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

Tianhang Zhang, Zilong Wang, Ho Yin Wong, Wai Cheong Tam, Xinyan Huang, Fu Xiao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number103579
JournalFire Safety Journal
Volume130
DOIs
Publication statusPublished - Jun 2022

Keywords

  • Artificial intelligence
  • Critical fire event
  • IoT
  • Scaled compartment
  • Smart firefighting

ASJC Scopus subject areas

  • General Chemistry
  • General Materials Science
  • Safety, Risk, Reliability and Quality
  • General Physics and Astronomy

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