Building Evaluation for Human-Centered Flood Management: Integrating Foundation Models and Value-Based Methods
- Institution
- Virginia Polytechnic Institute and State University, VA
- Principal investigator
- Ruichuan Zhang
- Funding
- $550K
- Duration
- 2025–2028
Moonbase score
Still being scored
Abstract
The objective of this project is to support research that looks to examine building design and operation during disasters, focusing on people who have navigational challenges that may adversely affect their evacuation. It provides new insights into building features from the perspective of those who rely on mobility aids. The outcomes potentially lead to responsible, resilient buildings and contribute to knowledge at the interaction of disasters, built environment, and populations. Additionally, this project promotes interdisciplinary education in engineering, humanities, and design through new course modules and outreach initiatives. Building designers and operators often overlook unique obstacles posed during disasters due to a lack of evaluation methods that can integrate architectural design principles, the needs of people with mobility support requirements, and disaster resilience goals. This gap is further rooted in the absence of voices from people who use mobility aids and efficient mechanisms to incorporate their needs into digital platforms. This project, by leveraging the power of foundation models, which are large deep learning models trained on extensive datasets of images and text, looks to automate extraction and analysis of key building features. Furthermore, value of the developed models will be evaluated by assessing how well such features align with need hierarchies derived from a curated database of lived experiences during past flood events. The findings are expected to inform design decisions across disaster types, built environment scales, and population groups. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- Ruichuan ZhangPI
- Ashley Shew
- Lu Zhang
- Tess Doeffinger
LOADING MOONBASE SCORE
Questions for Virginia Polytechnic Institute and State University
Answered by the team behind this research.
No questions yet. Sign in to ask the first one
Related grants
Same researchers, same institution, same topic, scored first.
- NSF · University of Arkansas64
III:Small: Counterfactually Fair Machine Learning through Causal Modeling
Lu Zhang · $485K · 2021–2026
- NSF · William Marsh Rice UniversityReviewed
RTG: Numerical Mathematics and Scientific Computing
Beatrice Riviere · $2.3M · 2023–2028
- NSF · Virginia Polytechnic Institute and State UniversityNot yet scored
IUCRC Planning Grant Virginia Tech: Center for Digital Twin Research, Innovation and Collaboration Hub (DT-RICH)
Dianzhi Sui · $20K · 2025–2027