Real-Time Flashover Prediction Model for Multi-Compartment Building Structures Using Attention Based Recurrent Neural Networks

Published

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

Abstract

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 for more than 110 000 fire cases with a wide range of fire and vent opening conditions are collected. Temperature limit to heat detectors is applied to mimic the loss of temperature data in real fire scenarios. P-Flashv2 is shown to be able to make predictions with a maximum lead time of 60 s and its performance is benchmarked against eight different model architectures. Results show that P-Flashv2 has an overall accuracy of 87.7 % and 89.5% for flashover predictions with a lead time setting of 30 s and 60 s, respectively. Additional model testing is conducted to assess P-Flashv2 prediction capability in real fire scenarios. Evaluating the model again with full-scale experimental data, P-Flashv2 has an overall prediction accuracy of 82.7 % and 85.6 % for cases with the lead time of setting 30 s and 60 s, respectively. Results from this study show that the proposed machine learning based model, P-Flashv2, can be used to facilitate data-driven fire fighting and reduce fire fighter deaths and injuries.

Citation

Expert Systems With Applications

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.eswa.2023.119899

Local Download

Keywords

Flashover occurrence, machine learning, real-time prediction, realistic fire and opening conditions, benchmark models.

Citation

Tam, W. , Fu, E. , Li, J. , Peacock, R. , Reneke, P. , Cleary, T. , Ngai, G. , Leong, H. and Huang, M. (2023), Real-Time Flashover Prediction Model for Multi-Compartment Building Structures Using Attention Based Recurrent Neural Networks, Expert Systems With Applications, [online], https://doi.org/10.1016/j.eswa.2023.119899, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934017 (Accessed October 31, 2024)