Pilot Study: Learning Fluid-Structure Interaction via Machine Learning
| 项目名称： | Pilot Study: Learning Fluid-Structure Interaction via Machine Learning |
| 摘要： | 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. |
| 状态： | Active |
| 资金： | 80000 |
| 资助组织： | United States Department of Transportation - FHWA - TTAP<==>Office of the Assistant Secretary for Research and Technology |
| 执行机构： | Oregon State University, Corvallis |
| 开始时间： | 20190816 |
| 预计完成日期： | 20210815 |
| 主题领域： | Bridges and other structures；Data and Information Technology；Highways；Hydraulics and Hydrology；Planning and Forecasting |
相关文献
- Seamless Comparative Modeling of Natural Hazards Using the Material Point Method
- Real-time hybrid experimental-numerical simulation of bridge infrastructure subject to cascading earthquake-tsunami hazards
- Non-contact Intelligent Inspection of Infrastructure
- The Impacts of Emerging Mobility Options and Vehicle Technologies on Travel Behavior
- Coupled Numerical Simulation of Debris Flow-Soil-Structure Interactions for Flexible Barrier Mitigation Systems
- Platooning for Improved Safety and Efficiency of Semi-trucks [PISES - III]
检索历史
应用推荐
