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Predicting Flashover Occurrence using Surrogate Temperature Data

Published

Author(s)

Andy Tam, Eugene Yujun Fu, Richard Peacock, Paul A. Reneke, Jun Wang, Grace Ngai, Hong Va Leong, Thomas Cleary

Abstract

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 a flashover prediction model which can be used to warn fire fighters before flashover occurs. Specifically, we consider the use of a fire simulation program to generate a set of synthetic data and an attention-based bidirectional long short-term memory to learn the complex relationships between temperature signals and flashover conditions. We first validate the fire simulation program with temperature measurements obtained from full-scale fire experiments. Then, we generate a set of synthetic temperature data which account for the realis-tic fire and vent opening conditions in a multi-compartment structure. Results show that our proposed method achieves promising performance for prediction of flashover even when temperature data is completely lost in the room of fire origin. It is believed that the flashover prediction model can facilitate the transformation of fire fighting tactics from traditional experience-based decision marking to data-driven decision marking and reduce fire fighter deaths and injuries.
Proceedings Title
35th Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence
Conference Dates
February 2-9, 2021
Conference Location
Virtual

Keywords

Flashover prediction, synthetic data, sensor failure, recurrent neural network, attention mechanism, smart fire fighting

Citation

Tam, A. , , E. , Peacock, R. , Reneke, P. , Wang, J. , Ngai, G. , , H. and Cleary, T. (2021), Predicting Flashover Occurrence using Surrogate Temperature Data, 35th Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence, Virtual, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931077 (Accessed December 3, 2021)
Created February 9, 2021, Updated April 6, 2021