Poster Presentations:
Graduate Student Researcher
Oregon State University
Uncertainty Quantification in Seismic-induced Building Debris Tsunami Evacuation
Co-Authors: Mehrshad Amini (University of Rhode Island), Andre R. Barbosa (Oregon State University), and Daniel Cox (Oregon State University)
Abstract: This study aims to quantify uncertainty in predicting seismic damage to buildings and associated debris at a community scale. Traditionally, the HAZUS methodology offers expected values for the debris fraction, resulting in deterministic outcomes for structural and nonstructural debris volumes. In this study, we extend the HAZUS methodology to quantity and propagate uncertainties in the model. Results are compared using two building inventory databases: (1) the National Structure Inventory (NSI), and (2) a local tax assessor inventory. The NSI is a publicly available building inventory developed by the U.S. Army Corps of Engineers, whereas local tax assessor data are unique for each community. This study is applied to the coastal city of Seaside, Oregon, which is vulnerable to earthquake and tsunami hazards from the Cascadia Subduction Zone. The seismic damage and associated debris volume are estimated at the parcel level across seven (7) mean recurrence intervals, ranging from 100-yr to 10,000-yr. Additionally, we compare the outcomes of this new approach with those of the traditional HAZUS methodology. The impact of the damage on debris volume is assessed on the tsunami life-safety, using IN-CORE, which is an open-source community resilience modeling environment. The insights gained will allow decision-makers, engineers, and scientists to better understand the uncertainty in seismic debris of buildings, which can improve the effectiveness of seismic and tsunamis risk management and response strategies.
Graduate Student Researcher
University of Southern California
CelerisAi: A Python-Taichi-Based Nearshore Wave Modeling Framework for Integrated AI Applications
Co-Authors: Patrick Lynett (University of Southern California)
Abstract: This presentation introduces PyCeleris, a novel nearshore wave modeling software developed in Python-Taichi. Building upon the Celeris Advent framework, CelerisAi offers a high-performance, real-time solution capable of simulating wave dynamics in coastal environments. By leveraging Taichi's high-performance parallel programming capabilities(CPU/GPU), CelerisAi enables efficient computations on personal laptops, and it is also suitable for large-scale simulations on HPCs.
A key advantage of CelerisAi is its seamless integration with machine learning and artificial intelligence environments. This integration allows for hybrid models combining numerical simulations with data-driven approaches. As a proof of concept, we demonstrate how CelerisAi can be used to train a neural network to predict wave effects based on boundary and initial conditions. This approach could reduce the memory footprint required for storing simulation results while simultaneously training a neural network on a large dataset of scenarios.
Beyond wave prediction, CelerisAi can serve as a foundation for addressing various coastal engineering problems, including inverse problems, sediment transport, and wave run-up. The framework's flexibility allows for exploring different neural network architectures, such as reinforcement learning for incorporating agent-based models into coastal infrastructure design or variational autoencoders for identifying patterns in coastal processes.
By combining numerical modeling with artificial intelligence, CelerisAi offers a promising approach to advancing the understanding of coastal dynamics and hazards.