Real-time forecast of compartment fire and flashover based on deep learning

• Published in: Fire safety journal Vol. 130; p. 103579
• Main Authors: Zhang, Tianhang, Wang, Zilong, Wong, Ho Yin, Tam, Wai Cheong, Huang, Xinyan, Xiao, Fu
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier Ltd 01-06-2022; Elsevier BV
• Subjects: Algorithms; Artificial intelligence; Critical fire event; Deep learning; Fire fighting; Flashover; Fuels; Internet of Things; IoT; Lead time; Machine learning; Mathematical models; Real time; Rescue operations; Scaled compartment; Smart firefighting; Temperature sensors; Training; Ventilation; Smart firefighting; Critical fire event; Artificial intelligence; Scaled compartment; IoT
• ISSN: 0379-7112; 1873-7226
• DOI: 10.1016/j.firesaf.2022.103579

Forecasting building fire development and critical fire events in real-time is of great significance for firefighting and rescue operations. This work proposes an artificial intelligence (AI) system to fast forecast the compartment fire development and flashover in advance based on a temperature sensor network and a deep-learning algorithm. This fire-forecast system is demonstrated in a 1/5 scale compartment with various ventilation conditions and fuel loads. After training 21 reduced-scale compartment tests, the deep learning model can well identify the fire development inside the compartment and predict the temperature 30 s in advance with relative errors of less than 10%. The flashover can be predicted with a 20-s lead time, and the forecast capacity and accuracy can be further improved with additional test data for training. The AI-forecast model performs well for fires with different fuel types and ventilation conditions and has the potential to be applied to fire scenarios with wider conditions. This research demonstrates the real-time building fire forecast based on Internet of Things (IoT) sensors and AI systems that can help future smart firefighting applications.
•A deep-learning AI model is developed to forecast compartment fire in real-time.•1/5-scaled room fire tests with temperature sensors are trained to demonstrate smart system.•AI model can predict temperature and flashover with a 20-s lead time and an error <10%.•The fire-forecast system driven by AI and IoT sensors can support future smart firefighting.