estimate possible losses; however, these assume each damage behaves independently and uncorrelated. This paper assumed that damages per mesh behave in a Poisson distribution to avoid over-dispersion and eliminate negative losses in estimations. The purpose of this study is to obtain a probabilistic portfolio loss model of an extensive water supply area. The proposed model was compared to the numerical simulation data with the correlated Poisson distribution. The application of the Normal To Anything (NORTA) obtained correlations for Poisson Distributions. The proposed probabilistic portfolio loss model, based on the generalized linear model and central limit theory, estimated the possible losses, such as the Probable Maximum Loss (PML, 90% non-exceedance) or Normal Expected Loss (NEL, 50 % non-exceedance). The proposed model can be used in other lifeline systems as well, though additional investigation is needed for confirmation. From the estimations, a seismic physical portfolio loss for the water supply system was presented. The portfolio was made to show possible outcomes for the system. The proposed method was tested and analyzed using an artificial field and a location-based scenario of a water supply pipeline system. This would aid in pre-disaster planning and would require only a few steps and time.