An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Richard G. Gann, Ickchan Kim, Steven P. Lund, William F. Guthrie, Rick D. Davis
Regulations for cigarette ignition resistance (CIR) of soft furnishings (beds and upholstered furniture) and less fire-prone cigarettes have contributed substantially to the decrease in losses from cigarette-initiated fires over time. Two Standard
Jun Wang, Andy Tam, Paul A. Reneke, Richard Peacock, Thomas Cleary, Eugene Yujun Fu, Grace Ngai, Hong Va Leong
This paper presents a study to examine the potential use of machine learning algorithms to build a model to forecast the likelihood of flashover occurrence for a single-floor multi-room compartment. Synthetic temperature data for heat detectors from
Wai Cheong Tam, Eugene Yujun Fu, Richard D. Peacock, Paul A. Reneke, Jun Wang, Jiajia Li, Thomas G. Cleary
This paper presents a learning-by-synthesis approach to facilitate the utilization of a machine learning paradigm to enhance situational awareness for fire fighting in buildings. An automated Fire Data Generator (FD-Gen) is developed. The overview of FD
Studies of human behavior during emergencies have observed that when presented with situational cues that a hazard may be present, humans can fail to act on this information in a timely manner. Models of human behavior in response to fire-related
Randall J. McDermott, Glenn P. Forney, Matthew S. Hoehler, Matthew F. Bundy, Lisa Y. Choe, Chao Zhang
The photograph on the left shows a large-scale experiment studying the interaction between fire and mechanically- loaded building elements performed during the commissioning of the National Fire Research Laboratory (NFRL) at the National Institute of
Ten commercial fire-retardant coatings (FRCs) designed for wood in outdoor applications, either film-forming or non-film forming (stains), and five top-coatings (used in combination with a FRC to increase its durability) were characterized by microscale
Wai Cheong Tam, Eugene Yujun Fu, Amy E. Mensch, Anthony P. Hamins, Christina Yu, Grace Ngai, Hong va Leong
This paper presents a study to examine the potential use of machine learning models to build a real-time detection algorithm for prevention of kitchen cooktop fires. Sixteen sets of time- dependent sensor signals were obtained from 60 normal/ignition
Stanley W. Gilbert, David T. Butry, Richard G. Gann, Rick D. Davis
Effective July 1, 2007, the U.S. Consumer Product Safety Commission promulgated a Standard that would severely reduce the heat release rate and the early heat output from mattresses and foundations that had been ignited by a flaming ignition source
Lisa Y. Choe, Selvarajah Ramesh, William L. Grosshandler, Matthew S. Hoehler, Mina S. Seif, John L. Gross, Matthew F. Bundy
This paper presents the results of compartment fire experiments on four 12.8 m long composite floor beams with various end support conditions. Specimens were constructed as partially- composite beams, consisting of W18x35 steel beams and 83 mm thick
The National Fire Research Laboratory is a unique large-fire research facility; able to characterize the response of full-scale building systems to realistic mechanical loading and fire. The facility maintains an infrastructure of measurements necessary
Selvarajah Ramesh, Lisa Choe, Mina S. Seif, Matthew Hoehler, William L. Grosshandler, Ana Sauca, Matthew Bundy, William E. Luecke, Yi Bao, Matthew Klegseth, Genda Chen, John Reilly, Branko Glisic
The National Institute of Standards and Technology recently expanded its large-scale structural-fire testing capabilities in the National Fire Research Laboratory. A landmark test series was conducted on long-span steel-concrete composite floor beams
This paper presents a learning-by-synthesis approach to facilitate the utilization of machine learning paradigm to enhance situational awareness for fire-fighting in buildings. An automated Fire Data Generator (FD-Gen) is developed. The overview of FD-Gen