In firefighting, the worst flames are the ones you don't see coming. Amid the chaos of a burning building, it is difficult to notice the signs of impending flashover -- a deadly fire phenomenon wherein nearly all combustible items in a room ignite suddenly. Flashover is one of the leading causes of firefighter deaths, but new research suggests that artificial intelligence (AI) could provide first responders with a much-needed heads-up.
Researchers at the National Institute of Standards and Technology (NIST), the Hong Kong Polytechnic University and other institutions have developed a Flashover Prediction Neural Network (FlashNet) model to forecast the lethal events precious seconds before they erupt. In a new study published in Engineering Applications of Artificial Intelligence, FlashNet boasted an accuracy of up to 92.1% across more than a dozen common residential floorplans in the U.S. and came out on top when going head-to-head with other AI-based flashover predicting programs.1
Until now, most machine learning-based prediction tools, including one the authors previously developed, have been trained to operate in a single, familiar environment. In reality, firefighters are not afforded such luxury. As they charge into hostile territory, they may know little to nothing about the floorplan, the location of fire or whether doors are open or closed.
- 1. Flashovers tend to suddenly flare up at approximately 600 degrees Celsius (1,100 degrees Fahrenheit) and can then cause temperatures to shoot up further. To anticipate these events, existing research tools either rely on constant streams of temperature data from burning buildings or use machine learning to fill in the missing data in the likely event that heat detectors succumb to high temperatures.
Wai Cheong Tam, Eugene Yujun Fu, Jiajia Li, Xinyan Huang, Jian Chen, Michael Xuelin Huang. A spatial temporal graph neural network model for predicting flashover in arbitrary building floorplans. Engineering Applications of Artificial Intelligence, 2022; 115: 105258 DOI: 10.1016/j.engappai.2022.105258
Rapid fire progression, such as flashover, has been one of the leading causes for firefighter deaths and injuries in residential building environments. Due to long computational time of and the required prior knowledge about the fire scene, existing models cannot be used to predict the potential occurrence of flashover in practical firefighting applications. In this paper, a scene-agnostic model (FlashNet) is proposed to predict flashover based on limited heat detector temperature information up to 150 °C. FlashNet utilizes spatial temporal graph convolutional neural networks to effectively learn features from the limited temperature information and to tackle building structure variations. The proposed model is benchmarked against five different state-of-the-art flashover prediction models. Results show that FlashNet outperforms the existing flashover prediction models and it can reliably predict flashover 30 s preceding its occurrence with an overall accuracy of about 92.1%. Ablation study is carried out to examine the effectiveness of different key model components and geometric average adjacency matrix. The research outcomes from this study are expected to enhance firefighters’ situational awareness in the fire scene, protecting them from hazardous fire environments and to pave the way for the development of data-driven prediction systems.