Pilot Study: Learning Fluid-Structure Interaction via Machine Learning
This proposal addresses the content area, improving mobility of people and goods, particularly ensuring reliable mobility across bridges after tsunami loading. This work also aligns with ongoing interest in tsunami loading on bridges and machine learning applications by the Oregon Department of Transportation and the Pacific Earthquake Engineering Research (PEER) Center. Although implemented herein for the analysis of bridges, the resulting machine learning framework would be applicable to other computationally-expensive simulations and a larger set of data-driven transportation problems, such as evacuation models, active traffic control, analyzing sensor data, etc. Implementing faster models that maintain the efficacy of the original data would result in prompt feedback for analysis and design, increased feasibility for parametric applications, and better fragility functions based on CFD/FSI rather than equivalent static analysis.
Language
- English
Project
- Status: Completed
- Funding: $80000
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Contract Numbers:
69A3551747110
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Sponsor Organizations:
United States Department of Transportation - FHWA - TTAP
1200 New Jersey Avenue, SE
Washington, DC 20590Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of Washington, Seattle
Civil and Environmental Engineering Department
201 More Hall, Box 352700
Seattle, WA United States 98195-2700 -
Performing Organizations:
Oregon State University, Corvallis
204 Rogers Hall
Corvallis, OR United States 97331 -
Principal Investigators:
Simpson, Barbara
- Start Date: 20190816
- Expected Completion Date: 20210815
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Bridges; Fluid mechanics; Machine learning; Pilot studies; Tsunamis
- Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Hydraulics and Hydrology; Planning and Forecasting;
Filing Info
- Accession Number: 01723940
- Record Type: Research project
- Source Agency: Pacific Northwest Transportation Consortium
- Contract Numbers: 69A3551747110
- Files: UTC, RIP
- Created Date: Nov 28 2019 8:38AM