Predicting Flashover Occurrence using Surrogate Temperature Data

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

Keywords:

Other Social Impact

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.

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Published

2021-05-18

How to Cite

Fu, E. Y., Tam, W. C., Wang, J., Peacock, R., Reneke, P. A., Ngai, G., Leong, H. V., & Cleary, T. (2021). Predicting Flashover Occurrence using Surrogate Temperature Data. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14785-14794. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17736

Issue

Section

AAAI Special Track on AI for Social Impact