Resilient Cities and Structures最新文献

Experience from past earthquakes has demonstrated the need to account for design goals beyond safety, known as functional recovery objectives, in the interest of community resilience. Frameworks have been proposed in the literature to assess the post-earthquake functional recovery of a building, but without accounting for utility systems’ disruption, which may be a key contributor to determining when a building is functional. This paper integrates a previously proposed probabilistic method for estimating the post-earthquake restoration of critical utility services with an individual building's functional recovery assessment framework. The integration was performed by incorporating utilities into the building system fault trees embedded into a functional recovery framework for various building occupancies (residential and commercial office buildings). Once incorporated, the results are used to interrogate the functional recovery of a reinforced concrete building, and the recovery time results were presented for seven cases investigating contributing factors in the functional recovery results including the number of crews available for lifeline restoration, the effect of low-quality service on meeting tenant requirements for elevators, heating ventilation and air conditioning (HVAC), plumbing and electrical systems, consideration of fire watch, the effect of building seismic retrofit, as well as different cases of fragility functions for the lifeline systems. Results showed that utility systems’ disruption does not have a significant impact on the recoccupancy of a building because only one utility-dependent building system (fire suppression) is needed for the building's safety. Unlike reoccupancy, utility systems are significant for functional recovery, mainly at moderate hazard levels because, at these levels, lifeline networks could be damaged without significant building damage, such that the lifeline systems restoration governs. Buildings with more restrictive tenant requirements are more sensitive to tenant disruptions.

Communities depend on critical infrastructure systems to support their regular operations and future development. Destructive events, such as natural disasters, threaten to disrupt service to these systems and the communities they support. Strategies designed to reduce the impacts from disasters and other events are therefore an important consideration for community planning. At a regional level, coordination between communities supports the efficient use of resources for implementing disaster risk reduction (DRR) measures and completing post-disaster repairs to meet the needs of all residents. Coordination is challenging, however, due to the complexity of regional systems and competing stakeholder interests. This work presents a case study model of regional water, wastewater, and power systems, and demonstrates the effect of seismic hardening and increased resource availability on post-earthquake repair requirements and critical infrastructure recovery. Model results indicate that implementing DRR strategies can reduce required repair costs by over 40 percent and outage severity by approximately 50 percent for the studied sectors. Not all strategies are effective for all sectors and locations, however, so this work discusses the importance of comprehensive, coordinated, and accessible emergency planning activities to ensure that the needs of all residents are considered.

Despite efforts to end homelessness in the United States, student homelessness is gradually growing over the past decade. Homelessness creates physical and psychological disadvantages for students and often disrupts school access. Research suggests that students who experience prolonged dislocation and school disruption after a disaster are primarily from low-income households and under-resourced areas. This study develops a framework to predict post-disaster trajectories for kindergarten through high school (K-12) students faced with a major disaster; the framework includes an estimation on the households with children who recover and those who experience long-term homelessness. Using the National Center for Education Statistics school attendance boundaries, residential housing inventory, and U.S. Census data, the framework first identifies students within school boundaries and links schools to students to housing. The framework then estimates dislocation induced by the disaster scenario and tracks the stage of post-disaster housing for each dislocated student. The recovery of dislocated students is predicted using a multi-state Markov chain model, which captures the sequences that households transition through the four stages of post-disaster housing (i.e., emergency shelter, temporary shelter, temporary housing, and permanent housing) based on the social vulnerability of the household. Finally, the framework predicts the number of students experiencing long-term homelessness and maps the students back to their pre-disaster school. The proposed framework is exemplified for the case of Hurricane Matthew-induced flooding in Lumberton, North Carolina. Findings highlight the disparate outcomes households with children face after major disasters and can be used to aid decision-making to reduce future disaster impacts on students.

