CAREER: Understanding and Modeling Collapse-induced Fire Dynamics at Multiple Scales

This Faculty Early Career Development Program (CAREER) award will advance understanding of how structural collapse influences fire behavior in residential communities, particularly in wildfire-prone regions. At the single-building scale, collapse during fires alters fire behavior by changing compartmentation, ventilation, fuel distribution, flame-spread pathways, and ember generation, with effects pronounced in light-frame wood structures, the most common residential construction in the United States. At the neighborhood scale, collapse can cause direct flame contact by toppling burning structures onto adjacent ones, introduce fuel into the space between buildings, and increase ember flux toward neighboring buildings, collectively leading to cascading fire spread in densely built areas. However, these interactions remain poorly understood and are rarely addressed in existing fire modeling tools. This project will address this critical gap and contribute to safer building design, improved neighborhood planning, and firefighter safety. The project will also integrate research with education by training students and engaging them in hands-on research and design activities, with research outcomes incorporated into coursework and design-based learning. These activities will equip students with practical skills in analyzing fire-induced collapse and designing fire-resilient structures and neighborhoods, strengthening the future workforce in fire safety and structural engineering. By addressing knowledge gaps in fire hazards and developing tools and insights for mitigating them, this project serves the national interest by promoting public safety and community resilience. This project aims to develop a comprehensive understanding and modeling framework for collapse-induced fire dynamics in light-frame wood structures at both the single-building and neighborhood scales. The research integrates experimental and computational approaches. Full-scale and scaled fire experiments on light-frame wood structures will be conducted to quantify how collapse affects fire behavior, including changes in ventilation, compartmentation, fuel distribution, and fire-spread pathways. These data will support the development of a computational framework that dynamically incorporates collapse processes into fire simulations through staged model updates, enabling efficient representation of evolving building conditions without requiring fully coupled fire-thermal-structural modeling. This modeling framework will be extended to simulate fire spread at the neighborhood scale, accounting for the effects of structural collapse on inter-structure fire spread. The developed modeling framework will be validated and used for parametric studies at both the single-structure and neighborhood scales to identify key factors influencing collapse-driven fire hazards. The outcomes of this project will provide new knowledge, modeling capabilities, and design insights to support fire-resilient building design and neighborhood planning in wildfire-prone regions. Insights from this project will also support emergency response by helping firefighters anticipate potential structure collapse, reducing their risk of injury or fatality during operations. 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. NSF Award ID: 2543737 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Shuna Ni | Institution: University of Maryland, College Park, COLLEGE PARK, MD | Award Amount: $716,567 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543737 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543737.html