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

The growing global interest in preventing and controlling fires in high-rise buildings reflects the increasing significance of this issue today. This research aims to establish an early warning framework for fire risk in high-rise buildings. Firstly, considering the importance of a scientific indicator system for the application of the model, this study combines the event analysis method with the building design fire code to identify 11 key risk factors that have a far-reaching impact on the prevention of fires in high-rise buildings. Based on identifying the risk factors, a high-rise building fire risk warning tree is also established, which scientifically solves the problem of the indicator system of the warning object. Subsequently, in response to the various complex issues arising from the uncertainty of fire occurrence in high-rise buildings, this study adopts the unascertained method to model the fire risk of high-rise buildings for early warning. In addition, the developed methodology was empirically validated through case studies and analyses of empirical data on fire risks in nine representative high-rise buildings. The results of the unascertained method were also compared with the results of the K-means method, from which it was concluded that the unascertained method can predict building fires more accurately. The research results provide a reliable decision support system for fire disaster prevention and control in high-rise buildings.

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Publication Details

Journal
Environmental Science and Pollution Research
Published
2024-10-25
DOI
https://doi.org/10.1007/s11356-024-35396-y
Citations
6
Primary Topic
Fire dynamics and safety research
Type
article
Field-Weighted Citation Impact
2.19

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article

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

Han Guo-bo, P.F. Gou, Jingdong Guo, Xiangming Wang et al.
6 citations
Environmental Science and Pollution Research
Fire dynamics and safety research
2.19
article

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

Han Guo-bo, P.F. Gou, Jingdong Guo, Xiangming Wang, Lining Zhang, Jing An, Hong Li
article en
6 citations

Abstract

The growing global interest in preventing and controlling fires in high-rise buildings reflects the increasing significance of this issue today. This research aims to establish an early warning framework for fire risk in high-rise buildings. Firstly, considering the importance of a scientific indicator system for the application of the model, this study combines the event analysis method with the building design fire code to identify 11 key risk factors that have a far-reaching impact on the prevention of fires in high-rise buildings. Based on identifying the risk factors, a high-rise building fire risk warning tree is also established, which scientifically solves the problem of the indicator system of the warning object. Subsequently, in response to the various complex issues arising from the uncertainty of fire occurrence in high-rise buildings, this study adopts the unascertained method to model the fire risk of high-rise buildings for early warning. In addition, the developed methodology was empirically validated through case studies and analyses of empirical data on fire risks in nine representative high-rise buildings. The results of the unascertained method were also compared with the results of the K-means method, from which it was concluded that the unascertained method can predict building fires more accurately. The research results provide a reliable decision support system for fire disaster prevention and control in high-rise buildings.

Environmental Science and Pollution ResearchVol. 31(52)
The University of Sydney (AU), North China Institute of Science and Technology (CN), Deakin University (AU), Beijing University of Technology (CN), University of Science and Technology Beijing (CN)
Deakin University
Climate action
Openalex Percentile: Top 13%
Fire dynamics and safety research
2.19
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