Session Abstracts:
Researcher
University of Texas at Austin
Development of A Combined Hurricane Storm Surge and River Runoff Model for the Texas Coast to understand transitional zones
Co-Authors: Clint Dawson (UT Austin), Mark Loveland (UT Austin), Chayanon Wichitrnithed (UT Austin), and Amin Kiaghadi (TWDB)
Abstract: We present our current efforts on the modeling of compound floods due to runoff and hurricane storm surge on the Texas Coast. In particular, we seek to enhance the current understanding of transitional zones between inland, riverine flooding, and coastal, surge driven flooding. To this end, we rely on recently developed finite element meshes, i.e., domain discretization’s for the United States North Atlantic and Gulf of Mexico coastlines. These meshes contain extraordinary resolution of floodplains and rivers on the Texas Coast and are used in our ADvanced CIRCulation (ADCRIC) numerical model. To comprehensively ascertain the dynamics and interactions between surge and riverine flows, we consider numerous past hurricanes making landfall on the Texas coast, including Hurricanes Ike and Harvey, as well as synthesized events designed to maximize floods.
Assistant Professor
University of Miami
A framework for managing life-cycle risk of coastal bridge network using digital twin technology
Co-Authors: Ziheng Geng (University of Miami)
Abstract: Bridges are critical yet vulnerable assets in infrastructure systems and their resilience is the cornerstone of resilient communities or cities. The resilience of these structures is affected by a range of time-dependent factors, including natural hazards, material deterioration, and overload conditions. Therefore, it is essential to monitor and assess their risks at a holistic level. Recent advancements in data acquisition have propelled the use of digital twins (DTs) as a transformative approach for modeling and managing physical assets in a virtual world. This innovative paradigm can enhance life-cycle risk assessment (LCRA) by integrating the available observations to update the risk profile. This study develops a framework for managing the life-cycle risk of coastal bridge network using digital twin. The framework utilizes a Bayesian network to capture the correlations and interdependencies at various levels, including within individual bridge components (e.g., multiple failure modes), between system components (e.g., bridges on an origin-destination route), and across interconnected systems (e.g., bridge and hydraulic systems). By integrating diverse data sources such as bridge inspection reports, traffic monitoring data, and river gage measurements, the framework enables real-time updates to the life-cycle risk of the bridge network. The framework is exemplified through a coastal bridge network in Miami-Dade County, illustrating its effectiveness in updating the bridge network risk using available data. Additionally, a future scenario of rising river levels is explored to demonstrate the framework’s potential in supporting the life-cycle risk management of infrastructure systems.
Postdoctoral Fellow
National Institute of Standards and Technology
Preparing for future hazards amplified by sea-level rise: Considering impacts to infrastructure and intelligent agents
Co-Authors: Rithika Dulam (NIST), Terri McAllister (NIST), and Jennifer Helgeson (NIST)
Abstract: Chronic hazards associated with a changing climate may necessitate difficult adaptation decisions in many coastal communities. Computational tools that account for uncertainties in future climate conditions, impacts, and human responses are needed to assist in decision-making processes towards community resilience. This presentation describes a novel decision-support tool for simulating household and business responses to future flooding amplified by sea-level rise. The model combines local sea-level rise scenarios and tide predictions to determine impacts on building exposure, electric power outages, and increases in travel times. Households and businesses are represented as agents with bounded rationality who respond to these impacts by taking risk mitigation actions that include relocating, elevating, installing solar panels, or doing nothing. Households and businesses evaluate their options by considering the following information on an annual basis either at their building or in their neighborhood: (1) days per year exposed, (2) days per year without electricity, (3) days per year with increases in travel time, and (4) neighbor migration. Businesses additionally consider available workforce, customers, and costs. Reinforcement learning is used to simulate agent learning and the decision-making process. The use of this decision-support tool is demonstrated using Galveston, Texas, USA, as a testbed community, and interactions between households and businesses can be studied. The primary equation behind the agent decision-making process is simple to understand, thus making this framework useful for cross-disciplinary collaborations and community engagement.
