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Displaying 1 - 25 of 144

Suspect Screening of Per- and Polyfluoroalkyl Substances in New Firefighter Turnout Gear Textiles

March 20, 2025

Author(s)

Andrew Maizel, Andre Thompson, Benjamin Place, Alix Rodowa, Jessica Reiner, Audrey Tombaugh, Halen Solomon, Brittany Stinger, Michelle Donnelly, Rick Davis

Firefighter turnout gear provides protection from exposure to flame, heat, and liquids, but has also been found to contain numerous per- and polyfluoroalkyl substances (PFAS). Previous examinations of PFAS in firefighter turnout gear have predominantly

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

A Cost-Effective Data-driven Approach to Flashover Prediction across Diverse Residential Layouts for Enhanced Firefighters Situational Awareness

December 30, 2024

Author(s)

Linhao Fan, Hongqiang Fang, Tianshui Liang, Wai Cheong Tam, Qixing Zhang

This paper presents gFlashNet, a generic flashover prediction model, designed to address the limitations of existing models that are restricted to specific residential building layouts. The aim of this research is to improve the scalability and

Examining the Effects of Deep Learning Model Structure on Model Interpretability for Time-Series Classifications in Fire Research

September 13, 2024

Author(s)

Wai Cheong Tam, Fan Linhao, Qi Tong, Fang Hongqiang

This present work utilizes an interpretability model to understand and explain the decisions of deep learning models. The use of DeepLIFT is proposed and attributions of a study case are obtained. Benchmarking against two other interpretability models

Detecting Firefighter's Thermal Risks in a Commercial Building Structure Using Machine Learning

August 30, 2024

Author(s)

Qi Tong, David Stroup, Wai Cheong Tam

A multi-input and multi-output (MIMO) machine learning model is developed to simultaneously detect firefighter's thermal risks across a commercial building structure. A total of 2000 numerical experiments with a wide range of fire and ventilation scenarios

Towards Better Understanding of Deep Learning Model for Time Series Forecasting in Fire Research

July 29, 2024

Author(s)

Linhao Fan, Qi Tong, Hongqiang FANG, Wei Zhong, Wai Cheong Tam, Tianshui Liang

Deep learning model has been a viable approach to forecast critical events in fire development. However, prior to its implementation in real-life firefighting, it is imperative to further understand the black box and assess its rationale. In this paper, an

The Effect of Mandatory Fitness Requirements on Cardiovascular Events: A State-by-State Analysis Using a National Database

March 1, 2024

Author(s)

Dillon Dzikowicz, Sankalp Saoji, Wai Cheong Tam, Wendy Brunner, Mary Carey

Background: Cardiovascular events are known to be the leading cause of death among on-duty firefighters. Implementing fitness standards may help reduce the incidence of cardiovascular deaths; however, standards vary between firefighter type and states. We

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)

Data-Driven Wireless Fire Hose Flow Rate Apparatus

January 25, 2024

Author(s)

Christopher U. Brown, Gregory W. Vogl, Wai Cheong Tam

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

Explainable Machine Learning Based Flashover Prediction Model Using Dimension-Wise Class Activation Map

June 26, 2023

Author(s)

Linhao Fan, Wai Cheong Tam, Qi Tong, Eugene Yujun Fu, Tianshui Liang

This paper presents the development of an explainable machine learning based flashover prediction model, named xFlashNet. Synthetic temperature data from more than 17 000 fire cases are used for model development. The effect of missing data due from heat

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

Measuring Water Flow Rate for a Fire Hose Using a Wireless Sensor Network for Smart Fire Fighting: An Update Using Various Hose Conditions

January 26, 2023

Author(s)

Christopher U. Brown, Gregory W. Vogl, Wai Cheong Tam

A wireless sensor network was created to measure water-flow rate in a fire hose. An integrated electronic piezoelectric (IEPE) accelerometer was chosen as the sensor to measure the flow rate based on the vibrations generated by water flowing through a fire

Thermal and gas mixture composition measurements preceding backdrafts in a 2/5th scale compartment

September 1, 2022

Author(s)

Ryan Falkenstein-Smith, Thomas Cleary

This report documents real-time and time-averaged temperature, global and local equivalence ratios, and oxygen, carbon dioxide, and carbon monoxide concentration measurements made at various positions in an isolated 2/5th scale compartment prior to a

A Spatial Temporal Graph Neural Network Model for Predicting Flashover in Arbitrary Building Floorplans

August 3, 2022

Author(s)

Wai Cheong Tam, Eugene Yujun Fu, Jiajia Li, Xinyan Huang, Jian Chen, Michael Xuelin Huang

Rapid fire progression, such as flashover, has been one of the leading causes for firefighter deaths and injuries in residential building environments. Due to long computational time of and the required prior knowledge about the fire scene, existing models

Real-time Forecast of Compartment Fire and Flashover based on Deep Learning

April 6, 2022

Author(s)

Tianhang Zhang, Zilong Wang, Ho Yin Wong, Wai Cheong Tam, Xinyan Huang, Fu Xiao

Forecasting building fire development and critical fire events in real-time is of great significance for firefighting and rescue operations. This work proposes an artificial intelligence (AI) system to fast forecast the compartment fire development and

Development of a Flashover Prediction Model for Compartment Fires Using Support Vector Regression

April 1, 2022

Author(s)

Wai Cheong Tam, Jun Wang, Richard D. Peacock, Paul A. Reneke, Eugene Yujun Fu, Thomas Cleary

This report provides additional technical details to an article entitled P-Flash – A Machine Learning-based Model for Flashover Prediction using Recovered Temperature Data. Research was conducted to examine the use of Support Vector Regression (SVR) to

A Generic Flashover Prediction Model for Residential Buildings Using Graph Neural Network

November 11, 2021

Author(s)

Wai Cheong Tam, Eugene Yujun Fu, Paul A. Reneke, Richard D. Peacock, Thomas Cleary

A generic graph neural network-based model is developed to predict the potential occurrence of flashover for different building structures. The proposed model transforms multivariate temperature data into graph-structure data. Utilizing graph convolution