Session Abstracts:
Session 7A
PhD Student
University of British Columbia
Presentation Title: Using red tag probability to inform functional recovery design provisions
Co-Author: Carlos Molina Hutt, Reid Zimmerman, Dustin Cook, Curt Haselton, and Ed Almeter
Abstract: With the development of new functional recovery design provisions, drift limits and strength reduction factors are being re-evaluated to determine whether stricter values need to be employed to achieve recovery-based design targets (e.g., red tag probability, functional recovery time). In support of the upcoming 2026 NEHRP recommendations, we present a methodology that employs the ATC 138 framework to analyze the relationships between strength, stiffness, and red tag probability. For an example 8-story modern reinforced concrete moment frame building, engineering demand parameters are generated for archetypes with design drift limits ranging from 0.5% to 2.0% and strength reduction factors ranging from 1.0 to 8.0. The demands are then used to evaluate component damage and red tag probability. Results of this analysis are used to populate a design space with possible drift limit and strength reduction combinations that achieve an acceptably low probability of red tags at the design level earthquake. This methodology will be expanded to evaluate the impact of drift limits and strength reduction factors on red tag probability for a large range of lateral system types and building heights to inform design provisions.
PhD Student
Rice University
Presentation Title: Leveraging cyberinfrastructure to support modeling of hurricane-induced debris impacts for coastal community resilience analysis
Co-Author: Jamie Padgett
Abstract: In this research, leveraging cyberinfrastructure through DesignSafe computational and data resources, we propose a probabilistic methodology to evaluate the impacts of hurricane-induced debris on coastal communities, crucial for resilience analysis in the face of climate disasters. Debris-related impacts extend beyond direct economic loss, encompassing structural damage, impairment of emergency transportation networks, and delayed recovery of vital systems. Our approach includes interdependent probabilistic models for debris volume estimation and spatial distribution, physical damage assessment, and impacts on network-level performance, particularly transportation infrastructure. The methodology also addresses the disruption in the recovery of interdependent systems. Applied to a testbed community with data relevant to the Galveston region in Texas, USA, the study highlights the importance of considering debris impacts in evaluating community-scale resilience metrics like infrastructure service availability and recovery, and equitable access to emergency facilities. This comprehensive analysis underscores the necessity of factoring in debris impacts in resilience planning for coastal communities.
Faculty
Florida International University
Presentation Title: Critical-Infrastructures Resilience Across US States During Extreme Events: Hurricane Harvey Versus Irma
Co-Author: Shahnawaz Rafi; Joost R. Santos
Abstract: In this study we compare the resilience and performance of critical infrastructure systems in the United States, focusing on the impact of Hurricane Harvey and Hurricane Irma in 2017. The objective is to identify any differences in the ability of critical infrastructure systems to withstand and recover from such extreme weather events across different regions in the USA. We gathered data through household surveys in Florida (Hurricane Irma) and Texas (Hurricane Harvey) following the landfall of both hurricanes, with a sample size of 780 respondents for each location in Florida and Texas. We investigated the number of households that experienced various types of utility service disruptions during these hurricanes, including electricity, water, phone/cell phone, internet, transportation, workforce, and grocery stores. In Florida and Texas, 74% and 66% of respondents reported electricity disruptions, and 41% and 50% experienced water supply disruptions. Phone reception was unavailable to about half of the surveyed households, and transportation was disrupted for over 40% respondents. Accessing groceries and work was difficult for more than half of the respondents in both states. We incorporated the survey responses into the Dynamic Inoperable Input-Output Model (DIIM) to understand and compare the economic consequences of utility service disruptions that households faced during Hurricane Harvey and Irma. The results may provide valuable insights in analyzing the vulnerability of critical infrastructure systems in the US and guide directions to improve their resilience. Identifying vulnerable sectors in our economy can inform targeted interventions to mitigate the impact of future shocks.
Postdoc or Researcher
Johns Hopkins University
Presentation Title: Alternatives for Resilient Communities with Consideration of Uncertainty
Co-Author: Kenneth Harrison; Tasnim Ibn Faiz
Abstract: Community resilience planning is a complex process that involves multiple large-scale systems, public sector decision-making, and the integration of diverse stakeholder perspectives. The National Institute of Standards and Technology (NIST) has developed the Alternatives for Resilient Communities (NIST-ARC) software as an interactive tool to facilitate the development of alternative action plans that meet community resilience and cost goals. However, community resilience assessment is subject to inherent uncertainties stemming from various sources, such as incomplete data, imprecise hazard predictions, and the dynamic nature of interdependencies among systems. Recognizing the significance of uncertainty in resilience assessment, this paper introduces an extension to the NIST-ARC software that explicitly considers uncertainty in the decision-making process. The proposed uncertainty extension allows for the quantification and propagation of uncertainties associated with input parameters through the model. By doing so, the uncertainty in output parameters, crucial for effective decision-making, can be estimated. To demonstrate the practical application of the uncertainty extension, a case study is presented using community data from Lumberton City in North Carolina, which suffered significant damage due to Hurricane Mathew in 2016. Through this case study, the paper showcases how the incorporation of uncertainty considerations in the decision-making process enhances the resilience planning efforts for disaster-prone communities.
