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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 > No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Predicting Flashover Occurrence using Surrogate Temperature Data

February 1, 2023

Authors

Eugene Yujun Fu

National Institute of Standards and Technology The Hong Kong Polytechnic University


Wai Cheong Tam

National Institute of Standards and Technology


Jun Wang

The Hong Kong Polytechnic University


Richard Peacock

National Institute of Standards and Technology


Paul A Reneke

National Institute of Standards and Technology


Grace Ngai

The Hong Kong Polytechnic University


Hong Va Leong

The Hong Kong Polytechnic University


Thomas Cleary

National Institute of Standards and Technology


Proceedings:

No. 17: IAAI-21, EAAI-21, AAAI-21 Special Programs and Special Track

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 35

Track:

AAAI Special Track on AI for Social Impact

Downloads:

Download PDF

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.

DOI:

10.1609/aaai.v35i17.17736


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 35



Topics: AAAI

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