Smart Detection of Fire Source in Tunnel Based on the Numerical Database and Artificial Intelligence

Xiqiang Wu, Younggi Park, Ao Li, Xinyan Huang, Fu Xiao, Asif Usmani

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

The fire event in a tunnel creates a rapid spread of heat and smoke flows in a long and confined space, which not only endangers human life but also challenges the fire-evacuation and firefighting strategies. A quick and accurate identification for the location and size of the original fire source is of great scientific and practical value in guiding fire rescue and fighting the tunnel fire. Nevertheless, it is a big challenge to acquire fire-source information in an actual tunnel fire event. In this study, the framework of artificial intelligence (AI) and big data is applied to predict the fire source in a numerical model of the tunnel. A big tunnel fire database of numerical simulations, with varying fire locations, fire sizes, and ventilation conditions, is constructed. Temporally varied temperatures measured by multiple sensor devices are used to train a long-short term memory recurrent neural network. Results demonstrate that the location and size of the tunnel fire and the ventilation wind speed can be predicted by the trained model with an accuracy of 90%. Sensitivity analysis is also carried out to optimize the database configuration and spatial–temporal arrangement of sensors in order to achieve a fast and reliable fire prediction. This work addresses the possibility of AI-based detection and prediction of fire source and hazard, thus, providing scientifically based guidance for smart-firefighting technologies and paving the way for future emergency-response tactics in a smart city.

Original languageEnglish
JournalFire Technology
DOIs
Publication statusAccepted/In press - 1 Jan 2020

Keywords

  • CFD
  • Deep learning
  • LSTM
  • Recurrent neural networks
  • Smart firefighting
  • Tunnel fires

ASJC Scopus subject areas

  • Materials Science(all)
  • Safety, Risk, Reliability and Quality

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