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A fire risk pre-warning framework for high-rise buildings based on unascertained method.

Li-Ning Zhang1, Xiang-Ming Wang2, Jing An3

  • 1School of Architecture Engineering, North China Institute of Science and Technology, Beijing, 101601, China.

Environmental Science and Pollution Research International
|October 25, 2024
PubMed
Summary

This study develops an early warning framework for high-rise building fire risk. An unascertained method accurately predicts fires, offering reliable decision support for prevention and control.

Keywords:
Case studyHigh-rise buildingsPre-warning tree for fire prevention riskUnascertained pre-warning modeling

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Area of Science:

  • Fire Safety Engineering
  • Risk Assessment
  • Building Science

Background:

  • Increasing global focus on high-rise building fire prevention and control.
  • Need for scientific indicator systems in fire risk assessment models.
  • Challenges posed by the uncertainty of fire occurrence in complex structures.

Purpose of the Study:

  • To establish an early warning framework for fire risk in high-rise buildings.
  • To develop a scientific indicator system for high-rise building fire risk assessment.
  • To provide a reliable decision support system for fire disaster prevention.

Main Methods:

  • Combined event analysis method with building design fire codes to identify 11 key risk factors.
  • Established a high-rise building fire risk warning tree.
  • Employed the unascertained method for fire risk modeling and early warning.
  • Validated methodology through case studies and empirical data analysis.
  • Compared unascertained method results with K-means clustering.

Main Results:

  • Identified 11 critical risk factors impacting high-rise building fire prevention.
  • Developed a functional fire risk warning tree for indicator systems.
  • The unascertained method demonstrated higher accuracy in predicting building fires compared to the K-means method.
  • Empirical validation confirmed the methodology's effectiveness.

Conclusions:

  • The unascertained method provides a more accurate prediction of building fires.
  • The developed framework offers a reliable decision support system for high-rise building fire prevention and control.
  • This research contributes to enhancing fire safety strategies in tall structures.