A Generic Flashover Prediction Model for Residential Buildings Using Graph Neural Network
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 operations, the temporal dependencies and spatial correlations of the temperature data are captured. Model assessment show that the generic flashover prediction model can distinguish different building structures and provide forecasts in advance to classify the potential occurrence of flashover with an overall accuracy of 93 %. This work constitutes a machine learning-based forecasting model framework accounting for a wide range of building structures. The research outcomes from this study are expected to facilitate data-driven fire fighting, leading to enhanced situational awareness and improved fire fighting safety to help reduce U.S. fire fighter deaths and injuries.
AOSFST 2021 – 12th Asia-Oceania Symposium on Fire Science and Technology
, Fu, E.
, Reneke, P.
, Peacock, R.
and Cleary, T.
A Generic Flashover Prediction Model for Residential Buildings Using Graph Neural Network, AOSFST 2021 – 12th Asia-Oceania Symposium on Fire Science and Technology, Brisbane, AU, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933601
(Accessed December 2, 2023)