U.S. flag

An official website of the United States government

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.

Search Publications by: Wai Cheong Tam (Fed)

Search Title, Abstract, Conference, Citation, Keyword or Author
Displaying 1 - 25 of 58

Understanding the Risk of Lithium-Ion Battery Fires - multi-source data analysis

March 10, 2026
Author(s)
Stanley Gilbert, Hongqiang Fang, David Butry, Wai Cheong Tam, Michelle Donnelly, Juan Fung
Lithium ion battery (LIB) fires are a growing problem that extends across the supply chain, including mining, production, warehousing, shipping, and waste disposal, as well as the consumer side. But data on such fires is fragmented and mostly incomplete

The influence of inclination angles on thermal runaway characteristics and burning behavior of 18650 lithium-ion batteries

February 10, 2026
Author(s)
Jian Chen, Xue Liu, Haolan Chen, Qiuhong Wang, Jun Deng, Jingyu Zhou, Wai Cheong Tam, Yanni Zhang
This paper experimentally examined the thermal runaway (TR) characteristics and burning behavior of 18650 lithium-ion batteries (LIBs) with different inclination angles and states of charge (SOC). A series of experiments on TR burning of LIBs was conducted

Machine Learning Based Forecasting for Building Fires

January 9, 2026
Author(s)
Wai Cheong Tam, Hongqiang Fang, Yifei Ding
The fast-evolving conditions of rapid fire progressions demand swift and informed decision-making from firefighters. This paper presents a series of research efforts to develop artificial intelligent-driven technologies that provide real-time, actionable

A Deep Learning-based Approach for Unsafe Area Prediction in Building Fire Evacuation

August 10, 2025
Author(s)
Hongqiang Fang, Botao Zhang, Wai Cheong Tam, Chendi Yang, S.M. Lo
Dynamic directional exit signs (DDES), also known as smart exit signs, are specifically developed to provide real-time guidance to evacuees during building fire emergencies. However, the existing research lacks adequate focus on effectively predicting

Structural Heart Abnormalities are Prevalent on the 12-lead ECG among Volunteer Firefighters

April 1, 2025
Author(s)
Alexander Bae, Chi-Ju Lai, Yichen Yu, Nicole Krupa, David Hostler, Wai Cheong Tam, Mary Carey, Wendy Brunner, Dillon Dzikowicz
Introduction: In the US, the most common cause of death among active-duty firefighters is sudden cardiac death (SCD). Underlying heart diseases are important propagator of ventricular tachyarrhythmias that cause SCD. The objective of this abstract is to

Fire Data Generator (FD-Gen) v1.0.0

March 6, 2025
Author(s)
Hongqiang Fang, Wai Cheong Tam
This document serves as the documentation for the Fire Data Generator (FD-Gen), an automated tool designed to streamline the creation of multiple Fire Dynamics Simulator (FDS) input files. By employing Monte Carlo methods to sample relevant fire parameters

Detecting Firefighter's Tenability Utilizing Machine Learning

August 30, 2024
Author(s)
Qi Tong, Hongqiang FANG, Eugene Yujun Fu, Wai Cheong Tam, Thomas Gernay
The proposed research aims to leverage machine learning to detect thermal operating classes and improve the tenability of firefighters in a commercial building. A total of 3000 simulations are run with FDS to collect temperature from heat detectors. The

Report on High Energy Arcing Fault Experiments - International Experimental Results from Bus Duct and Switchgear Enclosures

July 1, 2024
Author(s)
Gabriel Taylor, Anthony D. Putorti Jr., Scott Bareham, Christopher U. Brown, Wai Cheong Tam, Michael Heck, Lucy Fox, Stephen Fink, Michael Selepak, Edward Hnetkovsky, Nicholas Melly, Kenneth Hamburger, Kenneth Miller
This report documents an experimental program designed to collect data and information to evaluate the performance of models developed to estimate the electrical high energy arcing fault (HEAF) hazard. This report covers full-scale laboratory experiments

Building Fire Hazard Predictions Using Machine Learning

January 26, 2024
Author(s)
Eugene Yujun Fu, Wai Cheong Tam, Tianhang Zhang, Xinyan Huang
The lack of information on the fire ground has always been the leading factor in making wrong decisions . Wrong decisions can be made by individual firefighters, their local chiefs, and/or the incident commander. Any wrong decision at any level (scale)

Report on High Energy Arcing Fault Experiments: Experimental Results from Medium-Voltage Bus Duct and Switchgear Enclosures

September 15, 2023
Author(s)
Gabriel Taylor, Anthony D. Putorti Jr., Scott Bareham, Christopher U. Brown, Wai Cheong Tam, Ryan Falkenstein-Smith, Stephen Fink, Michael Heck, Edward Hnetkovsky, Nicholas Melly, Kenneth Hamburger, Kenneth Miller
This report documents an experimental program designed to investigate high energy arcing fault (HEAF) phenomena for medium-voltage, metal-enclosed bus ducts and switchgear. This report covers full-scale laboratory experiments using representative nuclear

A review of thermal exposure and fire spread mechanisms in Large Outdoor Fires and the Built Environment

July 28, 2023
Author(s)
Alex Filkov, Virginie Tihay-Felicelli, Nima Masoudvaziri, David Rush, Andres Valencia, Yu Wang, David Blunck, Mario Valero, Kamila Kempna, Jeruzalemska Jan Smolka, Jacques De Beer, Zakary Campbell-Lochrie, Felipe Centeno, Muhammad Asim Ibrahim Stuvaregatan, Calisa Katiuscia Lemmertz, Wai Cheong Tam
Due to socio-economic and climatic changes around the world, large outdoor fires in the built environment have become one of the global issues that threaten billions of people. The devastating effects of them are indicative of weaknesses in existing

Towards Real-Time Heart Health Monitoring in Firefighting Using Convolutional Neural Networks

June 28, 2023
Author(s)
Jiajia Li, Christopher U. Brown, Dillon Dzikowicz, Mary Carey, Wai Cheong Tam, Michael Xuelin Huang
A machine learning-based heart health monitoring model, named H2M, was developed. 24-hour electrocardiogram (ECG) data from 112 professional firefighters was used to train the proposed model. The model used carefully designed multi-layer convolution neural
Was this page helpful?