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Fire

Fire fighting

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Displaying 26 - 50 of 144

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

Predicting Flashover Occurrence using Surrogate Temperature Data

February 9, 2021
Author(s)
Andy Tam, Eugene Yujun Fu, Richard Peacock, Paul A. Reneke, Jun Wang, Grace Ngai, Hong Va Leong, Thomas Cleary
Fire fighter fatalities and injuries in the U.S. remain too high and fire fighting too hazardous. Until now, fire fighters rely only on their experience to avoid life-threatening fire events, such as flashover. In this paper, we describe the development of

On the Use of Machine Learning Models to Forecast Flashover Occurrence in a Compartment

September 15, 2020
Author(s)
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

Time Series Feature Extraction and Selection Tool for Fire Data

September 15, 2020
Author(s)
Jun Wang, Youwei Jia, Eugene Yujun Fu, Jiajia Li, Andy Tam
This paper aims to facilitate the use of machine learning to carry out supervised classification/regression tasks for time series data in fire research. Specifically, a feature engineering tool, FAST (Feature extrAction and Selection for Time-series), is

Prevention of Cooktop Ignition Using Detection and Multi-Step Machine Learning Algorithms

April 27, 2020
Author(s)
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 unattended cooking fires. 16 sets of time- dependent sensor signals were obtained from 60 normal/ignition cooking

Voices of First RespondersNationwide Public Safety Communication Survey Methodology: Development, Dissemination, and Demographics, Phase 2, Volume 1

March 20, 2020
Author(s)
Kristen Greene, Shanee T. Dawkins, Sandra S. Prettyman, Pamela J. Konkol, Mary F. Theofanos, Kevin C. Mangold, Susanne M. Furman, Michelle P. Steves
With the newly created Nationwide Public Safety Broadband Network (NPSBN), the public safety community is in the process of supplementing the use of land mobile radios (LMR) to a technology ecosystem that will include a variety of new communication tools

The Economics of Firefighter Injuries in the United States

December 11, 2019
Author(s)
David T. Butry, David H. Webb, Stanley W. Gilbert, Jennifer Taylor
This report identifies, summarizes, and evaluates the available data and the literature describing the economic costs associated with non-fatal firefighter injuries, illnesses, health exposures, and occupational disease (‘health outcomes’) resulting from