Resilient Cities and Structures最新文献

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.

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.

Earthquake is one of the natural disasters that affects the buildings and communities in developing countries. It causes different levels of damages to the buildings, making them uninhabitable for a period of time, called downtime (DT). This paper proposes a Fuzzy Logic hierarchical method to estimate the downtime of residential buildings in developing countries after an earthquake. The use of expert-based systems allows quantifying the indicators involved in the model using descriptive knowledge instead of hard data, accounting also for the uncertainties that may affect the analysis. The applicability of the methodology is illustrated using the information gathered after the 2015 Gorkha, Nepal, earthquake as a case study. On April 25, 2015, Nepal was hit by the Mw 7.8 Gorkha earthquake, which damaged and destroyed more than 500.000 residential buildings. Information obtained from a Rapid Visual Damage Assessment (RVDA) is used through a hierarchical scheme to evaluate the building damageability. Sensitivity analysis based on Sobol method is implemented to evaluate the importance of parameters gathered in the RVDA for building damage estimation. The findings of this work may be used to estimate the restoration time of damaged buildings in developing countries and to plan preventive safety measures.

The nonductile reinforced concrete building (NDRCB) stock—typically, pre-1974 structures in the U.S.—is a well-known high-risk group for seismic hazards. Prior studies indicate that there are approximately 1500 NDRCBs in Los Angeles. Through various ordinances, the owners are currently required to choose between demolition and, when appropriate, seismic retrofitting. Because fulfilling these ordinances will take decades, the potential risk of major losses will persist. In this study, a method for automated development of structural analysis models and damage fragilities for non-ductile moment-resisting frames is established. This capability enables seismic risk assessment at a regional scale using relatively limited building metadata and the era-specific seismic design code. The approach is used first to develop archetypal models in OpenSees, verified through static pushover and nonlinear time-history analyses against prior detailed studies. Fragility curves for discrete damage states are developed through a probabilistic seismic demand model. Additional investigations are carried out to consider the influence of soil-structural interaction effects and to determine the most suitable seismic intensity measures to quantify the seismic damage levels of NDRCB frames. The sensitivity of the proposed modeling method to variations/uncertainties in building configurations and properties is also examined through parametric studies. The method is limited to a particular subcategory of NDRBCs—namely, moment-resisting frames—but extensions to other types appear straightforward.

Resilient infrastructure is critical to a sustainable and functioning society. Infrastructure management and (re)development are highly complex processes encompassing various stakeholders’ interests while they are pressured by the uncertainty of climate change and social transition. In response to these challenges, various resilience initiatives emerged with different motivations and approaches. The purpose of this research is to understand the interplay between motivations and organizational approaches as well as resilience conceptualization. This can provide insights into which domains of resilience have been focused on and what needs to be improved in their organizational approaches to realize motivations. This research specifically investigates ten resilient infrastructure initiatives in the Netherlands. By using scoping review and content analysis, our results highlight that resilience initiatives conceptualize resilience in different ways, mainly focusing on built and organizational resilience with a focus on long-term and wider geographic scope. Each initiative had several motivations, including 1) creating innovative solutions, 2) sharing knowledge, 3) promoting commitment and cooperation, and 4) promoting resilience. These motivations are reflected in the organizational approach. For example, there was a strong link between the motivation ‘creating shared knowledge’ and the organizational approach ‘research collaboration.’ Generic motivation such as ‘promoting resilience’ does not have one mainstreaming approach, which shows promoting resilience in practice is still in the exploration stage. This research provides major motivations and organizational approaches and their link within the resilient infrastructure initiatives which can contribute to better organizing similar initiatives aiming for resilient infrastructure.

Resilience assessment of transportation infrastructure is a crucial aspect of ensuring the continued functionality of a city or region in the face of various disruptions. However, these infrastructures are also vulnerable to various types of disruptions, such as natural disasters. The ability of transportation infrastructures to withstand and recover from such disruptions is referred to as their resilience. This research presents a comprehensive framework to develop the resilience surface for assessing the resilience of transportation infrastructure such as bridges, roads, and tunnels. The framework involves the identification of the unique damage configurations through performing the fragility analysis, and the restoration of the infrastructures through developing recovery curves for each damage configuration by considering the relevant restoration data. The framework also considers the inherent uncertainty in the hazard intensity, modeling uncertainty, and restoration process. The framework is illustrated through the application to a case study of a highway bridge in Canada. The aim of this paper is to provide a useful tool for decision-makers to evaluate and improve the resilience of transportation infrastructures.

Fire is one of the extreme loading events that a building may experience during its service life and can have severe consequences on the safety of its occupants, first responders, and the structure. Steel framed buildings under severe fires can experience high levels of instability at a local or global level, which in turn can lead to the partial or progressive collapse of the structure. However, in current practice, fire resistance of structures is obtained without due consideration to a number of critical factors, and this is mainly due to the high level of complexity in undertaking advanced analysis of structures under fire exposure. This paper presents a parametric study on a ten-story braced steel framed building subjected to fire exposure wherein six different parameters are evaluated: fire severity, fire spread, load paths, temperature-induced creep, local instability, and analysis regime. Results from validated finite element models are utilized to evaluate the influence of the different parameters and recommend critical parameters to be incorporated in the analysis. Results show that the susceptibility of fire-induced progressive collapse significantly depends on the severity of the fire exposure scenario, including fire intensity, fire spread, and extent of burning. Also, accounting for the full effects of transient creep in fire-induced progressive collapse analysis is needed to obtain conservative failure times under severe to very intense fire exposure. Additionally, results from the parametric study infer that the sectional classification of a steel section based on local instability can alter under fire exposure and this effect is more critical in steel columns located in the higher stories of the building; a nonslender column at ambient conditions can transform to a slender section at elevated temperatures. This can induce temperature-induced local instability in the column and lead to an early onset of instability at member and structural levels.

This study proposes a concept for the development of a fireproof design code for the verification of dwellings susceptible to wildfire action. There are currently structural codes for the design of buildings when subjected to indoor fires, outside fires that grow on the facade of buildings, and even fires in an accident situation due to ignitions with hydrocarbons or transportation vehicles. All of these security verification specifications are described in EC1:1–2. The current regulation in Portugal uses safety criteria and risk categories that are for indoor fires, therefore is very conservative and may not present an economic fireproof design against the action of wildfires. The aim of this work is a straight verification based on natural temperature characteristic curves that simulate wildfire heat flow by convection, radiation, and the deposits of firebrands.