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Search Publications by: Paul A. Reneke (Fed)

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Displaying 1 - 25 of 110

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

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

Response Time Impact of Smoke Alarms

September 9, 2021
Author(s)
Stanley W. Gilbert, Thomas Cleary, Paul A. Reneke, Richard Peacock, David Butry
It can be assumed that smoke alarms reduce reported fires and casualties by reducing on average the amount of time it takes to detect a fire. This paper sets out to determine by how much that detection time is reduced. It does so by comparing the effect of

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

Towards a Process to Quantify the Hazard of Fire Protection Design Alternatives

May 1, 2019
Author(s)
Paul A. Reneke, Morgan Bruns, Stanley W. Gilbert, Chandler P. MacLaren, Richard D. Peacock, Thomas G. Cleary, David T. Butry
There are a variety of fire protection technologies that have the potential to improve life safety in residences including barrier fabrics for upholstered furniture, new nano-scaled flame retardants, or better fire detector technology. However, there is no

BFRL Publications, 2001. Volume 1 and Volume 2

February 19, 2017
Author(s)
Star R. Burgess, Kathleen C. Whisner, Glenn P. Forney, Paul A. Reneke
Welcome to BFRL Publications, 2001. These two CD-ROMs contain publications produced by BFRL staff, publications resulting from contracts or grants with BFRL and publications contained in proceedings from BFRL sponsored conferences or workshops. The Volume

Verification and Validation of CFAST, A Model of Fire Growth and Smoke Spread

February 19, 2017
Author(s)
Walter W. Jones, Richard D. Peacock, Glenn P. Forney, Paul A. Reneke
CFAST is a zone model capable of predicting the environment in a multi-compartment structure subjected to a fire. It calculates the time evolving distribution of smoke and fire gases and the temperature throughout a building during a user-specified fire

Movement on Stairs During Building Evacuations

June 8, 2016
Author(s)
Richard D. Peacock, Paul A. Reneke, Erica D. Kuligowski, Robert C. Hagwood
The time that it takes an occupant population to reach safety when descending a stair during building evacuations is typically estimated by measureable engineering variables such as stair geometry, speed, stair density, and pre-observation delay. In turn

A Review of Risk Perception in Building Fire Evacuation

October 1, 2014
Author(s)
Max T. Kinateder, Erica D. Kuligowski, Paul A. Reneke, Richard Peacock
Risk perception (RP) is studied in many research disciplines (e.g., safety engineering, psychology, or sociology), and the contexts surrounding the ways in which different concepts of RP are applied vary greatly. Definitions of RP can be broadly divided