Hurricane-induced hazards can result in significant damage to the built environment cascading into major impacts to the households, social institutions, and local economy. Although quantifying physical impacts of hurricane-induced hazards is essential for risk analysis, it is necessary but not sufficient for community resilience planning. While there have been several studies on hurricane risk and recovery assessment at the building- and community-level, few studies have focused on the nexus of coupled physical and social disruptions, particularly when characterizing recovery in the face of coastal multi-hazards. Therefore, this study presents an integrated approach to quantify the socio-physical disruption following hurricane-induced multi-hazards (e.g., wind, storm surge, wave) by considering the physical damage and functionality of the built environment along with the population dynamics over time. Specifically, high-resolution fragility models of buildings, and power and transportation infrastructures capture the combined impacts of hurricane loading on the built environment. Beyond simulating recovery by tracking infrastructure network performance metrics, such as access to essential facilities, this coupled socio-physical approach affords projection of post-hazard population dislocation and temporal evolution of housing and household recovery constrained by the building and infrastructure recovery. The results reveal the relative importance of multi-hazard consideration in the damage and recovery assessment of communities, along with the role of interdependent socio-physical system modeling when evaluating metrics such as housing recovery or the need for emergency shelter. Furthermore, the methodology presented here provides a foundation for resilience-informed decisions for coastal communities.

‘Planning Emergency Levels of Service’ (PELOS) are service delivery goals for infrastructure providers during and after an emergency event. These goals could be delivered through the existing infrastructure (e.g., pipes, lines, cables), or through other means (trucked water or the provision of generators). This paper describes how an operationalised framework of PELOS for the Wellington region, New Zealand was created, alongside the key stakeholders. We undertook interviews and workshops with critical infrastructure entities to create the framework. Through this process we found five themes that informed the context and development of the PELOS framework: interdependencies between critical infrastructure, the need to consider the vulnerabilities of some community members, emergency planning considerations, stakeholders’ willingness to collaborate on this research/project and the flexibility/adaptability of the delivery of infrastructure services following a major event. These themes are all explored in this paper. This research finds that the understanding of the hazardscape and potential outages from hazards is critical and that co-ordination between key stakeholders is essential to create such a framework. This paper may be used to inform the production of PELOS frameworks in other localities.

The role of community building portfolios in socioeconomic development and the growth of the built environment cannot be overstated. Damage to these structures can have far-reaching consequences on socioeconomic and environmental aspects, requiring a long-term perspective for recovery. As communities aim to enhance their resilience and sustainability, there is a cost burden that needs to be considered. To address this issue, this paper proposes a community-level performance enhancement approach that focuses on optimizing the long-term resilience and sustainability of community building portfolios, taking into account recurrent seismic hazards. A Gaussian process surrogate-based multi-objective optimization framework is utilized to optimize the cost objective while considering performance indicators for resilience and sustainability. The proposed framework involves using performance-based assessment methods to evaluate the socioeconomic and environmental consequences under stochastic and recurrent seismic hazard scenarios. These evaluated indicators are then used to efficiently optimize the community resilience and sustainability, taking into account the retrofit costs. Finally, approximate Pareto-optimal solutions are extracted and utilized for decision-making. In summary, this paper presents a novel approach for optimizing the long-term resilience and sustainability of community building portfolios by considering recurrent seismic hazards. The proposed framework incorporates performance-based assessment methods and multi-objective optimization techniques to achieve an optimal balance between cost, resilience, and sustainability, with the ultimate goal of enhancing community well-being and decision-making in the face of seismic hazards.

In 2015, the U.S National Institute of Standards and Technology (NIST) funded the Center of Excellence for Risk-Based Community Resilience Planning (CoE), a fourteen university-based consortium of almost 100 collaborators, including faculty, students, post-doctoral scholars, and NIST researchers. This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE - the Interdisciplinary Networked Community Resilience Modeling Environment (IN-CORE). IN-CORE enables communities, consultants, and researchers to set up complex interdependent models of an entire community consisting of people, businesses, social institutions, buildings, transportation networks, water networks, and electric power networks and to predict their performance and recovery to hazard scenario events, including uncertainty propagation through the chained models. The modeling environment includes a detailed building inventory, hazard scenario models, building and infrastructure damage (fragility) and recovery functions, social science data-driven household and business models, and computable general equilibrium (CGE) models of local economies. An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.

Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population, economics, physical services, and social services. An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas (CSA) that encompass an array of community resilience metrics (CRM) and support community resilience informed decision-making. Each testbed within IN-CORE has been developed by a team of engineers, social scientists, urban planners, and economists. Community models, begin with a community description, i.e., people, businesses, buildings, infrastructure, and progresses to the damage and loss of functions caused by a hazard scenario, i.e., a flood, tornado, hurricane, or earthquake. This process is accomplished through chaining of modular algorithms, as described. The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models, which have been the least studied area of community resilience but arguably one of the most important. Communities can then test the effect of mitigation and/or policies and compare the effects of “what if” scenarios on physical, social, and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.

Extreme weather conditions can adversely impact transport networks and driver behaviour, leading to variations in traffic volumes and travel times and increased accident rates. Emergency services that need to navigate to an accident site in the shortest possible time require real-time location-based weather and traffic information to coordinate their response.

We therefore require historical and high-resolution temporal real-time data to identify districts and roads that are prone to different types of incidents during inclement weather and to better support emergency services in their decision-making. However, real-time assessment of the current transport network requires a dense sensor network that can provide high-resolution data using internet-enabled technology.

In this research, we demonstrate how we obtain historical time-series and real-time data from sensors operated by the Tyne and Wear Urban Traffic and Management Control Centre and the Urban Observatory based at Newcastle upon Tyne, UK. In the study, we assess the impact of rainfall on traffic volume and travel time, and the cascading impacts during a storm event in Newcastle during early October 2021. We also estimate the economic cost of the storm, with regards to transport disruption, as the cost of travel, using the “value of time” based on Department for Transport guidelines (2021).

Using spatial-temporal analysis, we chose three locations to demonstrate how traffic parameters varied at different times throughout the storm. We identified increases in travel times of up to 600% and decreases in traffic volume of up to 100% when compared to historical data. Further, we assessed cascading impacts at important traffic locations and their broader implications for city areas. We estimated that the storm's economic impact on one sensor location increased by up to 370% of the reference value.

By analysing historical and real-time data, we detected and explained patterns in the data that would have remained uncovered if they had been examined individually. The combination of different data sources, such as traffic and weather, helps explain temporal fluctuations at locations where incidents were recorded near traffic detectors.

We anticipate our study to be a starting point for stakeholders involved in incident response to identify bottleneck locations in the network to help prepare for similar future events.

Virtual community resilience testbeds enable community-level inferences, convergence research, and serve as decision-making aids. Testbeds are critical for the verification and validation of emerging computational models and quantitative assessment frameworks of community-level disaster impacts, disruption, and recovery processes. This paper illuminates the significance of establishing a standardized approach for developing virtual community resilience testbeds and proposes a systematic schema for this purpose. The workflow facilitates testbed development by defining a series of steps, starting with specifying the testbed simulation scope. Arguing hazard and community modules are the principal components of a testbed, we present a generic structure for testbeds and introduce minimum requirements for initiating each module. The workflow dissects the testbed's architecture and different attributes of the components beneath these modules. The proposed steps outline existing relevant tools and resources for creating the building, infrastructure, population, organization, and governance inventories. The paper discusses challenges testbed developers may encounter in procuring, cleaning, and merging required data and offers the initiatives and potential remedies, developed either by the authors or other researchers, to address these issues. The workflow concludes by describing how the testbed will be verified, visualized, published, and reused. The paper demonstrates the application of the proposed workflow by developing a testbed based on Onslow County, North Carolina using publicly available data. To foster sharing and reusing of developed testbeds by other researchers, all supporting documents, metadata, template algorithms, computer codes, and inventories of the Onslow Testbed are available at the DesignSafe-CI. The procedure proposed here can be used by other researchers to guide and standardize testbed development processes, and open access to virtual testbeds to the broader research community.