PhD Student
Texas A&M University
Presentation Title: Ensemble-based Time Series Modeling for Predicting Power Outages During Extreme Weather: A Multi-factor Approach Integrating Meteorological, Geographical, and Socio-Demographical Features
Co-Author: Stephanie Paal
Abstract: Global warming is not just intensifying but radically amplifying extreme weather events across the globe. Take 2023 as a startling example: Texas experienced an unyielding, month-long heatwave with temperatures skyrocketing to a blistering 104°F, devoid of any rainfall. This stands in jarring contrast to the catastrophic winter storm Uri in 2021, which paralyzed Texas' electrical grid—engineered for far milder winters—triggering extensive power outages and causing devastating damage. Consequently, electricity supply was calculated based on these milder weather patterns, but the 2021 winter storm ""Uri"" far exceeded these supply estimates. This led to large-scale power outages, causing devastating consequences for Texas. To mitigate and prevent such damage, an effective strategy would involve accurate power outage prediction models to estimate outage quantities and prioritize electricity supply to areas expected to be most affected. This research has developed an ensemble-based time-series model for accurate power outage predictions. The primary independent variables used in this model include meteorological data, geographical features, and socio-demographical information, while the dependent variable considered is county-specific power outages. As all three major categories—meteorological, geographical, and socio-demographical—are organically interconnected and have non-negligible influences on power outage predictions, a multi-factor-based time-series model that considers variables from all three categories should be employed for accurate power outage predictions. Through correlation analysis with this multi-factor approach, this study revealed that areas densely populated with elderly residents are notably more vulnerable to intensified power outages.
Postdoc or Researcher
Texas A&M University
Presentation Title: Community resilience analysis under seismic hazard using agent-based modeling approach
Co-Author: Maria Koliou
Abstract: Seismic hazard is one of the most devastating hazards facing human society. Community resilience analysis which focuses on the post-hazard recovery process of a community, has received a tremendous amount of attention from researchers of a wide spectrum of disciplines. As a matter of fact, community resilience is a highly multi-disciplinary research topic as the functionality recovery of a community hinges upon the restoration of the functionality of multiple sub-systems, such as residential, business, and lifeline infrastructure, among others. So far, resilience analysis on each of the sub-systems in a community has been abundant. However, how to model the interactions among different sub-systems represents a significant challenge to the researchers interested in conducting community resilience analysis. Of all the approaches to conducting community resilience analysis, agent-based modeling (ABM) approach is promising in addressing this challenge. By generating different types of agents to represent various entities in sub-systems, the recovery process of each sub-system as well as their interactions can be simulated in a high resolution. This paper employs the ABM approach in simulating the recovery process of a community with multiple subsystems under multiple seismic scenarios. The effect of hazard mitigation strategies on community resilience is also investigated using the ABM approach.
Postdoc or Researcher
University of Kansas
Presentation Title: A Framework for Predicting a Community's Post-Disaster Temporary Housing Demand
Co-Author: Ava Greene; Elaina Sutley
Abstract: Temporary housing is an essential component of a community’s post-disaster recovery. It goes beyond providing a habitable shelter to instilling a sense of hope and dignity among households and empowering them to return to some semblance of normalcy. This research develops a framework that includes a set of integrated computational models and algorithms, to predict post-disaster needs for temporary housing across the disaster timeline. First, a housing unit inventory is created using an existing probabilistic housing unit allocation model and Census data. The framework then estimates population dislocation induced by the disaster scenario and simultaneously tracks the stage of post-disaster housing recovery for each dislocated household alongside the functionality restoration for every housing unit. Housing unit functionality is estimated using a temporally-based restoration model that incorporates the socioeconomic characteristics of the housing owner, and associated access and timing to receive recovery resources into downtime. The recovery of dislocated households is predicted using a multi-state Markov chain model based on households’ social vulnerability and captures the timing and sequence of households’ movement through the four stages of post-disaster housing: emergency shelter, temporary shelter, temporary housing, and permanent housing. The framework utilizes an optimized rule-guided stochastic algorithm to allocate housing units to households that transition to the permanent housing stage while their pre-disaster housing unit is not functional yet. Finally, it predicts the temporary housing demand at any time after the hazard occurrence. This finding can be used for efficient resource allocation, better logistics, and timely response to the community’s overwhelming post-disaster needs.
PhD Student
University of Maryland, College Park
Presentation Title: Assessing Adaptive Resilience in School Districts During Hurricane-Induced Closures
Co-Author: Allison Reilly
Abstract: This study investigates the heterogeneity in school closure durations caused by hurricanes across East and Gulf Coast US school districts. While closures due to natural hazards, including heat and snow, are increasingly common, the underlying causes for the variations in closure lengths remain inadequately understood, and likely reflect hazard intensity, district infrastructure, and local adaptive capacity. This research aims to unravel discernible patterns and influential factors that contribute to the temporal variability of hurricane-induced school closures for improved preparedness. Using advanced machine learning models, we categorize closures into two levels, emphasizing predictions for extreme closures due to higher impact. These predictions draw upon an array of indicators that encapsulate the capacity of school districts and the hazard characteristics. Later, by aligning projected closure figures with actual closure days, we assess the adaptive resilience of individual districts. More specifically, a district predicted to have a long duration closure that actually closed for a few days may reflect noteworthy adaptive capacity. A comprehensive analysis of the factors contributing to these divergences, complemented by an in-depth scrutiny of pertinent news and school documentation, forms the cornerstone of this study's. The critical significance of unplanned school closures lies in their substantial impact on students, families, and communities, causing disruption to educational systems, social challenges, and economic consequences. This examination can help policy formulation, resource allocation, and mitigation for schools.