Resilient Cities and Structures最新文献

This paper proposes a set of nonparametric statistical tools for analyzing the system resilience of civil structures and infrastructure and its migration upon changes in critical system parameters. The work is founded on the classic theoretic framework that system resilience is defined in multiple dimensions for a constructed system. Consequentially, system resilience can lose its parametric form as a random variable, falling into the realm of nonparametric statistics. With this nonparametric shift, traditional distribution-based statistics are ineffective in characterizing the migration of system resilience due to the variation of system parameters. Three statistical tools are proposed under the nonparametric statistical resilience analysis (npSRA) framework, including nonparametric copula-based sensitivity analysis, two-sample resilience test analysis, and a novel tool for resilience attenuation analysis. To demonstrate the use of this framework, we focus on electric distribution systems, commonly found in many urban, suburban, and rural areas and vulnerable to tropical storms. A novel procedure for considering resourcefulness parameters in the socioeconomic space is proposed. Numerical results reveal the complex statistical relations between the distributions of system resilience, physical aging, and socioeconomic parameters for the power distribution system. The proposed resilience distance computing and resilience attenuation analysis further suggests two proper nonparametric distance metrics, the Earth Moving Distance (EMD) metric and the Cramévon Mises (CVM) metric, for characterizing the migration of system resilience for electric distribution systems.

The community's resilience in the face of natural hazards relies heavily on the rapid and efficient restoration of electric power networks, which plays a critical role in emergency response, economic recovery, and the functionality of essential lifeline and social infrastructure systems. Leveraging the recent data revolution, the digital twin (DT) concept emerges as a promising tool to enhance the effectiveness of post-disaster recovery efforts. This paper introduces a novel framework for post-hurricane electric power restoration using a hybrid DT approach that combines physics-based and data-driven models by utilizing a dynamic Bayesian network. By capturing the complexities of power system dynamics and incorporating the road network's influence, the framework offers a comprehensive methodology to guide real-time power restoration efforts in post-disaster scenarios. A discrete event simulation is conducted to demonstrate the proposed framework's efficacy. The study showcases how the electric power restoration DT can be monitored and updated in real-time, reflecting changing conditions and facilitating adaptive decision-making. Furthermore, it demonstrates the framework's flexibility to allow decision-makers to prioritize essential, residential, and business facilities and compare different restoration plans and their potential effect on the community.

The swift recuperation of communities following natural hazards heavily relies on the efficiency of transportation systems, facilitating the timely delivery of vital resources and manpower to reconstruction sites. This paper delves into the pivotal role of transportation systems in aiding the recovery of built environments, proposing an evaluative metric that correlates transportation capacity with the speed of post-earthquake recovery. Focusing on optimizing urban population capacity in the aftermath of earthquakes, the study comprehensively examines the impact of pre-earthquake measures such as enhancing building or bridge seismic performance on post-earthquake urban population capacity. The methodology is demonstrated through an analysis of Beijing’s transportation system, elucidating how enhancements to transportation infrastructure fortify the resilience of built environments. Additionally, the concept of a resource supply rate is introduced to gauge the level of logistical support available after an earthquake. This rate tends to decrease when transportation damage is significant or when the demands for repairs overwhelm available resources, indicating a need for retrofitting. Through sensitivity analysis, this study explores how investments in the built environment or logistical systems can increase the resource supply rate, thereby contributing to more resilient urban areas in the face of seismic challenges.

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 services 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 dynamics 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 regardless 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 functionality 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 ongoing 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 population 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.

Mixed Reality (MR) Head Mounted Displays (HMDs) offer a hitherto underutilized set of advantages compared to conventional 3D scanners. These benefits, inherent to MR-HMDs albeit not originally intended for such applications, encompass the freedom of hand movement, hand tracking capabilities, and real-time mesh visualization. This study leverages these attributes to enhance indoor scanning process. The primary innovation lies in the conceptualization of manual-positioned MR virtual seeds for the purpose of indoor point cloud segmentation via a region-growing approach. The proposed methodology is effectively implemented using the HoloLens 2 platform. An application is designed to enable the remote placement of virtual tags based on the user's visual focus on the MR-HMD display. This non-intrusive interface is further enriched with expedited tag saving and deletion functionalities, as well as augmented tag visualization through overlaying them on real-world objects. To assess the practicality of the proposed method, a comprehensive real-world case study spanning an area of 330 s² is conducted. Remarkably, the survey demonstrates remarkable efficiency, with 20 virtual tags swiftly deployed, each requiring a mere 2 s for precise positioning. Subsequently, these virtual tags are employed as seeds in a region-growing algorithm for point cloud segmentation. The accuracy of virtual tag positioning is found to be exceptional, with an average error of 2.4 ± 1.8 cm. Importantly, the user experience is significantly enhanced, leading to improved seed positioning and, consequently, more accurate final segmentation results.

Lifelines are critical infrastructure systems characterized by a high level of interdependency that can lead to cascading failures after any disaster. Many approaches can be used to analyze infrastructural interdependencies, but they are usually not able to describe the sequence of events during emergencies. Therefore, interdependencies need to be modeled also taking into account the time effects. The methodology proposed in this paper is based on a modified version of the Input-output Inoperability Model and returns the probabilities of failure for each node of the system. Lifelines are modeled using graph theory, while perturbations, representing a natural or man-made disaster, are applied to the elements of the network following predetermined rules. The cascading effects among interdependent networks have been simulated using a spatial multilayer approach, while the use of an adjacency tensor allows to consider the temporal dimension and its effects. The method has been tested on a case study based on the 2011 Fukushima Dai-ichi nuclear disaster. Different configurations of the system have been analyzed and their probability of occurrence evaluated. Two models of the nuclear power plant have been developed to evaluate how different spatial scales and levels of detail affect the results.

Probabilistic seismic performance assessment method for buildings offers a valuable approach to simulate the broader regional impacts: economic losses, downtime, and casualties. A crucial aspect of this process entails accounting for the spatial correlation of building performances, aiming for an accurate estimation of the probability of extreme regional losses, such as the simultaneous collapse of buildings with similar structural characteristics. In this study, a correlation model based on a Gaussian random field is employed, and several key challenges associated with its application are addressed. In addition, efficiency of five different methods of selecting station records from the same earthquake scenario is compared. The minimum number of earthquake records necessary to achieve a stable correlation result is determined. Additionally, spatial correlations derived from different history earthquake events are compared. By addressing these critical issues, this research contributes to refining the reliability of probabilistic methods for regional resilience assessment.

Construction of disaster-resilient cities has attracted considerable attention. However, traditional methods of studying urban disaster resilience through experimental approaches are often constrained by various limitations, such as testing sites, costs and ethical considerations. To address these constraints, this paper proposes incorporating digital twin concepts into urban disaster resilience research. By establishing a connection between the physical realm of the city and its virtual counterpart, this approach utilizes digital simulations to overcome the limitations of experimental methods and enables dynamic deduction and control of the disaster process. This paper delves into three key aspects encompassing the acquisition of data from reality to the virtual space, disaster simulation within the virtual space, and translation of virtual insights into effective disaster prevention strategies in reality. It provides a comprehensive summary of relevant research endeavors from the authors’ research group and showcases the effectiveness and potential of the proposed techniques. These findings serve as references for pre-disaster planning, real-time emergency assessments, post-disaster rescue operations, and accident investigations for buildings and cities.

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.