Graduate Student Researcher
University of Maryland, College Park
EV Charging Station Accessibility and Usage During Extreme Weather Events: Insights from Hurricane Beryl
Co-Authors: Diako Abbasi (University of Maryland, College Park), Allison Reilly (University of Maryland, College Park), Jiehong Lou (University of Maryland, College Park) and Dr. Deb Niemeier (University of Maryland, College Park)
Abstract: This research investigates the impact of extreme weather events, specifically Hurricane Beryl in 2024, on the usage patterns and operational status of electric vehicle (EV) charging stations in Texas. By analyzing real-time charging port data, recorded every 10 minutes, we developed weekly usage profiles over five months to identify deviations during the hurricane week compared to typical historical patterns. The study highlights how flood events affect EV charging usage patterns spatially and temporally, focusing on three key hypotheses as the main drivers: power outages, road accessibility, and stations’ inundation. We employed Hurricane Beryl’s flood inundation map, a 100-year flood map, and power outage data to examine the underlying causes of these variations. Three indices were created to capture the preparedness, intensity, and recovery phases of EV charging station resilience during the hurricane. An accessibility index was also developed to determine how the accessibility level of each station varies during flood events and how critical this can be due to the congestion level of that station. The findings provide valuable insights for decision-makers and grid operators to enhance preparedness and mitigation strategies, optimize infrastructure placement, balance power demand, enhance grid stability, and facilitate rapid recovery from such events, ultimately contributing to the resilience of EV infrastructure during natural disasters.
Graduate Student Researcher
Texas A&M University
Non-locality and Spillover Effects of Residential Flood Damage on Community Recovery: Insights from High-resolution Flood Claim and Mobility Data
Co-Authors: Russell Blessing (Texas A&M University at Galveston), Samuel Brody (Texas A&M University at Galveston), and Ali Mostafavi (Texas A&M University)
Abstract: Examining the relationship between vulnerability of the built environment and community recovery is crucial for understanding disaster resilience. Yet, this relationship is rather neglected in the existing literature due to previous limitations in the availability of empirical datasets needed for such analysis. In this study, we combine fine-resolution flood damage claims data and human mobility data during the 2017 Hurricane Harvey in Harris County, Texas, to specify the extent to which vulnerability of the built environment (i.e., flood property damage) affects community recovery locally and regionally. We examine this relationship using a spatial lag, spatial reach, and spatial decay models to measure the extent of spillover effects of residential damage on community recovery. The findings show that: first, the severity of residential damage significantly affects the speed of community recovery. A greater extent of residential damage suppresses community recovery not only locally but also in the surrounding areas. Second, the spatial spillover effect of residential damage on community recovery speed decays with distance from the highly damaged areas. Third, spatial areas display heterogeneous spatial decay coefficients, which are associated with urban structure features such as the density of points-of-interest facilities and roads. These findings provide a novel data-driven characterization of the spatial diffusion of residential flood damage effects on community recovery and move us closer to a better understanding of complex spatial processes that shape community resilience to hazards. This study also provides valuable insights for emergency managers and public officials seeking to mitigate the non-local effects of residential damage.
Assistant Professor
University of Cincinnati
Robust design of earthen levees in the face of flood hazards
Co-Authors: Liang Zhang (University of Cincinnati)
Abstract: Flood is one of the most costly natural disasters in the United States, causing significant economic losses annually. These high-loss flood events are becoming increasingly frequent, threatening the safety of millions of human lives and properties. To prevent flood hazards, numerous flood protection infrastructure (e.g., earthen levees) have been built across the whole United States, and mainly designed via deterministic approaches. In the routine geotechnical design of earthen levees, the final recommended design tends to be subject to various uncertainties (e.g., uncertain soil property parameters) and hazard conditions (e.g., flood). The robust geotechnical design (RGD) approach is an effective solution to achieve design robustness of geotechnical systems in the face of the uncertain soil parameters. However, in the RGD framework, the current robustness measures cannot evaluate the design robustness against hazard conditions such as flooding hazards with rising flood water elevations for flood protection infrastructure. This paper proposed a new robustness measure to address this gap and integrates this concept into a multi-objective optimization design framework, where both the construction cost and design robustness are optimized simultaneously under the safety constraint. The Pareto front-based optimization method is adopted to elucidate the trade-off relationship between conflicting design objectives and identify the knee point on the Pareto front as the most preferable design. The application of the proposed robustness measure for design optimization of flood protection infrastructure in the face of flood hazard is elaborated on through a case study of earthen levee design problem.