Evidence map›Paper›PMID 42832548›Full record

ArticlePloS one2026

Fire risk assessment for high-rise buildings based on Integrated Disaster Risk Theory.

Yingda Huang, Zonglai Liu, Xiaogeng Wei, Zhi-Jin Wu

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yingda HuangCollege of Jilin Emergency Management, Changchun Institute of Technology, Changchun, China.
Zonglai LiuCollege of Jilin Emergency Management, Changchun Institute of Technology, Changchun, China.ORCID https://orcid.org/0009-0005-2311-5403
Xiaogeng WeiChina City Development Academy, Beijing, China.
Zhi-Jin WuJilin Heyuan Fire Engineering Co., Ltd., Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-rise buildings have become a dominant form of urban development. However, because these buildings face multiple fire safety risks, including difficulties in fire suppression, diverse occupancy types, rapid fire spread, and challenging evacuation, once a fire occurs, it is likely to cause serious casualties and significant property losses. It is therefore essential to carry out fire risk assessments for high-rise buildings in advance and to adopt targeted prevention and control measures. To improve the systematic and scenario-specific assessment of fire risk in high-rise buildings, this study introduces the Integrated Disaster Risk Theory into high-rise building fire risk assessment and reconstructs the assessment indicator system according to the specific formation mechanism of high-rise building fires. Indicator weights were integrated using a game-theoretic approach and then combined with cloud model theory to construct a fire risk assessment model for high-rise buildings, thereby identifying the corresponding fire risk levels of high-rise buildings. The assessment results for a typical high-rise building in Changchun showed that its fire risk level was Grade II, indicating a relatively low fire risk. This result was consistent with those obtained using an existing fire risk index method (FRI), thereby supporting the plausibility and consistency of the proposed model. This study provides a more targeted fire risk assessment framework for high-rise buildings, offering methodological support for fire prevention, evacuation management, and emergency rescue resource allocation.

Indexed as

Disaster PlanningDisastersFiresHumansModels, TheoreticalRisk Assessment

Identifiers

PMID42832548
PMCPMC13637918

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.