A spatial temporal graph neural network model for predicting flashover in arbitrary building floorplans. (October 2022)

• Record Type: Journal Article
• Authors: Tam, Wai Cheong; Fu, Eugene Yujun; Li, Jiajia; Huang, Xinyan; Chen, Jian; Huang, Michael Xuelin
• Is Part Of: Engineering applications of artificial intelligence. Volume 115(2022)
• Publication Date: 2022-10
• Subjects: Machine learning -- Compartment fires -- Synthetic fire data -- Smart firefighting -- Intelligent system; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105258
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.