Poster Presentations:
PhD Student
Texas A&M University
Presentation Title: Empirical Model to Predict Scour Around Shallow Foundations Using Fully-Coupled 3D Numerical Simulation Studies
Co-Authors: Nripojyoti Biswas; Amit Gajurel; Anand J. Puppala; Maria Koliou
Abstract: Progressive scour and erosion around residential foundation structures results in significant challenges for coastal communities, particularly during storm surge events, due to a loss of long-term serviceability of the infrastructure. The exacerbation of climate change and associated sea level rise (SLR) has amplified both the frequency and intensity of such hazards. Existing mitigating guidelines are largely based on historical observations and do not consider different flow conditions. The observed limitations of these practices in addressing recent challenges suggest a more nuanced approach that comprehensively accounts for a wider range of flow conditions and their subsequent effects on distresses is imperative for contemporary design standards. This research presents a fully-coupled 3-dimensional numerical model for estimating scour around an isolated shallow foundation. The model integrates the Re-Normalization Group (RNG) k- turbulence model, coupled with the Meyer-Peter Müller (MPM) sediment transport model, ensuring a dynamic representation of bed morphology alterations over time. Systematic simulations with different combinations of key factors, including flow depth, velocity and duration were used to generate a spectrum of scour values under assorted flow conditions. Results from these analyses were used to derive an empirical model that offers an expedited method for estimating scour depths under potential storm surge scenarios. This model will serve as a valuable tool for preliminary decision-making in storm surge damage prevention.
PhD Student
Texas A&M University
Presentation Title: Hurricane induced riverine-coastal flooding on communities of Atchafalaya basin
Co-Authors: James, Kaihatu
Abstract: The Atchafalaya Basin region of Louisiana contains long, shallow bathymetry and a flat coastal area, which leaves it extremely vulnerable to sizeable hurricane inundation. In addition, numerous narrow waterways, all subject to associated bank overflow, mark the region, increasing the hazard risk. Prediction of the impact of this inundation is necessary to quantify flooding and damage risk to the area. To accomplish this goal, a flow-wave coupling model is developed to simulate the riverine-coastal flooding around the Atchafalaya basin, and in turn examine the area’s hurricane and riverine flooding vulnerability. The Delft3D-FM model suite is used for this application. The flow module of Delft3D-FM, using a single unstructured grid, is developed with resolutions varying from 10km in the offshore to 100m near the communities of interest. This is in turn coupled with a spectral wave model (SWAN). For additional information regarding impact forces on structures, a phase-resolving wave model (FUNWAVE-TVD) with a high resolution (0.5m) structured grid is coupled to SWAN to simulate the effect of transient wave forcing on local structures. The various components of the overall modeling system are further verified using both publicly available data and informal eyewitness information on flood extent from community observers in the area. To complete the risk picture, we use synthetic storm databases to determine the sensitivity of flooding impact and the combined risk of storm surge and riverine flooding. Sea level rise estimates and meteorological databases with established climate change characteristics are also used to further help guide mitigation efforts.
PhD Student
University of Maryland, College Park
Presentation Title: Assessing Community Resilience and Mobility Shifts in Response to Major Disasters: A Case Study on Hurricane Ida
Co-Authors: Behnam Tahmasbi; Asal Mehditabrizi
Abstract: This research focuses on understanding the effects of major disasters, specifically Hurricane Ida, on travel and commuting patterns. The aim is to evaluate the resilience capacity of communities by scrutinizing mobility changes occurring before, during, and after such catastrophic events. Using mobile device location data, we intend to analyze shifts in travel behaviors and commuting routes, providing real-time insights into how people adapt in crisis situations. A crucial aspect of this study is the exploration of how socioeconomic factors influence community resilience and evacuation choices. Investigating this relationship is key for understanding the varying responses to natural disasters across different communities. Ultimately, our research seeks to develop an explanatory model that reveals how individuals alter their movements and travel choices in response to evacuation notices and disaster challenges. By analysis of big mobile location data, we aim to provide a comprehensive understanding of the disruptions and adaptations in travel behavior and commuting patterns caused by significant events like Hurricane Ida. This study aspires not only to contribute to academic understanding but also to aid in refining disaster response strategies, thereby fostering more resilient communities.