Resilient Cities and Structures最新文献

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.

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.

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.

The multi-disciplinary data and information available at a community level comprise the foundation of natural hazard resilience modeling. These data enable and inform mitigation and recovery planning decisions prior to and following damaging events such as earthquakes. This paper presents a multi-disciplinary seismic resilience modeling methodology to assess the vulnerability of the built environment and economic systems. This methodology can assist decision-makers with developing effective mitigation policies to improve the seismic resilience of communities. Two complementary modeling strategies are designed to examine the impacts of scenario earthquakes from a combined engineering and economic perspective. The engineering model is developed using a probabilistic fragility-based modeling approach and is analyzed using Monte Carlo (MC) simulations subject to seismic multi-hazard, including simulated ground shaking and resulting liquefaction of the soil, to quantify the physical damage to buildings and electric power substations (EPS). The outcome of the analysis is subsequently used as input to repair and recovery models to quantify repair cost and recovery time metrics for buildings and as input to functionality models to estimate the functionality of individual buildings and substations by accounting for their interdependency. The economic model consists of a spatial computable general equilibrium (SCGE) model that aggregates commercial buildings into sectors for retail, manufacturing, services, etc., and aggregates residential buildings into a wide range of household groups. The SCGE model employs building functionality estimates to quantify the economic losses. The outcomes of this integrated modeling consist of engineering and economic impact metrics, which are used to investigate mitigation actions to help inform a community on approaches to achieve its resilience goals. An illustrative case study of Salt Lake County (SLC), Utah, developed through an extensive collaborative partnership and engagement with SLC officials, is presented. The results demonstrate the effectiveness of the proposed methodology in quantifying the loss and functional recovery of infrastructure systems, the impacts on capital stock, employment, and household income and the effect of various mitigation strategies in reducing the losses and functional recovery time subject to earthquakes with varying intensities.

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.

This study introduces an advanced community-level resilience analysis methodology integrating 3D fragility surfaces for combined successive earthquake-tsunami hazard and analysis. The methodology facilitates comprehensive evaluations of spatial damage, economic loss, and risk under multi-hazard conditions. This study compares earthquake-only analysis results to the successive earthquake-tsunami analysis at the community level to reveal – and quantify - significant disparities in damage and loss estimations between the analyses, emphasizing the need to consider both hazards in community planning even at lower seismic intensities. Critical assessment of the FEMA combinational rule demonstrates its limitations in accurately predicting losses and damage patterns at higher hazard intensities, highlighting the necessity for refined models that accurately account for hazard interactions. This research advances multi-hazard community-level resilience analysis by offering a robust framework for earthquake and tsunami assessment, underscoring the need for integration of detailed multi-hazard analyses into resilience planning. Finally, it suggests future directions for enhancing framework applicability across diverse community settings and structural types, aiming to improve community resilience.

Records of wave-induced damage on coastal bridges during natural hazards have been well documented over the past two decades. It is of utmost importance to decipher the loading mechanism and enhance the resilience of coastal bridges during extreme wave-inducing events. Quantification of vulnerability of these structures is an essential step in designing a resilient bridge system. Recently, considerable efforts have been made to study the force applied and the response of coastal bridge systems during extreme wave loading conditions. Although remarkable progress can be found in the quantification of load and response of coastal superstructures, very few studies assessed coastal bridge resiliency against extreme wave-induced loads. This paper adopts a simplified and practical technique to analyze and assess the resilience of coastal bridges exposed to extreme waves. Component-level and system-level fragility analyses form the basis of the resiliency analysis where the recovery functions are adopted based on the damage levels. It is shown that wave period has the highest contribution to the variation of bridge resiliency. Moreover, this study presents the uncertainty quantification in resiliency variation due to changes in wave load intensity. Results show that the bridge resiliency becomes more uncertain as the intensity of wave parameters increases. Finally, possible restoration strategies based on the desired resilience level and the attitude of decision-makers are also discussed.

Traditionally, nonlinear time history analysis (NLTHA) is used to assess the performance of structures under future hazards which is necessary to develop effective disaster risk management strategies. However, this method is computationally intensive and not suitable for analyzing a large number of structures on a city-wide scale. Surrogate models offer an efficient and reliable alternative and facilitate evaluating the performance of multiple structures under different hazard scenarios. However, creating a comprehensive database for surrogate modelling at the city level presents challenges. To overcome this, the present study proposes meta databases and a general framework for surrogate modelling of steel structures. The dataset includes 30,000 steel moment-resisting frame buildings, representing low-rise, mid-rise and high-rise buildings, with criteria for connections, beams, and columns. Pushover analysis is performed and structural parameters are extracted, and finally, incorporating two different machine learning algorithms, random forest and Shapley additive explanations, sensitivity and explainability analyses of the structural parameters are performed to identify the most significant factors in designing steel moment resisting frames. The framework and databases can be used as a validated source of surrogate modelling of steel frame structures in order for disaster risk management.

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.