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Search Publications by: Wai Cheong Tam (Fed)

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Displaying 1 - 25 of 51

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

Real-Time Flashover Prediction Model for Multi-Compartment Building Structures Using Attention Based Recurrent Neural Networks

March 17, 2023
Author(s)
Wai Cheong Tam, Eugene Yujun Fu, Jiajia Li, Richard D. Peacock, Paul A. Reneke, Thomas Cleary, Grace Ngai, Hong Va Leong, Michael Xuelin Huang
This paper presents the development of an attention based bi-directional gated recurrent unit model, P-Flashv2, for the prediction of potential occurrence of flashover in a traditional 111 m2 single story ranch-style family home. Synthetic temperature data