Predicting transient building fire based on external smoke images and deep learning

Abstract

A real-time evaluation of fire severity inside a building could facilitate decision-making in firefighting and rescue operations. This work explores the real-time prediction of transient fire scenarios by using external smoke images and deep learning algorithms. A big database of 1845 simulated compartment fire scenarios is formed. Three input parameters (constant fire heat release rate, opening size, and fuel type) are paired with the external smoke images, and then trained by Convolutional Neural Network (CNN) model. Results show that by training either the front-view or side-view smoke images, the artificial intelligence (AI) method can well identify the transient fire heat release rate inside the building, even without knowing the burning fuels, and the error is no more than 20%. This work demonstrates that the deep learning algorithms can be trained with simulated smoke images to determine the hidden fire information in real-time and shows great potential in smart firefighting applications.