A Generic Flashover Prediction Model for Residential Buildings Using Graph Neural Network

Research output: Unpublished conference presentation (presented paper, abstract, poster)Conference presentation (not published in journal/proceeding/book)Academic researchpeer-review

Abstract

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 graphstructure 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 US fire fighter deaths and injuries.
Original languageEnglish
Publication statusPublished - Dec 2021
EventAOSFST 2021–12th Asia-Oceania Symposium on Fire Science and Technology -
Duration: 7 Dec 20219 Dec 2021

Competition

CompetitionAOSFST 2021–12th Asia-Oceania Symposium on Fire Science and Technology
Period7/12/219/12/21

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