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.

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.

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.

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.

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.

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.

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.