Building Artificial-Intelligence Digital Fire (AID-Fire) system: A real-scale demonstration

Tianhang Zhang, Zilong Wang, Yanfu Zeng, Xiqiang Wu, Xinyan Huang, Fu Xiao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)

Abstract

The identification of building fire evolution in real-time is of great significance for firefighting, evacuation, and rescue. This work proposed a novel framework of Artificial-Intelligence Digital Fire (AID-Fire) that can identify complex building fire information in real-time. The smart system consists of four main parts, Internet of Things sensor network (data collection and transfer), cloud server (data storage and management), AI Engine (data processing), and User Interface (fire information display). A large numerical database, containing 533 fire scenarios with varying fire sizes, positions, and number of fire sources, is established to train a Convolutional Long-Short Term Memory (Conv-LSTM) neural network. The proposed fire digital twin is demonstrated and validated in a full-scale fire test room (26 m2). Results show that the AI engine successfully identify the fire information by learning the spatial-temporal features of the temperature data with a relative error of less than 15% and a delay time of less than 1 s. Moreover, detailed fire development and spread can be accurately displayed in the digital-twin interface. This proposed AID-Fire system can provide valuable support for smart firefighting practices, thus paving the way for a fire-resilient smart city.

Original languageEnglish
Article number105363
JournalJournal of Building Engineering
Volume62
DOIs
Publication statusPublished - 15 Dec 2022

Keywords

  • Building fire
  • Cyber-physics
  • Deep learning
  • Digital twin
  • IoT
  • Smart firefighting

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Architecture
  • Building and Construction
  • Safety, Risk, Reliability and Quality
  • Mechanics of Materials

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